Understanding the global rise of artemisinin resistance: Insights from over 100,000 Plasmodium falciparum samples

Artemisinin-based combination therapies (ACTs) are the most important antimalarial treatment currently available. By combining a fast-acting artemisinin derivative with a longer-acting partner drug, ACTs provide a highly effective treatment for uncomplicated malaria. The artemisinin derivative reduces parasite biomass over the first 3 days of treatment, before the partner drug clears the remaining infection (Nosten and White, 2007). Because of their effectiveness, the WHO has recommended ACTs as primary treatment for uncomplicated malaria, and as a follow-up treatment for severe malaria, since 2006 (World Health Organization, 2006). As of 2024, ACTs remain the most widely used antimalarial globally in treating infection by Plasmodium falciparum and have been adopted as a primary malaria treatment strategy by 84 countries (World Health Organization, 2022b). Currently, around 65% of all uncomplicated malaria cases are treated with ACTs, with up to 70% of infections in Africa treated using the ACT artemether-lumefantrine (Dhorda et al., 2024). While new drugs are in development to replace ACTs, the lengthy testing and clinical trial process means ACTs will likely remain the primary treatment for the foreseeable future (Erhunse and Sahal, 2021).

Given their critical role in malaria treatment, preventing resistance to ACTs remains a major priority for the malaria community. Initially, there was optimism that resistance to artemisinin may not occur, or would occur more slowly than resistance to previous drugs, due to the combination of an artemisinin derivative with a partner drug (Yeung et al., 2004; Pongtavornpinyo et al., 2008). However, this optimism faded in 2008, when reports of reduced efficacy of artemisinin-based drugs emerged from the Thai-Cambodian border. These were the first recorded instances of what we now refer to as artemisinin ‘partial resistance’ (ART-R) (Noedl et al., 2008; Dondorp et al., 2009; Ashley et al., 2014; World Health Organization, 2016). ART-R is characterised by delayed parasite clearance time following treatment with an artemisinin derivative, where high parasitaemia persists beyond day 3 of treatment, or if parasite clearance half-life exceeds 5 hours (Rosenthal, 2021). It is termed ‘partial’ resistance because parasite clearance is delayed rather than prevented, leaving more parasites for the partner drug to clear (Behrens et al., 2021). While this does not immediately lead to treatment failure, it increases selective pressure for partner drug resistance, potentially leading to parasites which are resistant to both drugs and a higher likelihood of overall treatment failure.

After emerging on the Thai-Cambodia border in 2008, ART-R then spread across the Greater Mekong Subregion into Myanmar, Vietnam, and Laos throughout the early 2010s (Ashley et al., 2014; Tun et al., 2015). As with resistance to previous antimalarials chloroquine and sulfadoxine–pyrimethamine (Roux et al., 2021; Mita et al., 2009), Southeast Asia had become the initial hotspot for artemisinin resistance. With those drugs, resistant parasites then spread westwards to East Africa, before dispersing widely across the continent and causing a major public health crisis with effects still felt decades later (Trape, 2001; Murray et al., 2012). Now, a major concern is whether ART-R will follow the same trajectory. As Africa carries 94% of the global malaria burden, with over 233 million cases annually, if a decline in artemisinin efficacy caused widespread ACT failure, the consequences could be catastrophic (Dhorda et al., 2024; Duffey et al., 2021; World Health Organization, 2023). Alarmingly, reports of reduced therapeutic efficacy have been increasing in East and Northeast Africa since 2019 (Balikagala et al., 2021; Assefa et al., 2024; Conrad et al., 2023; Uwimana et al., 2020). Although ACTs remain largely effective, several countries have reported efficacies below 90%, including Angola, the DRC, Burkina Faso, Uganda, and Tanzania (Ishengoma et al., 2024; Moriarty et al., 2021; Dimbu et al., 2021; Ebong et al., 2021; Gansané et al., 2021). Monitoring and preventing the spread of ART-R is therefore a major global health priority.

We compiled a global dataset of publicly available kelch13 mutation data for 112,933 samples collected between 1980 and 2023, from 73 countries (see Methods). These data and metadata were compiled from three sources: the MalariaGEN Pf7 release (Abdel Hamid et al., 2023), the Worldwide Antimalarial Resistance Network (WWARN) Artemisinin Resistance Molecular Surveyor, and a literature search. Mutations were annotated based on their artemisinin resistance status using the most recent WHO guidelines: either ‘validated’, ‘candidate’, or ‘no status’ (Supplementary file 1). Parasites with the 3D7 reference kelch13 sequence or the A578S mutation were classified as artemisinin-susceptible (World Health Organization, 2022b). Throughout this work, we refer to all BTB/POZ and propeller mutations (codons 349–726) as ‘propeller mutations’. Samples from each country were assigned to one of thirteen broader populations, allowing us to assess trends at continental scales (see Methods). These populations included: South America, West Africa, Central Africa, North Africa, Northeast Africa, East Africa, Southern Africa, Western Asia, Eastern South Asia, Far-Eastern South Asia, Western Southeast Asia, Eastern Southeast Asia, and Oceania.

Global geographic overview

Across the samples, we observed 492 unique, non-synonymous mutations in the BTB/POZ and propeller domains of kelch13 (Supp. Dataset S2). Most samples had the 3D7 reference sequence (85.5%, n=96,574), with the most common mutation being the WHO validated marker C580Y (6.5%, n=7,307). The other most common mutations were all WHO validated/candidate markers: F446I (1.5%), R561H (0.7%), P441L (0.7%), and R539T (0.4%). Southeast Asia had the highest proportion of samples with a kelch13 propeller mutation (Figure 1A). For example, over half of samples from Eastern Southeast Asia (52%) had a kelch13 propeller mutation, with over 98% of these having a WHO validated/candidate marker. Similarly, in Western Southeast Asia, 35% of samples had a kelch13 propeller domain mutation, with 94% of these having a WHO validated/candidate mutation (Figure 1A). These populations also showed the highest diversity of WHO validated/candidate markers relative to unique kelch13 propeller mutations (Figure 1B).

Figure 1

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kelch13 genotypes and sample metadata for 112,933 Plasmodium falciparum isolates.

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The East and Northeast African populations had an increased frequency of WHO validated/candidate markers (Figure 1A). For example, 10% of Northeast African samples had a kelch13 propeller mutation, ~80% of which were WHO validated/candidate markers. Similarly, in East Africa, 4% of samples had a kelch13 propeller mutation, and ~25% of these were WHO validated/candidate markers for ART-R (Supplementary file 3). The East and Northeast regions of Africa also had a high number of WHO validated/candidate mutations relative to unique kelch13 propeller mutations (Figure 1B, Supplementary file 3). In contrast, the proportion of mutant samples was low in other areas of Africa (Figure 1A). In West and Central Africa, for example, mutant samples made up just 2% of the total samples, and this was even lower in Southern Africa (~1%). Similarly, the proportion of samples with a WHO validated/candidate mutation in West, Central, and Southern Africa was also low, typically below 0.13% (Supplementary file 3). Even though West Africa was the largest study population and had the largest number of unique kelch13 propeller mutations (n=185), only five unique WHO validated/candidate mutations were observed there (Supplementary file 3). Together with lower overall frequencies of WHO mutations, this may imply that there is weaker selective pressure for WHO validated/candidate mutations in West Africa.

Other global populations had low frequencies of kelch13 mutations and low mutational diversity (Figure 1A). For example, only 0.45% of South American samples had a kelch13 propeller mutation, and only three unique mutations were reported in the region (Supplementary file 3). One of these was a WHO validated/candidate mutation (C580Y), which was found in 0.41% of all South American samples (Supplementary file 3). Similarly, in Western Asia (the second smallest population), only 0.64% of samples had mutations, none of which had WHO validated/candidate status (Figure 1A). In Eastern South Asia, 3% of all samples had a kelch13 propeller mutation. In this region, only 13 unique mutations were observed, four of which were WHO validated/candidate mutations, accounting for 0.54% of samples (Figure 1A). In Far-Eastern South Asia, levels of kelch13 mutation diversity were also very low. Only 0.52% of samples had a kelch13 propeller mutation, and no WHO validated/candidate mutations were recorded. In Oceania, ~2% of all samples had a mutation in the kelch13 propeller domain (Figure 1A). While 24 unique kelch13 propeller mutations were observed in Oceania, only one had WHO validated/candidate status (Figure 1A). For a complete list of mutation metrics in all populations, see (Supplementary file 3).

Global temporal overview

The first documented kelch13 propeller domain mutation observed in the dataset was from 1991, with the first WHO validated/candidate mutation identified in Thailand in 1997 (R359T). No other ART-R markers were detected before 2000, likely due to limited sampling of kelch13 propeller mutations pre-2000, with an average of just 138 samples per year (Supplementary file 4). After 2000, sample numbers increased, reaching up to 17,980 in 2014, coinciding with an increase in global ACT use (Figure 1C, Supplementary file 4). The total number of unique kelch13 mutations and validated/candidate mutations also both rose throughout the 2000s (Figure 2—figure supplement 1). By the time ACTs were introduced as first-line treatments in 2006, over half of the currently known WHO validated/candidate mutations had been observed, predominantly in Southeast Asia (Figure 2). By this time, artemisinin derivatives were already widely used in the region, likely creating an evolutionary pressure for these mutations to emerge and spread (see Section ‘Southeast Asia’). As of the most recent observed years, ART-R-associated mutations have reached high frequency throughout Southeast Asia (Figures 3 and 4).

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kelch13 genotypes and sample metadata for 112,933 Plasmodium falciparum isolates.

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Figure 3

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Figure 4

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In contrast, ART-R mutations were uncommon in Africa before 2010, with only three unique WHO mutations observed in just two samples each (Figure 2). Several WHO validated/candidate markers then increased in frequency in East and Northeast Africa after 2018 (Figures 3 and 4). For example, seven unique mutations increased in prevalence in Northeast Africa after 2020 (Figure 3), with the WHO-validated mutations A675V and C469Y increasing in frequency between 2020–2022 (from 4.7% to 9.4% and 6.9% to 9%, respectively). Another mutation, C469F, almost tripled in frequency, although overall frequency remained low, increasing from 0.9% in 2020 to 2.5% by 2022. Three WHO validated/candidate mutations were detected in East Africa in 2023: C469F (2.8%), A675V (5.6%), and R561H (declined from 22.5% in 2021 to 8.9% in 2023). In Central Africa, validated/candidate markers P441L, R561H, and C469Y were each detected in single years, although all occurred in fewer than 0.5% of samples. These data support the growing concern that ART-R has begun to emerge in Africa (see Section ‘East and Northeast Africa’).

In the previous section, we highlighted how markers of ART-R have been found across the globe, but until recently, had been mostly localised in Southeast Asia. Concerningly, several WHO validated/candidate mutations have now emerged in parts of Africa, with potentially widespread consequences for human health. Here, we provide detailed case studies for Southeast Asia and East/Northeast Africa, focusing on how resistance-associated kelch13 markers emerged and spread, summarising their current frequencies, and highlighting key questions on their geographical distributions. We then discuss kelch13 substitutions observed in other regions, including West and Central Africa, South America, and Oceania.

Southeast Asia

How did kelch13 mutants initially emerge and spread in Southeast Asia?

Artemisinin combination therapies have been used extensively across Southeast Asia since the mid-1990s, where they were quickly adopted by national governments seeking alternatives to drugs such as chloroquine, which had developed widespread resistance. Thailand, for example, adopted ACTs in 1995, shortly followed by Cambodia in 2000, Myanmar in 2002 and Vietnam in 2003 (Hassett and Roepe, 2019; Satimai et al., 2012). While ART-R was only discovered in Southeast Asia in 2008, the proportion of samples with a validated/candidate mutation had in fact already been high by 2006 (Figure 5; Kagoro et al., 2022). ART-R associated kelch13 mutations then emerged independently across several locations in Southeast Asia, before a hard selective sweep drove a multidrug resistant co-lineage to dominance across the region (KEL1/PLA1, containing the C580Y kelch13 mutation) (Takala-Harrison et al., 2015; Imwong et al., 2017; Imwong et al., 2020; Amato et al., 2018; Hamilton et al., 2019). Analysis of genomic regions flanking the kelch13 gene suggested this migration occurred several times, with the C580Y mutation initially spreading eastward from Cambodia to Vietnam, and later, from Cambodia to Thailand and Laos around 2013–15 (Takala-Harrison et al., 2015; Imwong et al., 2017). Over the next 10–12 years, the C580Y mutation reached near-fixation frequencies in several of these countries (Figure 5). In contrast, in Myanmar, C580Y emerged and spread independently on a different parasite genetic background (Imwong et al., 2017). This pattern of identical kelch13 mutations arising independently, alongside migration events between countries, would become a repeated occurrence across Southeast Asia, with several of the most common kelch13 mutations spreading via both mechanisms (Takala-Harrison et al., 2015; Imwong et al., 2017; Imwong et al., 2020; Hamilton et al., 2019).

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Genomic epidemiology played a critical role in identifying these patterns of emergence and migration in Southeast Asia. While it was initially thought there had been a single emergence of ART-R in the region, comparison of parasite genetic backgrounds revealed it had in fact emerged independently across several countries (Neafsey et al., 2021; Amato et al., 2018; Verity et al., 2020). This enabled policy makers to focus on slowing local resistance through improved surveillance and access to malaria prevention, rather than focusing solely on preventing parasite migration across borders (World Health Organization, 2015b; Manzoni et al., 2024). Data from these surveillance efforts then became a cornerstone for developing mitigation strategies in the region. One such strategy, the ‘WHO Strategy for Malaria Elimination in the Greater Mekong Subregion’ (2015–2030), leveraged this evidence to achieve significant reductions in overall malaria burden of 77% (World Health Organization, 2015b; Manzoni et al., 2024). This underscores the importance of accurate and consistent molecular surveillance data in formulating effective public health policies and interventions.

What is the current distribution and frequency of kelch13 mutations across Southeast Asia?

The proportion of samples with a WHO validated or candidate kelch13 substitution remained high as of 2019. Based on the latest data, between 37.9–75.8% of samples had a validated/candidate mutation in most Southeast Asian countries (Figure 5). For example, the proportion of samples with a WHO validated/candidate mutation in Vietnam (2019) and Thailand (2017) was 75.8% and 67.5%, respectively (Supplementary file 5). Similarly, in Cambodia, these frequencies fluctuated between 80% and –90% from 2016 to 2018. In 2019, 52.1% of Cambodian samples had a WHO validated/candidate mutation; however, data for this year were limited to three sites from a single study (Peto et al., 2022). Laos showed a slightly lower frequency of 37.9% (2018), although it exhibited significant year-to-year variability, with a peak of 89% in 2014 (based on 111 samples). In contrast, Myanmar showed both a lower frequency and slower increase in the proportion of samples with a kelch13 mutation between 2004 and 2019 (Supplementary file 5), although frequencies were still high as of the most recent year (51.5%). Between 1993 and 2019, there were 117 unique kelch13 mutations present across Southeast Asia, although only 22 of these were observed in more than 25 samples (Table 1). While the C580Y mutation, a WHO-validated marker, was initially only one of several markers to emerge, it later rose to high frequency in many countries, such as Thailand, Laos, Cambodia, and Vietnam.

Table 1

Marker	WHO status	Total	Proportion (%)
3D7 reference sequence	-	18,958	58.08
C580Y	validated	7,271	22.27
F446I	validated	1,644	5.04
P441L	candidate	672	2.06
R561H	validated	666	2.04
R539T	validated	478	1.46
Y493H	validated	437	1.34
I543T	validated	391	1.20
G449A	candidate	355	1.09
P574L	validated	292	0.89
P553L	validated	226	0.69
M476I	validated	173	0.53
N458Y	validated	140	0.43
G538V	candidate	126	0.39
A675V	validated	95	0.29
G533S	no status	86	0.26
N537I	candidate	49	0.15
A676D	no status	44	0.13
V568G	candidate	38	0.12
M562I	no status	35	0.11
T474I	no status	31	0.09
C469F	candidate	30	0.09
E461G	no status	27	0.08

As of 2019, C580Y remains dominant, with the majority of samples with any kelch13 mutation showing this marker. For instance, 83% of all samples from Cambodia with a kelch13 mutation had C580Y. A similar trend was observed in Thailand (54%) and Vietnam (65%). While other kelch13 substitutions were circulating in these countries, such as R539T and R561H, these were at much lower frequencies. Notably, Thailand showed a larger diversity of other markers than Cambodia, Vietnam, or Laos. This is most likely due to the malaria-free corridor which separates parasite populations in east and west Thailand. Eastern Thailand tends to have higher proportions of C580Y, while western Thailand more closely reflects the diversity in bordering Myanmar (Kobasa et al., 2018). In Myanmar, several unique kelch13 mutations were circulating at moderate frequencies in 2019, with no single substitution reaching fixation. C580Y is not the dominant mutation in Myanmar, where mutations such as F446I, R561H, and P441L have all continued circulating (Bonnington et al., 2017). This pattern of several circulating markers has remained consistent between 2004 and 2019, whereas countries such as Thailand and Laos saw C580Y overtake others during the same time period. In total, of the 22 most common kelch13 mutations, 8 of these were unique to Myanmar and China within Southeast Asia, including: E461G, A675V, M562I, F446I, A676D, G538V, R561H, P441L.

The high frequency of kelch13 mutations in Southeast Asia has translated into increased negative clinical outcomes, evidenced by delayed clearance rates above 10% in TES (Supplementary files 6). We obtained the results of studies which monitor the therapeutic efficacy of common ACTs from the WHO Malaria Threats Map (Manzoni et al., 2024, Figure 5—figure supplement 1). Out of 94 studies across Southeast Asia, 52 (55%) reported delayed clearance rates (>10%) for dihydroartemisinin-piperaquine (DHA-PPQ) treatment, with 18 (19%) reporting treatment failure rates over 10%. Artesunate-mefloquine (AS-MQ) paints a similar picture: of 38 studies, 21 (55%) reported delayed clearance rates, with 2 (5%) reporting treatment failure rates over 10%. In contrast, artemether-lumefantrine (AL) has shown slightly better outcomes, with only 15 of 131 studies (11%) reporting delayed clearance rates of >10%, and four (3%) reporting treatment failure rates >10%. This lower delayed clearance rate for AL may be attributed to the fact that AL has only been a first-line treatment in Laos and Myanmar, where ART-R is lower (World Health Organization, 2023; World Health Organization, 2015b; World Health Organization, 2021; Jacob et al., 2021). The high frequency of delayed clearance and treatment failure indicates the growing challenge in malaria treatment using ACTs in Southeast Asia.

These findings underscore the need for ongoing genomic surveillance to establish the composition and frequencies of circulating parasite populations in Southeast Asia. However, data from the region are limited by the lack of systematic, longitudinal sampling, without which it is difficult to properly estimate the frequencies of resistance (Mayor et al., 2023). For example, in our data, no information was available for Southeast Asian countries after 2019. Additionally, many countries showed considerable between-year variation in the proportion of samples with kelch13 mutations, which may be due to a disproportionately large number of samples from a single study or region and inconsistent sampling. One major challenge for surveillance is that it becomes more challenging to collect samples as overall numbers are driven down, as is the case in Southeast Asia. While systematic genomic surveillance is still key to interpreting the frequencies of kelch13 mutations, going forwards, additional strategies may also be required to account for lower case numbers. These could include enhanced collaborations and data sharing to pool data, and compensatory statistical techniques like time-aggregated sampling.

Did fitness costs affect the spread of kelch13 mutations across Southeast Asia?

Experimental studies have assessed the fitness costs of kelch13 mutations using various methods, including growth and competition assays, as well as measurements of metabolic and transcriptional responses (see Box 1 for an overview). Although validated and candidate mutations have so far been described in this review as conferring a unified ‘resistant’ phenotype, in reality, individual mutations can differ in their effects on both parasite fitness and clearance rate, and these effects may vary further depending on the context in which the parasite is treated with artemisinin (Behrens et al., 2023; WWARN K13 Genotype-Phenotype Study Group, 2019).

Box 1

Fitness costs associated with ART-R.

Fitness costs could significantly impact the spread of ART-R. In the presence of the drug, resistant parasites have a selective advantage, making them more likely to transmit between individuals and spread within a population (White, 2004). However, if there are fitness costs to being resistant, then in the absence of the drug, resistant parasites would be outcompeted by susceptible genotypes, making them more likely to remain at low frequency (Rosenthal, 2013).

Numerous in vitro studies have demonstrated fitness costs associated with kelch13 mutations, such as decreased parasite blood stage growth rate (Straimer et al., 2017) and competitive growth disadvantage (Stokes et al., 2021; Nair et al., 2018; Mathieu et al., 2020). Delayed parasite clearance under artemisinin exposure is linked to decreased haemoglobin endocytosis, limiting the parasite’s ability to utilise important amino acids (Bunditvorapoom et al., 2018). kelch13 mutant parasites also have lower heme levels during the trophozoite stage, which may cause growth defects (Heller and Roepe, 2018b; Heller et al., 2018a). kelch13 mutations may also affect stability of the protein, leading to transcriptional stress responses and cell development issues (Mok et al., 2015; Behrens et al., 2024; Yang et al., 2019). Moreover, different kelch13 mutations can confer differing levels of ART-R and fitness costs. For example, C580Y and R561H show similar increased survival under drug treatment in vitro, but differing fitness costs (Nair et al., 2018). The F446I mutation, common in Myanmar, provides a small survival advantage during drug treatment, but lower competitive growth in vitro than C580Y (Stokes et al., 2021). While other regions of the genome have also been associated with decreased clearance rate for artemisinin (Cerqueira et al., 2017; Mukherjee et al., 2017), these mechanisms are complex and require further experimental validation of potential fitness costs (for a review, see Pandit et al., 2023).

The presence of these costs is unsurprising given the highly conserved nature of the kelch13 propeller domains, yet may have important consequences for selection for ART-R (Ariey et al., 2014; Miotto et al., 2015). Fitness costs may be one reason why kelch13 mutations are typically observed in isolation, rather than multiple mutations within a single parasite. This effect was seen in our aggregated dataset, where only one double kelch13 propeller mutation (M579T/N657H) occurred in a single study (Mishra et al., 2017). However, as neither M579T nor N657H was found individually or together in other studies, this double mutation may have arisen from laboratory cross-contamination or a mixed-strain infection.

Interestingly, differences in the clearance rate and fitness effects of different kelch13 mutations are correlated with their geographic distribution across Southeast Asia (Straimer et al., 2015; Stokes et al., 2021). For example, although the R539T mutation has a larger effect in ring-stage survival assays (RSA) than C580Y, it also generates a larger fitness cost in vitro (Ashley et al., 2014; Straimer et al., 2015; Stokes et al., 2021). This difference corresponds closely with their frequency in Southeast Asia, with C580Y generally being the more prevalent substitution across much of Eastern Thailand, Laos, and Cambodia (Figure 5). Another example is the F446I mutation, which confers a lower degree of resistance than C580Y, but also a lower fitness cost in competition assays (Ashley et al., 2014; Stokes et al., 2021). This again correlates with their relative geographical distributions, with C580Y more common across Eastern Southeast Asia, while F446I is more common in parts of Myanmar and Western Thailand (Figure 5). Interestingly, there is known to be increased transmission, lower drug pressure, and higher competition between parasites in Myanmar relative to other areas of Southeast Asia, suggesting the lower fitness cost of the F446I mutation may provide a selective advantage over C580Y in these areas (Imwong et al., 2017; Imwong et al., 2020). This may have resulted in several mutations with lower fitness costs being more strongly selected for than mutations which cause greater ART-R, potentially explaining the relatively stable frequencies of several mutations observed there between 2004 and 2019. In a further example, R561H shows similar survival rates to C580Y under treatment, but a decreased fitness cost in vitro (Nair et al., 2018). This could mean R561H has the potential to spread more widely, which is concerning as it has increased in frequency in Myanmar since 2016 (Hamilton et al., 2019; Imwong et al., 2017).

Notably, individual kelch13 substitutions have been shown to not only have different phenotypic and fitness effects, but these effects can also differ depending on the genetic background of the parasite in question (Stokes et al., 2021; Siddiqui et al., 2021; Stokes et al., 2022). For example, gene-editing studies have demonstrated that after introducing kelch13 mutations into parasites of Southeast Asian origin, they exert a lower fitness cost and a greater effect on RSA survival than parasites from Africa (Stokes et al., 2021; Stokes et al., 2022). There is also evidence of ‘genetic backgrounds’ which are specifically associated with kelch13 mutations, and tend to be more common in parasites from areas in Southeast Asia where ART-R has emerged and spread extensively (Cerqueira et al., 2017; Miotto et al., 2015; Uwimana et al., 2021). For example, genomic changes have been identified which may compensate for the fitness costs of the kelch13 mutations, such as altering nutrient permeable channels in the parasite vacuole membrane and allowing uptake of important amino acids. These backgrounds are more common in Southeast Asia, which could be one possible reason why ART-R emerged and spread in this region so quickly, alongside early and widespread ACT use (Mesén-Ramírez et al., 2021). Although speculative, these effects could mean resistance mutations are more likely to appear (and therefore spread) on certain genetic backgrounds, or in combination with important genetic changes (Cerqueira et al., 2017; Stokes et al., 2021; Miotto et al., 2015). While other regions of the genome have also been associated with decreased clearance rate for artemisinin, information on how these mechanisms may interact with fitness costs is more limited (for a review, see Miotto et al., 2024).

As novel kelch13 mutations emerge and spread, understanding their fitness costs across different geographical regions and in relation to parasite genetic backgrounds may be crucial. This understanding will require assessing fitness costs through in vitro assays and integrating these findings with genomic surveillance to reveal broader patterns. Genomic surveillance may aid in generating models which can predict the spread of resistance given the frequency of certain genetic backgrounds. These patterns also highlight the advantages of whole genome sequencing over targeted amplicon sequencing, as this additional information can be used to inform models using the genetic background of resistant parasites.

East and Northeast Africa

How did kelch13 mutants initially emerge and spread in East and Northeast Africa?

ACTs have been used extensively across Africa, particularly after 2006 (Newton et al., 2006Supplementary files 6 and 7). By 2008, almost all African countries had implemented ACTs as the primary treatment for malaria, meaning the emergence and spread of ART-R in Southeast Asia around 2008 occurred in tandem with a growing reliance on ACTs in Africa (World Health Organization, 2009). This led to fears that similar mutations could not only arise in Africa, but would result in a wave of resistant parasites. In the last 5 years, those fears are beginning to be realised, with reports of WHO validated/candidate kelch13 mutations in several countries across East and Northeast Africa. This started with the identification of the R561H mutation in Rwanda in 2019. This mutation was shown to have been circulating in the region since 2013–2015, before increasing to ~13% in 2019, with demonstrated effects on parasite clearance rate (Uwimana et al., 2020; Uwimana et al., 2021). This was then mirrored by the dramatic increase in both A675V and C469Y mutations in Uganda between 2 015 and 2019, which were also associated with delayed parasite clearance (Balikagala et al., 2021). Given the extent of ACT use in Africa over the past two decades, it is unclear why ART-R has only emerged now, around 10–15 years behind Southeast Asia, although it is most likely due to the slower initial rollout of ACTs in Africa, combined with differences in case-by-case drug exposure (Hassett and Roepe, 2019). Other factors, such as higher immunity, intra-host competition, or differences in effective population sizes in Africa may have also slowed ART-R (see Section ‘Scenario 3: Lower frequency and slower spread of ART-R in Africa than Southeast Asia’).

After detecting ART-R associated kelch13 mutations in Africa, an initial concern was that these mutations had been introduced via gene flow from Southeast Asian parasites, as was the case with resistance to chloroquine and sulfadoxine–pyrimethamine (Wellems and Plowe, 2001; Gregson and Plowe, 2005). However, evidence from genomic surveillance suggests that the kelch13 mutations observed so far in Africa have emerged independently. For example, ART-R parasites from Uganda are genetically more similar to African parasites than those from Southeast Asia, suggesting an independent African emergence (Ikeda et al., 2018). Similarly, analysis of kelch13 flanking microsatellites and genome-wide single-nucleotide polymorphisms suggests Ugandan parasites with C469F, C469Y, and A675V form a clade distinct from Southeast Asian parasites, implying a single evolutionary origin and clonal expansion within Africa (Conrad et al., 2023). A further example is A675V, C469Y, and R561H mutants from Uganda, Rwanda, and Tanzania, which have different kelch13 flanking haplotypes to parasites with these mutations from Southeast Asia, again pointing to a single evolutionary origin which arose independently in Africa (Balikagala et al., 2021; Uwimana et al., 2020; Ishengoma et al., 2024). Together, these analyses show clear evidence of independent emergence of kelch13 markers in Africa, suggesting local conditions are prompting the emergence and selection of ART-R. Nevertheless, ongoing genomic surveillance will be needed to identify migratory pathways between continents should they occur.

What is the current distribution and frequency of kelch13 mutations in East and Northeast Africa?

As of 2024, WHO validated/candidate mutations have been identified in several countries across East and Northeast Africa, including Uganda, Rwanda, Sudan, Eritrea, Tanzania, Kenya, and Ethiopia (Fola et al., 2023; Figure 6). Concerningly, the frequency of these mutations has increased dramatically since 2018, particularly in Uganda, Rwanda, Tanzania, and Eritrea. In 2022, 26% of samples from Uganda had a WHO validated/candidate mutation, an increase from ~1% in 2013. Similarly, 17% and 15% of samples from Rwanda and Eritrea had a WHO mutation in 2023 and 2019, respectively. Tanzania also had a high proportion of parasites with WHO mutations in 2021 (23%), increasing from ~0.1% to 0.2% between 2017 and 2019. However, Tanzanian samples from 2021 were all from close to the borders with Uganda and Rwanda, which have high levels of ART-R, suggesting a possible geographic sampling bias. Sudan showed a small proportion of parasites with a WHO mutation, reaching 2.2% in 2020. Similarly, Kenya, DRC, Zambia, and Mozambique each showed a small proportion of samples with a WHO mutation, although the total frequencies of samples with any propeller domain mutation in these countries remained ~4% or less. Concerningly, these trends are strikingly similar to those observed in Southeast Asia around 2008, where the initial emergence of validated/candidate mutations was followed by exponential increases in the frequency of resistant parasites over a decade.

Figure 6

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Alongside an increase in WHO validated/candidate mutations, many East and Northeast African countries also had an increase in frequency of propeller domain kelch13 mutations which are not assigned WHO validated/candidate status. These countries included Kenya, Rwanda, Tanzania, Eritrea, South Sudan, Sudan, and Uganda. The phenotypic effects of these mutations are unclear, but in some cases, in vitro evidence suggests they could have phenotypic effects on artemisinin clearance rates (WWARN K13 Genotype-Phenotype Study Group, 2019; Mukherjee et al., 2017). Tracking the changes in frequency of these mutations is important, as they may in fact provide some degree of ART-R.

As of 2024, we have not seen any single WHO validated/candidate mutation rise to prominence in East or Northeast Africa, although this may occur in coming years - as with C580Y in Southeast Asia. In the past 5 years, several WHO validated/candidate markers have circulated simultaneously, with individual mutations mostly localised to specific countries within the region, similar to patterns seen in the early period of ART-R in Southeast Asia (Figure 6 and Table 2). For example, the most common WHO validated/candidate mutation, A675V, has risen to high frequencies in Uganda and Rwanda (9.4% in 2022 and 5.6% in 2023, respectively), but has not been observed in neighbouring countries. Similarly, Rwanda, and Tanzania have a high frequency of parasites with the R561H mutation (8.9% in 2023 and 22.5% in 2021), but this mutation is uncommon elsewhere. A third example is R622I, which was the most common mutation in Eritrea (15.2% in 2019) and Sudan (2% in 2020), but rare outside Northeast Africa. Several of these mutations have increased in frequency over the past 5 years, with A675V, C459Y and P441L all increasing in frequency in Uganda since 2018. A similar situation occurred in Rwanda, where three validated/candidate mutations increased in frequency between 2019 and 2023; A675V from 1.5% to 5.6%, C469F from 1.5% to 2.8%, and R561H from 4.5% to 8.9%. The emergence and spread of different kelch13 mutations in separate regions of East and Northeast Africa suggests they are emerging independently in response to local conditions, rather than migrating between areas (Assefa et al., 2024; Abera et al., 2021).

Table 2

Marker	WHO status	Total	Proportion (%)
3D7 reference sequence	-	22,219	92.95
A675V	validated	306	1.28
C469Y	validated	301	1.26
R622I	validated	184	0.77
R561H	validated	158	0.66
A578S	not associated	149	0.62
C469F	candidate	97	0.41
P441L	candidate	65	0.27
V555A	no status	29	0.12

Interestingly, the WHO validated/candidate kelch13 mutations which have arisen in East and Northeast Africa are different to those found in Southeast Asia (Figure 6C). For example, the most common kelch13 mutations in Southeast Asia are C580Y and F446I, but these are uncommon in Africa, with C580Y only observed in seven samples across the entire continent. Other common substitutions in Southeast Asia were also rare in Africa, including R539T, P574L, F446I, and G449A, all with fewer than five samples. Similarly, of the most common WHO validated/candidate markers in Africa (A675V, C469Y, R622I, R561H, and C469F), only R561H was found regularly in Southeast Asia. This could be due to differences in ACT use between continents. For example, artemether-lumefantrine is the most common treatment for malaria in Africa, but in Southeast Asia, dihydroartemisinin-piperaquine and artesunate-mefloquine are used more often (Tun et al., 2018; World Health Organization, 2022a; Supplementary file 7). Theoretically, these differences could promote different kelch13 mutations, because their partner drugs are associated with selection for different genetic backgrounds with differing resistance mutations. For example, R539T has been associated with mefloquine-resistant parasites, whereas C580Y has been associated with piperaquine-resistant parasites, because changes in mdr1 and plasmepsin2/3, respectively, alter the fitness costs of kelch13 mutations in the presence of these different drugs (Parobek et al., 2017). It is also possible that differences in the phenotypic effects of the substitutions, alongside variation in transmission rates, fitness costs or genetic backgrounds in Africa, have selected for different kelch13 mutations relative to Southeast Asia, although this remains speculative.

Why is ART-R centred in Eastern and Northeastern Africa?

It is unclear why WHO validated/candidate mutations have arisen in Northeast and East Africa first, or why we have not seen a wider spread of these mutations across Africa, despite extensive ACT use. One possibility is higher drug pressure in the region. For example, household survey data from 2003 to 2015 suggests ACT use among symptomatic children was significantly higher in Eastern Africa compared to Central or West Africa, particularly around 2015 (Bennett et al., 2017). During this time in Uganda, 70.2% of symptomatic children received an ACT, compared to an average of 19.7% across the continent (Bennett et al., 2017). Inappropriate use of artemisinin monotherapies has also been observed in some areas of Uganda, with inadequate dosing of artemether-lumefantrine observed in ~12% of children and ~16% of adults in some districts (Wang et al., 2018). However, anecdotal reports also suggest this issue occurs more widely across Africa. Another possibility is that unstable transmission rates of parasites in East and Northeast Africa have promoted the development of ART-R, because when there is a switch from low to high malaria incidence in an area, host immunity is temporarily weakened and drugs need to be used more frequently (Rosenthal et al., 2024). This is relevant in East and Northeast Africa, as there are many highland regions which are known to be centres of unstable transmission (Rodó et al., 2021) and in particular may have been a factor in northern Uganda, where the discontinuation of an effective indoor residual spraying program could have contributed to a sudden resurgence in malaria cases and an increase in drug pressure (Conrad et al., 2023). Indeed, Uganda accounted for 5% of all global malaria cases in 2022, with higher transmission rates relative to many other African countries (only Nigeria and the DRC had more cases in that year; World Health Organization, 2023).

Could fitness costs, genetic backgrounds, or ecological factors slow the emergence of ART-R in Africa?

Although fitness costs of kelch13 mutations are interesting conceptually, what potential do they have to slow the emergence of ART-R in Africa? Despite WHO validated/candidate kelch13 mutations having arisen several times in East and Northeast Africa, so far there does not seem to be strong selective pressure for resistant parasites across the continent (Section ‘Rest of the World’; Rosenthal, 2021; Balikagala et al., 2021; Uwimana et al., 2020; Rosenthal et al., 2024). This suggests these mutants are being outcompeted in many areas, possibly due to a combination of higher fitness costs and different genetic backgrounds, alongside differences in competition between parasites (Cohen et al., 2012; Wasakul et al., 2023). For example, elevated transmission rates, combined with lower levels of drug pressure, may increase the disadvantage generated by fitness costs (Abebaw et al., 2022; Agaba et al., 2022; Bylicka-Szczepanowska and Korzeniewski, 2022). This is reflected in African countries where ART-R is relatively more common, such as Eritrea. In some cases, these are areas which have made substantial reductions in total malaria burden and reduced transmission, both of which may mean the fitness costs of resistance mutations are less important, giving resistant genotypes a larger advantage (Fola et al., 2023). Although local-context specific, this could slow the emergence and spread of ART-R in some areas of Africa.

However, it is important to note that the role of fitness costs and genetic backgrounds in determining population level parasite dynamics remains mostly speculative. To date, many of these effects have only been established in vitro (see Box 1 for details), and in in vivo settings, these costs likely interact with many other important factors which affect the relative competition between parasites, including drug use, vector dynamics, and antigenicity (Cohen et al., 2012; Myers-Hansen et al., 2020; He et al., 2024). Moreover, even if these factors do play a role, it is clearly still possible for ART-R to emerge independently on divergent genetic backgrounds and can be associated with varying degrees of selective pressure depending on local conditions, as seen in Southeast Asia (Takala-Harrison et al., 2015). Lastly, while the focus of this review has been on kelch13 markers, it is not implausible that resistance in Africa could emerge through an alternative mechanism, and the fitness dynamics determining its spread may differ substantially (Demas et al., 2018; Ndiaye et al., 2021; Henriques et al., 2014).

In addition to parasite-specific factors, ecological and epidemiological differences between Africa and Southeast Asia could play a role in slowing the emergence of ART-R. For example, higher levels of population immunity among African populations can reduce parasite biomass during infection, lowering the likelihood of needing treatment and therefore decreasing drug pressure (Doolan et al., 2009). High rates of transmission and mixed-genotype infections can further increase within-host competition, reducing the likelihood that resistant parasites reach fixation. Moreover, the high genetic diversity generated by intensive transmission may have the effect of breaking up resistant haplotypes, hindering their spread (White et al., 2009). While the relative impact of these factors remains difficult to quantify, their combined effect may partially explain the slower rise of ART-R in Africa to date.

Future studies should work toward combining genomic surveillance of parasites with in vitro and population level estimates of effects on both transmission and fitness, as this may allow for improved modelling of epidemiological dynamics. This should be combined with whole-genome sequencing, as this would allow improved understanding of the role of these factors on ART-R dynamics. If their relative importance could be quantified, it could be crucial for genomic surveillance, as it could affect the relative priority given to tracking cases of specific mutations/genetic backgrounds and their spread between areas (Mayor et al., 2023).

Rest of the world

We identified many kelch13 mutations circulating across the rest of Africa, in particular West and Central Africa (Figure 7). For example, WHO validated/candidate mutations were detected at very low frequencies in Ghana, Mali, Nigeria, Equatorial Guinea, and the DRC (Figure 7). However, most mutations in West and Central Africa did not have WHO-validated/candidate status and therefore have unknown phenotypic effects. For instance, non-WHO validated/candidate kelch13 mutations were detected in Benin and Burkina Faso in West Africa, and in the Central African Republic, Chad, the DRC, and Equatorial Guinea in Central Africa (Figure 7). While the phenotypic effects of these mutations are unclear, some may affect phenotypic resistance. For example, we observed a mutation, R539I, at the same kelch13 position as the WHO-validated R539T in Togo, Ghana, Mali, and the DRC (Figure 7). This mutation should be surveyed closely and assessed in phenotypic assays of ART-R due to its position at a known resistance locus in kelch13.

Figure 7

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WHO validated/candidate mutations were uncommon in other populations. In South America, one WHO mutation, C580Y, was detected in 19 samples from Guyana (Figure 7). Aside from this, only two other mutations were detected in South America, each in one sample (A617S in Peru in 2016 and A504T in Colombia in 2018; Figure 7). In Oceania, kelch13 mutations were present in Indonesia, Papua New Guinea, the Solomon Islands, and Vanuatu. Evidence of ART-R was found in Papua New Guinea, where C580Y was present in 10 samples. The T474A was also circulating in Oceania, predominantly in Indonesia. From 1998 to 2020, 25 non-WHO validated or candidate mutations were detected in Oceania. A675V, F446I, R539T, and R561H were also present in India (Figure 5). These markers are also seen across Southeast Asia (Figures 5 and 6), highlighting the potential for ART-R in India. In contrast, in Far-East South Asia, non-validated/candidate mutations were detected in only five samples (0.002% of population). Similarly, Afghanistan, Iran, and Pakistan all showed very low proportions of mutant samples (<0.2% of samples), none of which had WHO validated/candidate status.

Given the emergence of ART-R in Africa, one obvious question is: what are the most likely scenarios for its spread? With the lessons learned from ART-R in Southeast Asia, it is useful to consider potential future scenarios on how ART-R might emerge and spread in Africa within the near future. Here, we articulate three possible scenarios regarding the frequency of kelch13 mutations in Africa: a spread of similar magnitude to that of Southeast Asia, and two more optimistic scenarios. We then discuss broader trends we might observe going forwards, including the human cost of ART-R in Africa, the effect of mitigation efforts, regional variation in ART-R and its dynamics over the next few years. In doing so, we hope this will inform long-term strategic thinking on mitigation strategies, and in properly allocating resources for future surveillance. It is important to note that these scenarios are largely speculative and need to be continually updated with new data as the situation progresses.

Scenario 1: Continued or increased spread of ART-R across Africa, similar to that seen in Southeast Asia

The emergence and spread of ART-R throughout Southeast Asia provides an example of how the situation might develop in Africa. In Southeast Asia, ART-R emerged in several independent locations, before a single drug-resistant lineage spread across the region (Hamilton et al., 2019; Imwong et al., 2017). The C580Y mutation then reached near-fixation (~86%) in parts of Cambodia within a period of around 12–16 years (Figure 5; Noedl et al., 2008; Dondorp et al., 2009; Kagoro et al., 2022). This offers one plausible scenario as to how ART-R might progress in Africa, although it does not preclude the possibility that it could spread even faster than in Southeast Asia. It should also be noted that a worse possible scenario would be the spread of ‘full’ (as opposed to ‘partial’) artemisinin resistance, but since this has not been reported in the field, a more likely scenario is the continued spread of ART-R.

As of 2024, this ‘partial’ resistance has now emerged independently in at least Uganda, Tanzania, Rwanda, Sudan, and Eritrea (Conrad et al., 2023; Uwimana et al., 2020; World Health Organization, 2022a). Importantly, there is evidence of an increase in the proportion of samples with mutant kelch13 markers in several of these countries since 2014, with similar increases in frequency to those observed in Southeast Asia around 10–15 years ago (Figure 8). If these areas were to follow a similar trajectory to Cambodia, we could see frequencies of over 54% delayed clearance under ACT treatment by around 2030 (Slater et al., 2016). However, we stress that the trends presented here are illustrative rather than predictive and do not represent model-based projections (Figure 8). Additionally, the most recent data for Southeast Asia were from 2019, so these increases may not reflect more recent trajectories of ART-R in the region.

Figure 8 with 1 supplement see all

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Nevertheless, this scenario is further supported by estimates of selection coefficients in Uganda, which suggest current selective pressures are comparable to those observed in Southeast Asia between 2003 and 2018. These estimates imply the frequency of mutant kelch13 parasites could reach as high as 95% between 2028 and 2033 (Meier-Scherling et al., 2024). Modelling also suggests that under the continued use of ACT therapies, kelch13 mutations could become established in some areas (up to 57–88% frequency depending on model assumptions), although this will depend on their initial frequency and could be reduced using TACT strategies (see Box 2; Nguyen et al., 2023b).

Box 2

What treatment strategies could slow the spread of treatment failure?

ART-R is a major concern because of its broader role in ACT treatment failure. In essence, ART-R increases selective pressure for partner drug resistance, which in turn increases the likelihood of ACT treatment being unsuccessful. Several strategies have been proposed to slow the spread of treatment failure, including ACT cycling, multiple first-line therapies (MFTs), and triple artemisinin combination therapies (TACTs). Each of these methodshas been used at various times and locations, although all have varying costs, benefits, and degrees of evidence for their efficacy (Boni, 2022; van der Pluijm et al., 2021; Chen and Hsiang, 2022). It is important to note under each of these strategies, mutants which are resistant to both artemisinin derivatives and partner drugs may eventually arise (Box 2—figure 1). Having a strategy for if this occurs will be paramount, and will require careful genomic surveillance to identify cases where they emerge (Mayor et al., 2023).

ACT cycling

As of 2022, the WHO recommends a policy of ‘ACT cycling’, which involves using a single first-line ACT, before switching to a second once efficacy decreases below a certain threshold in the population (~ 90%) (World Health Organization, 2022a; World Health Organization, 2015a). Theoretically, this should reduce the frequency of resistance to the first drug, by providing time for resistance to decline in frequency while the second drug is in use (Box 2—figure 1; ; Nguyen et al., 2023a; Boni, 2022; Boni et al., 2008). This strategy initially proved effective in Cambodia, where artesunate-mefloquine was replaced with dihydroartemisinin–piperaquine, before reverting back after resistant parasites reduced in frequency (Rasmussen et al., 2022; Ross et al., 2018). However, there is some evidence to suggest the strategy later drove the emergence of triply resistant mutants in the northeast of the country (Rossi et al., 2017).

Multiple front-line therapies

More recently, several countries have adopted the use of MFTs, that is using multiple ACT therapies concurrently within a population (World Health Organization, 2021). Importantly, modelling studies have found MFTs have similar efficacy to cycling strategies but are less likely to accelerate the emergence and spread of resistance (Nguyen et al., 2015; Boni et al., 2008; Smith et al., 2010). This work suggests MFTs are most effective when using partner drugs with different mechanisms of action, as this reduces the chances of cross-resistance to both drugs (Li et al., 2024). However, the WHO has stopped short of recommending MFTs more generally (World Health Organization, 2013), citing contradictions in results from modelling work, and challenges in locations with both high drug use and resistance (Boni, 2022; Antao and Hastings, 2012). For example, the high frequency of the artemisini- resistant lineage in Cambodia means there is a lack of alternative treatments should MFTs fail (Rasmussen et al., 2022; Ross et al., 2018).

Triple artemisinin-based combination therapies (TACTs)

Another alternative is TACTs, which combine an artemisinin derivative with two partner drugs. Theoretically, this method preserves treatment efficacy, because of the rarity of parasites acquiring resistance to both partner drugs over a course of treatment (van der Pluijm et al., 2020). Recently, large-scale clinical trials in Southeast Asia have demonstrated TACTs are effective at national scales, even in situations where multidrug-resistant parasites are present (Peto et al., 2022; van der Pluijm et al., 2020). Modelling studies suggest that TACTs could significantly delay the emergence and spread of ART-R compared to other strategies (Zupko et al., 2023; Li et al., 2024; Nguyen et al., 2023a). However, their effectiveness diminishes once resistant parasites reach a threshold frequency in the population, which can be as low as 1%. A further drawback of TACTs is that because three drugs in total are being administered, the frequency of negative side-effects on patient health is more common (Peto et al., 2022; van der Pluijm et al., 2021; van der Pluijm et al., 2020). While promising, for TACTs to be useful at population scales, several practical issues need to be ironed out, such as correct dosing and formulation of partner drugs for overall safety, alongside increased cost-effectiveness and regional availability (for a review, see Nsanzabana, 2021). Moreover, their use requires public acceptance, as the reduced risk of emergent resistance is being balanced against an increased risk of side effects for any individual taking the drugs (van der Pluijm et al., 2021; van der Pluijm et al., 2020).

Box 2—figure 1

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These increases in ART-R would in turn increase selective pressure for resistance to partner drugs, raising the likelihood of overall treatment failure. Indeed, there is evidence of treatment failure potentially occurring in some areas of Angola and the Democratic Republic of the Congo (Dimbu et al., 2024;Moriarty et al., 2021; Dimbu et al., 2021; World Health Organization, 2022a). Were such failures to become widespread, the human and economic consequences for the region would be disastrous (see Section ‘Widespread ART-R in Africa would be associated with significant human and economic costs’). Alarmingly, the observed increases in ART-R across East and Northeast Africa in recent years suggest this scenario is fast becoming a reality.

Throughout this review, we have highlighted the vital role genomic surveillance has played in understanding the global emergence and spread of ART-R in P. falciparum. In Southeast Asia, genomic surveillance helped to identify instances of migration of resistant parasites between countries and quantify the frequencies of resistance in different areas (see Section ‘Spread of Artemisinin Partial Resistance’). Now, in East and Northeast Africa, genomic surveillance continues to demonstrate its usefulness, by identifying the independent emergence and spread of kelch13 mutations in Sudan, Uganda, Eritrea, and Rwanda (Conrad et al., 2023; Uwimana et al., 2020; Mihreteab et al., 2023; Mohamed et al., 2020; Owoloye et al., 2021; Jalei et al., 2023). These discoveries were only possible due to the enhanced sensitivity of genomic surveillance in detecting low-frequency genetic variants - as several TES from the region suggested treatment efficacy remained high (>95%) (Assefa et al., 2024). However, TES primarily aids in understanding treatment failure due to partner drug resistance, so is not expected to inform on ART-R in the same way as genomic surveillance is able to do. Moving forward, it is essential to use the information gathered from both genomic surveillance and TES, along with lessons learned from ART-R in South-East Asia, to develop and implement mitigation strategies, such as properly allocating healthcare resources (Assefa et al., 2024; World Health Organization, 2015b; World Health Organization, 2021).

Given the emergence of ART-R associated with kelch13 markers in Africa, it is likely only a matter of time before these begin to spread widely across the continent (Dhorda et al., 2024; Rosenthal et al., 2024). This review has highlighted the crucial role of genomic surveillance in alerting public health agencies to the spread of ART-R and in planning the use of limited resources to contain it. Recent years have seen limitations on funding, political instability, and other public health priorities, such as the COVID-19 pandemic, divert already limited resources away from genomic surveillance of P. falciparum, jeopardising the surveillance of ART-R in Africa (Lyimo et al., 2022; Mensah et al., 2022). Despite these challenges, it will be crucial to continually develop and champion the use of genomic surveillance as a vital part of the surveillance toolkit for ART-R, which also contains methods such as treatment efficacy studies and in vitro phenotypic studies. Without coordinating these surveillance tools, increases in ART-R may only be identified when they have already spread widely, negatively affecting patient outcomes and compromising our ability to implement mitigation strategies (Zupko et al., 2023). This means continuing surveillance is vital, not only for containing the spread of ART-R, but also for minimising the human costs it will cause.

1. 1. Abdel Hamid MM
   2. Abdelraheem MH
   3. Acheampong DO
   4. Ahouidi A
   5. Ali M
   6. Almagro-Garcia J
   7. Amambua-Ngwa A
   8. Amaratunga C
   9. Amenga-Etego L
   10. Andagalu B
   11. Anderson T
   12. Andrianaranjaka V
   13. Aniebo I
   14. Aninagyei E
   15. Ansah F
   16. Ansah PO
   17. Apinjoh T
   18. Arnaldo P
   19. Ashley E
   20. Auburn S
   21. Awandare GA
   22. Ba H
   23. Baraka V
   24. Barry A
   25. Bejon P
   26. Bertin GI
   27. Boni MF
   28. Borrmann S
   29. Bousema T
   30. Bouyou-Akotet M
   31. Branch O
   32. Bull PC
   33. Cheah H
   34. Chindavongsa K
   35. Chookajorn T
   36. Chotivanich K
   37. Claessens A
   38. Conway DJ
   39. Corredor V
   40. Courtier E
   41. Craig A
   42. D’Alessandro U
   43. Dama S
   44. Day N
   45. Denis B
   46. Dhorda M
   47. Diakite M
   48. Djimde A
   49. Dolecek C
   50. Dondorp A
   51. Doumbia S
   52. Drakeley C
   53. Drury E
   54. Duffy P
   55. Echeverry DF
   56. Egwang TG
   57. Enosse SMM
   58. Erko B
   59. Fairhurst RM
   60. Faiz A
   61. Fanello CA
   62. Fleharty M
   63. Forbes M
   64. Fukuda M
   65. Gamboa D
   66. Ghansah A
   67. Golassa L
   68. Goncalves S
   69. Harrison GLA
   70. Healy SA
   71. Hendry JA
   72. Hernandez-Koutoucheva A
   73. Hien TT
   74. Hill CA
   75. Hombhanje F
   76. Hott A
   77. Htut Y
   78. Hussein M
   79. Imwong M
   80. Ishengoma D
   81. Jackson SA
   82. Jacob CG
   83. Jeans J
   84. Johnson KJ
   85. Kamaliddin C
   86. Kamau E
   87. Keatley J
   88. Kochakarn T
   89. Konate DS
   90. Konaté A
   91. Kone A
   92. Kwiatkowski DP
   93. Kyaw MP
   94. Kyle D
   95. Lawniczak M
   96. Lee SK
   97. Lemnge M
   98. Lim P
   99. Lon C
   100. Loua KM
   101. Mandara CI
   102. Marfurt J
   103. Marsh K
   104. Maude RJ
   105. Mayxay M
   106. Maïga-Ascofaré O
   107. Miotto O
   108. Mita T
   109. Mobegi V
   110. Mohamed AO
   111. Mokuolu OA
   112. Montgomery J
   113. Morang’a CM
   114. Mueller I
   115. Murie K
   116. Newton PN
   117. Ngo Duc T
   118. Nguyen T
   119. Nguyen T-N
   120. Nguyen Thi Kim T
   121. Nguyen Van H
   122. Noedl H
   123. Nosten F
   124. Noviyanti R
   125. Ntui VN-N
   126. Nzila A
   127. Ochola-Oyier LI
   128. Ocholla H
   129. Oduro A
   130. Omedo I
   131. Onyamboko MA
   132. Ouedraogo J-B
   133. Oyebola K
   134. Oyibo WA
   135. Pearson R
   136. Peshu N
   137. Phyo AP
   138. Plowe CV
   139. Price RN
   140. Pukrittayakamee S
   141. Quang HH
   142. Randrianarivelojosia M
   143. Rayner JC
   144. Ringwald P
   145. Rosanas-Urgell A
   146. Rovira-Vallbona E
   147. Ruano-Rubio V
   148. Ruiz L
   149. Saunders D
   150. Shayo A
   151. Siba P
   152. Simpson VJ
   153. Sissoko MS
   154. Smith C
   155. Su X-Z
   156. Sutherland C
   157. Takala-Harrison S
   158. Talman A
   159. Tavul L
   160. Thanh NV
   161. Thathy V
   162. Thu AM
   163. Toure M
   164. Tshefu A
   165. Verra F
   166. Vinetz J
   167. Wellems TE
   168. Wendler J
   169. White NJ
   170. Whitton G
   171. Yavo W
   172. van der Pluijm RW
   173. MalariaGEN

   (2023) Pf7: an open dataset of Plasmodium falciparum genome variation in 20,000 worldwide samples

   Wellcome Open Research 8:22.

   https://doi.org/10.12688/wellcomeopenres.18681.1

   • PubMed
   • Google Scholar
2. 1. Abebaw A
   2. Aschale Y
   3. Kebede T
   4. Hailu A

   (2022) The prevalence of symptomatic and asymptomatic malaria and its associated factors in Debre Elias district communities, Northwest Ethiopia

   Malaria Journal 21:e167.

   https://doi.org/10.1186/s12936-022-04194-7

   • PubMed
   • Google Scholar
3. 1. Abera D
   2. Kibet CK
   3. Degefa T
   4. Amenga-Etego L
   5. Bargul JL
   6. Golassa L

   (2021) Genomic analysis reveals independent evolution of Plasmodium falciparum populations in Ethiopia

   Malaria Journal 20:e129.

   https://doi.org/10.1186/s12936-021-03660-y

   • PubMed
   • Google Scholar
4. 1. Agaba BB
   2. Rugera SP
   3. Mpirirwe R
   4. Atekat M
   5. Okubal S
   6. Masereka K
   7. Erionu M
   8. Adranya B
   9. Nabirwa G
   10. Odong PB
   11. Mukiibi Y
   12. Ssewanyana I
   13. Nabadda S
   14. Muwanguzi E

   (2022) Asymptomatic malaria infection, associated factors and accuracy of diagnostic tests in a historically high transmission setting in Northern Uganda

   Malaria Journal 21:e392.

   https://doi.org/10.1186/s12936-022-04421-1

   • PubMed
   • Google Scholar
5. 1. Amambua-Ngwa A
   2. Amenga-Etego L
   3. Kamau E
   4. Amato R
   5. Ghansah A
   6. Golassa L
   7. Randrianarivelojosia M
   8. Ishengoma D
   9. Apinjoh T
   10. Maïga-Ascofaré O
   11. Andagalu B
   12. Yavo W
   13. Bouyou-Akotet M
   14. Kolapo O
   15. Mane K
   16. Worwui A
   17. Jeffries D
   18. Simpson V
   19. D’Alessandro U
   20. Kwiatkowski D
   21. Djimde AA

   (2019) Major subpopulations of Plasmodium falciparum in sub-Saharan Africa

   Science 365:813–816.

   https://doi.org/10.1126/science.aav5427

   • PubMed
   • Google Scholar
6. 1. Amato R
   2. Pearson RD
   3. Almagro-Garcia J
   4. Amaratunga C
   5. Lim P
   6. Suon S
   7. Sreng S
   8. Drury E
   9. Stalker J
   10. Miotto O
   11. Fairhurst RM
   12. Kwiatkowski DP

   (2018) Origins of the current outbreak of multidrug-resistant malaria in southeast Asia: a retrospective genetic study

   The Lancet. Infectious Diseases 18:337–345.

   https://doi.org/10.1016/S1473-3099(18)30068-9

   • PubMed
   • Google Scholar
7. 1. Amusan A
   2. Akinola O
   3. Akano K
   4. Hernández-Castañeda M
   5. Dick JK
   6. Sowunmi A
   7. Hart G
   8. Gbotosho G

   (2025) Polymorphisms in Pfkelch13 domains before and after the introduction of artemisinin-based combination therapy in Southwest Nigeria

   PLOS ONE 20:e0316479.

   https://doi.org/10.1371/journal.pone.0316479

   • PubMed
   • Google Scholar
8. 1. Anderson TJC
   2. Nair S
   3. McDew-White M
   4. Cheeseman IH
   5. Nkhoma S
   6. Bilgic F
   7. McGready R
   8. Ashley E
   9. Pyae Phyo A
   10. White NJ
   11. Nosten F

   (2017) Population parameters underlying an ongoing soft sweep in Southeast Asian malaria parasites

   Molecular Biology and Evolution 34:131–144.

   https://doi.org/10.1093/molbev/msw228

   • PubMed
   • Google Scholar
9. 1. Antao T
   2. Hastings I

   (2012) Policy options for deploying anti-malarial drugs in endemic countries: a population genetics approach

   Malaria Journal 11:e422.

   https://doi.org/10.1186/1475-2875-11-422

   • Google Scholar
10. 1. Ariey F
    2. Witkowski B
    3. Amaratunga C
    4. Beghain J
    5. Langlois A-C
    6. Khim N
    7. Kim S
    8. Duru V
    9. Bouchier C
    10. Ma L
    11. Lim P
    12. Leang R
    13. Duong S
    14. Sreng S
    15. Suon S
    16. Chuor CM
    17. Bout DM
    18. Ménard S
    19. Rogers WO
    20. Genton B
    21. Fandeur T
    22. Miotto O
    23. Ringwald P
    24. Le Bras J
    25. Berry A
    26. Barale J-C
    27. Fairhurst RM
    28. Benoit-Vical F
    29. Mercereau-Puijalon O
    30. Ménard D

    (2014) A molecular marker of artemisinin-resistant Plasmodium falciparum malaria

    Nature 505:50–55.

    https://doi.org/10.1038/nature12876

    • PubMed
    • Google Scholar
11. 1. Ashley EA
    2. Dhorda M
    3. Fairhurst RM
    4. Amaratunga C
    5. Lim P
    6. Suon S
    7. Sreng S
    8. Anderson JM
    9. Mao S
    10. Sam B
    11. Sopha C
    12. Chuor CM
    13. Nguon C
    14. Sovannaroth S
    15. Pukrittayakamee S
    16. Jittamala P
    17. Chotivanich K
    18. Chutasmit K
    19. Suchatsoonthorn C
    20. Runcharoen R
    21. Hien TT
    22. Thuy-Nhien NT
    23. Thanh NV
    24. Phu NH
    25. Htut Y
    26. Han K-T
    27. Aye KH
    28. Mokuolu OA
    29. Olaosebikan RR
    30. Folaranmi OO
    31. Mayxay M
    32. Khanthavong M
    33. Hongvanthong B
    34. Newton PN
    35. Onyamboko MA
    36. Fanello CI
    37. Tshefu AK
    38. Mishra N
    39. Valecha N
    40. Phyo AP
    41. Nosten F
    42. Yi P
    43. Tripura R
    44. Borrmann S
    45. Bashraheil M
    46. Peshu J
    47. Faiz MA
    48. Ghose A
    49. Hossain MA
    50. Samad R
    51. Rahman MR
    52. Hasan MM
    53. Islam A
    54. Miotto O
    55. Amato R
    56. MacInnis B
    57. Stalker J
    58. Kwiatkowski DP
    59. Bozdech Z
    60. Jeeyapant A
    61. Cheah PY
    62. Sakulthaew T
    63. Chalk J
    64. Intharabut B
    65. Silamut K
    66. Lee SJ
    67. Vihokhern B
    68. Kunasol C
    69. Imwong M
    70. Tarning J
    71. Taylor WJ
    72. Yeung S
    73. Woodrow CJ
    74. Flegg JA
    75. Das D
    76. Smith J
    77. Venkatesan M
    78. Plowe CV
    79. Stepniewska K
    80. Guerin PJ
    81. Dondorp AM
    82. Day NP
    83. White NJ
    84. Tracking Resistance to Artemisinin Collaboration

    (2014) Spread of artemisinin resistance in Plasmodium falciparum malaria

    The New England Journal of Medicine 371:411–423.

    https://doi.org/10.1056/NEJMoa1314981

    • PubMed
    • Google Scholar
12. 1. Assefa A
    2. Fola AA
    3. Tasew G

    (2024) Emergence of Plasmodium falciparum strains with artemisinin partial resistance in East Africa and the Horn of Africa: is there a need to panic?

    Malaria Journal 23:34.

    https://doi.org/10.1186/s12936-024-04848-8

    • PubMed
    • Google Scholar
13. 1. Asua V
    2. Vinden J
    3. Conrad MD
    4. Legac J
    5. Kigozi SP
    6. Kamya MR
    7. Dorsey G
    8. Nsobya SL
    9. Rosenthal PJ

    (2019) Changing molecular markers of antimalarial drug sensitivity across Uganda

    Antimicrobial Agents and Chemotherapy 63:e01818-18.

    https://doi.org/10.1128/AAC.01818-18

    • Google Scholar
14. 1. Ataide R
    2. Ashley EA
    3. Powell R
    4. Chan J-A
    5. Malloy MJ
    6. O’Flaherty K
    7. Takashima E
    8. Langer C
    9. Tsuboi T
    10. Dondorp AM
    11. Day NP
    12. Dhorda M
    13. Fairhurst RM
    14. Lim P
    15. Amaratunga C
    16. Pukrittayakamee S
    17. Hien TT
    18. Htut Y
    19. Mayxay M
    20. Faiz MA
    21. Beeson JG
    22. Nosten F
    23. Simpson JA
    24. White NJ
    25. Fowkes FJI

    (2017) Host immunity to Plasmodium falciparum and the assessment of emerging artemisinin resistance in a multinational cohort

    PNAS 114:3515–3520.

    https://doi.org/10.1073/pnas.1615875114

    • PubMed
    • Google Scholar
15. 1. Balikagala B
    2. Fukuda N
    3. Ikeda M
    4. Katuro OT
    5. Tachibana S-I
    6. Yamauchi M
    7. Opio W
    8. Emoto S
    9. Anywar DA
    10. Kimura E
    11. Palacpac NMQ
    12. Odongo-Aginya EI
    13. Ogwang M
    14. Horii T
    15. Mita T

    (2021) Evidence of artemisinin-resistant malaria in Africa

    The New England Journal of Medicine 385:1163–1171.

    https://doi.org/10.1056/NEJMoa2101746

    • PubMed
    • Google Scholar
16. 1. Baym M
    2. Stone LK
    3. Kishony R

    (2016) Multidrug evolutionary strategies to reverse antibiotic resistance

    Science 351:aad3292.

    https://doi.org/10.1126/science.aad3292

    • PubMed
    • Google Scholar
17. 1. Behrens HM
    2. Schmidt S
    3. Spielmann T

    (2021) The newly discovered role of endocytosis in artemisinin resistance

    Medicinal Research Reviews 41:2998–3022.

    https://doi.org/10.1002/med.21848

    • PubMed
    • Google Scholar
18. Preprint

    1. Behrens HM
    2. Schmidt S
    3. Peigney D
    4. Sabitzki R
    5. Henshall I
    6. May J
    7. Maïga-Ascofaré O
    8. Spielmann T

    (2023) Impact of different mutations on kelch13 protein levels, ART resistance and fitness cost in Plasmodium falciparum parasites

    bioRxiv.

    https://doi.org/10.1101/2022.05.13.491767

    • Google Scholar
19. 1. Behrens HM
    2. Schmidt S
    3. Henshall IG
    4. López-Barona P
    5. Peigney D
    6. Sabitzki R
    7. May J
    8. Maïga-Ascofaré O
    9. Spielmann T

    (2024) Impact of different mutations on Kelch13 protein levels, ART resistance, and fitness cost in Plasmodium falciparum parasites

    mBio 15:e0198123.

    https://doi.org/10.1128/mbio.01981-23

    • PubMed
    • Google Scholar
20. 1. Bennett A
    2. Bisanzio D
    3. Yukich JO
    4. Mappin B
    5. Fergus CA
    6. Lynch M
    7. Cibulskis RE
    8. Bhatt S
    9. Weiss DJ
    10. Cameron E
    11. Gething PW
    12. Eisele TP

    (2017) Population coverage of artemisinin-based combination treatment in children younger than 5 years with fever and Plasmodium falciparum infection in Africa, 2003-2015: a modelling study using data from national surveys

    The Lancet. Global Health 5:e418–e427.

    https://doi.org/10.1016/S2214-109X(17)30076-1

    • PubMed
    • Google Scholar
21. 1. Björkman Nyqvist M
    2. Svensson J
    3. Yanagizawa-Drott D

    (2022) Can good products drive out bad? a randomized intervention in the antimalarial medicine market in Uganda

    Journal of the European Economic Association 20:957–1000.

    https://doi.org/10.1093/jeea/jvab053

    • Google Scholar
22. 1. Boni MF
    2. Smith DL
    3. Laxminarayan R

    (2008) Benefits of using multiple first-line therapies against malaria

    PNAS 105:14216–14221.

    https://doi.org/10.1073/pnas.0804628105

    • Google Scholar
23. 1. Boni MF

    (2022) Breaking the cycle of malaria treatment failure

    Frontiers in Epidemiology 2:1041896.

    https://doi.org/10.3389/fepid.2022.1041896

    • PubMed
    • Google Scholar
24. 1. Bonnington CA
    2. Phyo AP
    3. Ashley EA
    4. Imwong M
    5. Sriprawat K
    6. Parker DM
    7. Proux S
    8. White NJ
    9. Nosten F

    (2017) Plasmodium falciparum kelch 13 mutations and treatment response in patients in Hpa-Pun District, Northern Kayin state, Myanmar

    Malaria Journal 16:480.

    https://doi.org/10.1186/s12936-017-2128-x

    • PubMed
    • Google Scholar
25. 1. Bunditvorapoom D
    2. Kochakarn T
    3. Kotanan N
    4. Modchang C
    5. Kümpornsin K
    6. Loesbanluechai D
    7. Krasae T
    8. Cui L
    9. Chotivanich K
    10. White NJ
    11. Wilairat P
    12. Miotto O
    13. Chookajorn T

    (2018) Fitness loss under amino acid starvation in Artemisinin-resistant Plasmodium falciparum isolates from Cambodia

    Scientific Reports 8:12622.

    https://doi.org/10.1038/s41598-018-30593-5

    • PubMed
    • Google Scholar
26. 1. Bushman M
    2. Antia R
    3. Udhayakumar V
    4. de Roode JC

    (2018) Within-host competition can delay evolution of drug resistance in malaria

    PLOS Biology 16:e2005712.

    https://doi.org/10.1371/journal.pbio.2005712

    • PubMed
    • Google Scholar
27. 1. Bylicka-Szczepanowska E
    2. Korzeniewski K

    (2022) Asymptomatic malaria infections in the time of COVID-19 pandemic: experience from the central African republic

    International Journal of Environmental Research and Public Health 19:e3544.

    https://doi.org/10.3390/ijerph19063544

    • PubMed
    • Google Scholar
28. 1. Cerqueira GC
    2. Cheeseman IH
    3. Schaffner SF
    4. Nair S
    5. McDew-White M
    6. Phyo AP
    7. Ashley EA
    8. Melnikov A
    9. Rogov P
    10. Birren BW
    11. Nosten F
    12. Anderson TJC
    13. Neafsey DE

    (2017) Longitudinal genomic surveillance of Plasmodium falciparum malaria parasites reveals complex genomic architecture of emerging artemisinin resistance

    Genome Biology 18:e78.

    https://doi.org/10.1186/s13059-017-1204-4

    • PubMed
    • Google Scholar
29. 1. Chen I
    2. Hsiang MS

    (2022) Triple artemisinin-based combination therapies for malaria: a timely solution to counter antimalarial drug resistance

    The Lancet Infectious Diseases 22:751–753.

    https://doi.org/10.1016/S1473-3099(21)00748-9

    • Google Scholar
30. 1. Churcher TS
    2. Trape JF
    3. Cohuet A

    (2015) Human-to-mosquito transmission efficiency increases as malaria is controlled

    Nature Communications 6:6054.

    https://doi.org/10.1038/ncomms7054

    • PubMed
    • Google Scholar
31. 1. Cohen JM
    2. Smith DL
    3. Cotter C
    4. Ward A
    5. Yamey G
    6. Sabot OJ
    7. Moonen B

    (2012) Malaria resurgence: a systematic review and assessment of its causes

    Malaria Journal 11:122.

    https://doi.org/10.1186/1475-2875-11-122

    • PubMed
    • Google Scholar
32. 1. Conrad MD
    2. Rosenthal PJ

    (2019) Antimalarial drug resistance in Africa: the calm before the storm?

    The Lancet. Infectious Diseases 19:e338–e351.

    https://doi.org/10.1016/S1473-3099(19)30261-0

    • PubMed
    • Google Scholar
33. 1. Conrad MD
    2. Asua V
    3. Garg S
    4. Giesbrecht D
    5. Niaré K
    6. Smith S
    7. Namuganga JF
    8. Katairo T
    9. Legac J
    10. Crudale RM
    11. Tumwebaze PK
    12. Nsobya SL
    13. Cooper RA
    14. Kamya MR
    15. Dorsey G
    16. Bailey JA
    17. Rosenthal PJ

    (2023) Evolution of partial resistance to Artemisinins in malaria parasites in Uganda

    The New England Journal of Medicine 389:722–732.

    https://doi.org/10.1056/NEJMoa2211803

    • PubMed
    • Google Scholar
34. 1. Coulibaly A
    2. Diop MF
    3. Kone A
    4. Dara A
    5. Ouattara A
    6. Mulder N
    7. Miotto O
    8. Diakite M
    9. Djimde A
    10. Amambua-Ngwa A

    (2022) Genome-wide SNP analysis of Plasmodium falciparum shows differentiation at drug-resistance-associated loci among malaria transmission settings in southern Mali

    Frontiers in Genetics 13:943445.

    https://doi.org/10.3389/fgene.2022.943445

    • PubMed
    • Google Scholar
35. 1. Datoo MS
    2. Dicko A
    3. Tinto H
    4. Ouédraogo JB
    5. Hamaluba M
    6. Olotu A
    7. Beaumont E
    8. Ramos Lopez F
    9. Natama HM
    10. Weston S
    11. Chemba M
    12. Compaore YD
    13. Issiaka D
    14. Salou D
    15. Some AM
    16. Omenda S
    17. Lawrie A
    18. Bejon P
    19. Rao H
    20. Chandramohan D
    21. Roberts R
    22. Bharati S
    23. Stockdale L
    24. Gairola S
    25. Greenwood BM
    26. Ewer KJ
    27. Bradley J
    28. Kulkarni PS
    29. Shaligram U
    30. Hill AVS
    31. Mahamar A
    32. Sanogo K
    33. Sidibe Y
    34. Diarra K
    35. Samassekou M
    36. Attaher O
    37. Tapily A
    38. Diallo M
    39. Dicko OM
    40. Kaya M
    41. Maguiraga SO
    42. Sankare Y
    43. Yalcouye H
    44. Diarra S
    45. Niambele SM
    46. Thera I
    47. Sagara I
    48. Sylla M
    49. Dolo A
    50. Misidai N
    51. Simando S
    52. Msami H
    53. Juma O
    54. Gutapaka N
    55. Paul R
    56. Mswata S
    57. Sasamalo I
    58. Johaness K
    59. Sultan M
    60. Alexander A
    61. Kimaro I
    62. Lwanga K
    63. Mtungwe M
    64. Khamis K
    65. Rugarabam L
    66. Kalinga W
    67. Mohammed M
    68. Kamange J
    69. Msangi J
    70. Mwaijande B
    71. Mtaka I
    72. Mhapa M
    73. Mlaganile T
    74. Mbaga T
    75. Yerbanga RS
    76. Samtouma W
    77. Sienou AA
    78. Kabre Z
    79. Ouedraogo WJM
    80. Yarbanga GAB
    81. Zongo I
    82. Savadogo H
    83. Sanon J
    84. Compaore J
    85. Kere I
    86. Yoni FL
    87. Sanre TM
    88. Ouattara SB
    89. Provstgaard-Morys S
    90. Woods D
    91. Snow RW
    92. Amek N
    93. Ngetsa CJ
    94. Ochola-Oyier LI
    95. Musyoki J
    96. Munene M
    97. Mumba N
    98. Adetifa UJ
    99. Muiruri CM
    100. Mwawaka JS
    101. Mwaganyuma MH
    102. Ndichu MN
    103. Weya JO
    104. Njogu K
    105. Grant J
    106. Webster J
    107. Lakhkar A
    108. Ido NFA
    109. Traore O
    110. Tahita MC
    111. Bonko MDA
    112. Rouamba T
    113. Ouedraogo DF
    114. Soma R
    115. Millogo A
    116. Ouedraogo E
    117. Sorgho F
    118. Konate F
    119. Valea I

    (2024) Safety and efficacy of malaria vaccine candidate R21/Matrix-M in African children: a multicentre, double-blind, randomised, phase 3 trial

    The Lancet 403:533–544.

    https://doi.org/10.1016/S0140-6736(23)02511-4

    • Google Scholar
36. 1. de Haan F
    2. Amaratunga C
    3. Thi VAC
    4. Orng LH
    5. Vonglokham M
    6. Quang TN
    7. Lek D
    8. Boon WPC
    9. Dondorp AM
    10. Moors EHM

    (2023) Strategies for deploying triple artemisinin-based combination therapy in the Greater Mekong Subregion

    Malaria Journal 22:261.

    https://doi.org/10.1186/s12936-023-04666-4

    • PubMed
    • Google Scholar
37. 1. de Laurent ZR
    2. Chebon LJ
    3. Ingasia LA
    4. Akala HM
    5. Andagalu B
    6. Ochola-Oyier LI
    7. Kamau E

    (2018) Polymorphisms in the K13 Gene in Plasmodium falciparum from different malaria transmission areas of Kenya

    The American Journal of Tropical Medicine and Hygiene 98:1360–1366.

    https://doi.org/10.4269/ajtmh.17-0505

    • PubMed
    • Google Scholar
38. 1. Demas AR
    2. Sharma AI
    3. Wong W
    4. Early AM
    5. Redmond S
    6. Bopp S
    7. Neafsey DE
    8. Volkman SK
    9. Hartl DL
    10. Wirth DF

    (2018) Mutations in Plasmodium falciparum actin-binding protein coronin confer reduced artemisinin susceptibility

    PNAS 115:12799–12804.

    https://doi.org/10.1073/pnas.1812317115

    • PubMed
    • Google Scholar
39. 1. Dhorda M
    2. Kaneko A
    3. Komatsu R
    4. Kc A
    5. Mshamu S
    6. Gesase S
    7. Kapologwe N
    8. Assefa A
    9. Opigo J
    10. Adoke Y
    11. Ebong C
    12. Karema C
    13. Uwimana A
    14. Mangara J-LN
    15. Amaratunga C
    16. Peto TJ
    17. Tripura R
    18. Callery JJ
    19. Adhikari B
    20. Mukaka M
    21. Cheah PY
    22. Mutesa L
    23. Day NPJ
    24. Barnes KI
    25. Dondorp A
    26. Rosenthal PJ
    27. White NJ
    28. von Seidlein L

    (2024) Artemisinin-resistant malaria in Africa demands urgent action

    Science 385:252–254.

    https://doi.org/10.1126/science.adp5137

    • PubMed
    • Google Scholar
40. 1. Dimbu PR
    2. Horth R
    3. Cândido ALM
    4. Ferreira CM
    5. Caquece F
    6. Garcia LEA
    7. André K
    8. Pembele G
    9. Jandondo D
    10. Bondo BJ
    11. Nieto Andrade B
    12. Labuda S
    13. Ponce de León G
    14. Kelley J
    15. Patel D
    16. Svigel SS
    17. Talundzic E
    18. Lucchi N
    19. Morais JFM
    20. Fortes F
    21. Martins JF
    22. Pluciński MM

    (2021) Continued low efficacy of artemether-lumefantrine in Angola in 2019

    Antimicrobial Agents and Chemotherapy 65:e01949.

    https://doi.org/10.1128/AAC.01949-20

    • Google Scholar
41. 1. Dimbu PR
    2. Labuda S
    3. Ferreira CM
    4. Caquece F
    5. André K
    6. Pembele G
    7. Pode D
    8. João MF
    9. Pelenda VM
    10. Nieto Andrade B
    11. Horton B
    12. Kennedy C
    13. Svigel SS
    14. Zhou Z
    15. Morais JFM
    16. Rosário J do
    17. Fortes F
    18. Martins JF
    19. Plucinski MM

    (2024) Therapeutic response to four artemisinin-based combination therapies in Angola, 2021

    Antimicrobial Agents and Chemotherapy 68:e01525.

    https://doi.org/10.1128/aac.01525-23

    • Google Scholar
42. 1. Dondorp AM
    2. Nosten F
    3. Yi P
    4. Das D
    5. Phyo AP
    6. Tarning J
    7. Lwin KM
    8. Ariey F
    9. Hanpithakpong W
    10. Lee SJ
    11. Ringwald P
    12. Silamut K
    13. Imwong M
    14. Chotivanich K
    15. Lim P
    16. Herdman T
    17. An SS
    18. Yeung S
    19. Singhasivanon P
    20. Day NPJ
    21. Lindegardh N
    22. Socheat D
    23. White NJ

    (2009) Artemisinin resistance in Plasmodium falciparum malaria

    The New England Journal of Medicine 361:455–467.

    https://doi.org/10.1056/NEJMoa0808859

    • PubMed
    • Google Scholar
43. 1. Doolan DL
    2. Dobaño C
    3. Baird JK

    (2009) Acquired immunity to malaria

    Clinical Microbiology Reviews 22:13–36.

    https://doi.org/10.1128/CMR.00025-08

    • PubMed
    • Google Scholar
44. 1. Duffey M
    2. Blasco B
    3. Burrows JN
    4. Wells TNC
    5. Fidock DA
    6. Leroy D

    (2021) Assessing risks of Plasmodium falciparum resistance to select next-generation antimalarials

    Trends in Parasitology 37:709–721.

    https://doi.org/10.1016/j.pt.2021.04.006

    • PubMed
    • Google Scholar
45. 1. Ebong C
    2. Sserwanga A
    3. Namuganga JF
    4. Kapisi J
    5. Mpimbaza A
    6. Gonahasa S
    7. Asua V
    8. Gudoi S
    9. Kigozi R
    10. Tibenderana J
    11. Bwanika JB
    12. Bosco A
    13. Rubahika D
    14. Kyabayinze D
    15. Opigo J
    16. Rutazana D
    17. Sebikaari G
    18. Belay K
    19. Niang M
    20. Halsey ES
    21. Moriarty LF
    22. Lucchi NW
    23. Souza SSS
    24. Nsobya SL
    25. Kamya MR
    26. Yeka A

    (2021) Efficacy and safety of artemether-lumefantrine and dihydroartemisinin-piperaquine for the treatment of uncomplicated Plasmodium falciparum malaria and prevalence of molecular markers associated with artemisinin and partner drug resistance in Uganda

    Malaria Journal 20:e484.

    https://doi.org/10.1186/s12936-021-04021-5

    • Google Scholar
46. 1. Erhunse N
    2. Sahal D

    (2021) Protecting future antimalarials from the trap of resistance: Lessons from artemisinin-based combination therapy (ACT) failures

    Journal of Pharmaceutical Analysis 11:541–554.

    https://doi.org/10.1016/j.jpha.2020.07.005

    • PubMed
    • Google Scholar
47. 1. Fola AA
    2. Feleke SM
    3. Mohammed H
    4. Brhane BG
    5. Hennelly CM
    6. Assefa A
    7. Crudal RM
    8. Reichert E
    9. Juliano JJ
    10. Cunningham J
    11. Mamo H
    12. Solomon H
    13. Tasew G
    14. Petros B
    15. Parr JB
    16. Bailey JA

    (2023) Plasmodium falciparum resistant to artemisinin and diagnostics have emerged in Ethiopia

    Nature Microbiology 8:1911–1919.

    https://doi.org/10.1038/s41564-023-01461-4

    • PubMed
    • Google Scholar
48. 1. Gansané A
    2. Moriarty LF
    3. Ménard D
    4. Yerbanga I
    5. Ouedraogo E
    6. Sondo P
    7. Kinda R
    8. Tarama C
    9. Soulama E
    10. Tapsoba M
    11. Kangoye D
    12. Compaore CS
    13. Badolo O
    14. Dao B
    15. Tchwenko S
    16. Tinto H
    17. Valea I

    (2021) Anti-malarial efficacy and resistance monitoring of artemether-lumefantrine and dihydroartemisinin-piperaquine shows inadequate efficacy in children in Burkina Faso, 2017-2018

    Malaria Journal 20:48.

    https://doi.org/10.1186/s12936-021-03585-6

    • PubMed
    • Google Scholar
49. 1. Gregson A
    2. Plowe CV

    (2005) Mechanisms of resistance of malaria parasites to antifolates

    Pharmacological Reviews 57:117–145.

    https://doi.org/10.1124/pr.57.1.4

    • PubMed
    • Google Scholar
50. 1. Guillot C
    2. Bouchard C
    3. Aenishaenslin C
    4. Berthiaume P
    5. Milord F
    6. Leighton PA

    (2022) Criteria for selecting sentinel unit locations in a surveillance system for vector-borne disease: A decision tool

    Frontiers in Public Health 10:1003949.

    https://doi.org/10.3389/fpubh.2022.1003949

    • PubMed
    • Google Scholar
51. 1. Hailu A
    2. Lindtjørn B
    3. Deressa W
    4. Gari T
    5. Loha E
    6. Robberstad B

    (2017) Economic burden of malaria and predictors of cost variability to rural households in south-central Ethiopia

    PLOS ONE 12:e0185315.

    https://doi.org/10.1371/journal.pone.0185315

    • PubMed
    • Google Scholar
52. 1. Hamilton WL
    2. Amato R
    3. van der Pluijm RW
    4. Jacob CG
    5. Quang HH
    6. Thuy-Nhien NT
    7. Hien TT
    8. Hongvanthong B
    9. Chindavongsa K
    10. Mayxay M
    11. Huy R
    12. Leang R
    13. Huch C
    14. Dysoley L
    15. Amaratunga C
    16. Suon S
    17. Fairhurst RM
    18. Tripura R
    19. Peto TJ
    20. Sovann Y
    21. Jittamala P
    22. Hanboonkunupakarn B
    23. Pukrittayakamee S
    24. Chau NH
    25. Imwong M
    26. Dhorda M
    27. Vongpromek R
    28. Chan XHS
    29. Maude RJ
    30. Pearson RD
    31. Nguyen T
    32. Rockett K
    33. Drury E
    34. Gonçalves S
    35. White NJ
    36. Day NP
    37. Kwiatkowski DP
    38. Dondorp AM
    39. Miotto O

    (2019) Evolution and expansion of multidrug-resistant malaria in southeast Asia: a genomic epidemiology study

    The Lancet. Infectious Diseases 19:943–951.

    https://doi.org/10.1016/S1473-3099(19)30392-5

    • PubMed
    • Google Scholar
53. 1. Hamilton WL
    2. Ishengoma DS
    3. Parr JB
    4. Bridges DJ
    5. Barry AE

    (2023) Nanopore sequencing for malaria molecular surveillance: opportunities and challenges

    Trends in Parasitology 39:996–1000.

    https://doi.org/10.1016/j.pt.2023.09.014

    • PubMed
    • Google Scholar
54. 1. Hanboonkunupakarn B
    2. White NJ

    (2022) Advances and roadblocks in the treatment of malaria

    British Journal of Clinical Pharmacology 88:374–382.

    https://doi.org/10.1111/bcp.14474

    • PubMed
    • Google Scholar
55. 1. Hassett MR
    2. Roepe PD

    (2019) Origin and spread of evolving artemisinin-resistant Plasmodium falciparum malarial parasites in Southeast Asia

    The American Journal of Tropical Medicine and Hygiene 101:1204–1211.

    https://doi.org/10.4269/ajtmh.19-0379

    • PubMed
    • Google Scholar
56. 1. He Q
    2. Chaillet JK
    3. Labbé F

    (2024) Antigenic strain diversity predicts different biogeographic patterns of maintenance and decline of antimalarial drug resistance

    eLife 12:RP90888.

    https://doi.org/10.7554/eLife.90888

    • PubMed
    • Google Scholar
57. 1. Heller LE
    2. Goggins E
    3. Roepe PD

    (2018a) Dihydroartemisinin-Ferriprotoporphyrin IX adduct abundance in Plasmodium falciparum malarial parasites and the relationship to emerging artemisinin resistance

    Biochemistry 57:6935–6945.

    https://doi.org/10.1021/acs.biochem.8b00960

    • PubMed
    • Google Scholar
58. 1. Heller LE
    2. Roepe PD

    (2018b) Quantification of free Ferriprotoporphyrin IX Heme and Hemozoin for Artemisinin sensitive versus delayed clearance phenotype Plasmodium falciparum malarial parasites

    Biochemistry 57:6927–6934.

    https://doi.org/10.1021/acs.biochem.8b00959

    • PubMed
    • Google Scholar
59. 1. Henden L
    2. Lee S
    3. Mueller I
    4. Barry A
    5. Bahlo M

    (2018) Identity-by-descent analyses for measuring population dynamics and selection in recombining pathogens

    PLOS Genetics 14:e1007279.

    https://doi.org/10.1371/journal.pgen.1007279

    • Google Scholar
60. 1. Henriques G
    2. Hallett RL
    3. Beshir KB
    4. Gadalla NB
    5. Johnson RE
    6. Burrow R
    7. van Schalkwyk DA
    8. Sawa P
    9. Omar SA
    10. Clark TG
    11. Bousema T
    12. Sutherland CJ

    (2014) Directional selection at the pfmdr1, pfcrt, pfubp1, and pfap2mu loci of Plasmodium falciparum in Kenyan children treated with ACT

    The Journal of Infectious Diseases 210:2001–2008.

    https://doi.org/10.1093/infdis/jiu358

    • PubMed
    • Google Scholar
61. 1. Ikeda M
    2. Kaneko M
    3. Tachibana S-I
    4. Balikagala B
    5. Sakurai-Yatsushiro M
    6. Yatsushiro S
    7. Takahashi N
    8. Yamauchi M
    9. Sekihara M
    10. Hashimoto M
    11. Katuro OT
    12. Olia A
    13. Obwoya PS
    14. Auma MA
    15. Anywar DA
    16. Odongo-Aginya EI
    17. Okello-Onen J
    18. Hirai M
    19. Ohashi J
    20. Palacpac NMQ
    21. Kataoka M
    22. Tsuboi T
    23. Kimura E
    24. Horii T
    25. Mita T

    (2018) Artemisinin-resistant Plasmodium falciparum with high survival rates, Uganda, 2014-2016

    Emerging Infectious Diseases 24:718–726.

    https://doi.org/10.3201/eid2404.170141

    • PubMed
    • Google Scholar
62. 1. Imwong M
    2. Suwannasin K
    3. Kunasol C
    4. Sutawong K
    5. Mayxay M
    6. Rekol H
    7. Smithuis FM
    8. Hlaing TM
    9. Tun KM
    10. van der Pluijm RW
    11. Tripura R
    12. Miotto O
    13. Menard D
    14. Dhorda M
    15. Day NPJ
    16. White NJ
    17. Dondorp AM

    (2017) The spread of artemisinin-resistant Plasmodium falciparum in the Greater Mekong subregion: a molecular epidemiology observational study

    The Lancet. Infectious Diseases 17:491–497.

    https://doi.org/10.1016/S1473-3099(17)30048-8

    • PubMed
    • Google Scholar
63. 1. Imwong M
    2. Dhorda M
    3. Myo Tun K
    4. Thu AM
    5. Phyo AP
    6. Proux S
    7. Suwannasin K
    8. Kunasol C
    9. Srisutham S
    10. Duanguppama J
    11. Vongpromek R
    12. Promnarate C
    13. Saejeng A
    14. Khantikul N
    15. Sugaram R
    16. Thanapongpichat S
    17. Sawangjaroen N
    18. Sutawong K
    19. Han KT
    20. Htut Y
    21. Linn K
    22. Win AA
    23. Hlaing TM
    24. van der Pluijm RW
    25. Mayxay M
    26. Pongvongsa T
    27. Phommasone K
    28. Tripura R
    29. Peto TJ
    30. von Seidlein L
    31. Nguon C
    32. Lek D
    33. Chan XHS
    34. Rekol H
    35. Leang R
    36. Huch C
    37. Kwiatkowski DP
    38. Miotto O
    39. Ashley EA
    40. Kyaw MP
    41. Pukrittayakamee S
    42. Day NPJ
    43. Dondorp AM
    44. Smithuis FM
    45. Nosten FH
    46. White NJ

    (2020) Molecular epidemiology of resistance to antimalarial drugs in the greater Mekong subregion: an observational study

    The Lancet. Infectious Diseases 20:1470–1480.

    https://doi.org/10.1016/S1473-3099(20)30228-0

    • PubMed
    • Google Scholar
64. 1. Ippolito MM
    2. Pringle JC
    3. Siame M
    4. Katowa B
    5. Aydemir O
    6. Oluoch PO
    7. Huang L
    8. Aweeka FT
    9. Bailey JA
    10. Juliano JJ
    11. Meshnick SR
    12. Shapiro TA
    13. Moss WJ
    14. Thuma PE

    (2020) Therapeutic efficacy of artemether-lumefantrine for uncomplicated falciparum malaria in Northern Zambia

    The American Journal of Tropical Medicine and Hygiene 103:2224–2232.

    https://doi.org/10.4269/ajtmh.20-0852

    • PubMed
    • Google Scholar
65. 1. Ishengoma DS
    2. Saidi Q
    3. Sibley CH
    4. Roper C
    5. Alifrangis M

    (2019) Deployment and utilization of next-generation sequencing of Plasmodium falciparum to guide anti-malarial drug policy decisions in sub-Saharan Africa: opportunities and challenges

    Malaria Journal 18:e267.

    https://doi.org/10.1186/s12936-019-2853-4

    • PubMed
    • Google Scholar
66. Preprint

    1. Ishengoma DS
    2. Mandara CL
    3. Bakari C
    4. Fola AA
    5. Madebe R
    6. Seth MD
    7. Francis F
    8. Buguzi C
    9. Moshi R
    10. Garimo I
    11. Lazaro S
    12. Lusasi A
    13. Aaron S
    14. Chacky F
    15. Mohamed A
    16. Njau RJ
    17. Kitau J
    18. Rasmussen C
    19. Bailey JA
    20. Juliano JJ
    21. Warsame M

    (2024) Evidence of artemisinin partial resistance in North-Western Tanzania: clinical and drug resistance markers study

    medRxiv.

    https://doi.org/10.1101/2024.01.31.24301954

    • Google Scholar
67. 1. Jacob CG
    2. Thuy-Nhien N
    3. Mayxay M
    4. Maude RJ
    5. Quang HH
    6. Hongvanthong B
    7. Vanisaveth V
    8. Ngo Duc T
    9. Rekol H
    10. van der Pluijm R
    11. von Seidlein L
    12. Fairhurst R
    13. Nosten F
    14. Hossain MA
    15. Park N
    16. Goodwin S
    17. Ringwald P
    18. Chindavongsa K
    19. Newton P
    20. Ashley E
    21. Phalivong S
    22. Maude R
    23. Leang R
    24. Huch C
    25. Dong LT
    26. Nguyen K-T
    27. Nhat TM
    28. Hien TT
    29. Nguyen H
    30. Zdrojewski N
    31. Canavati S
    32. Sayeed AA
    33. Uddin D
    34. Buckee C
    35. Fanello CI
    36. Onyamboko M
    37. Peto T
    38. Tripura R
    39. Amaratunga C
    40. Myint Thu A
    41. Delmas G
    42. Landier J
    43. Parker DM
    44. Chau NH
    45. Lek D
    46. Suon S
    47. Callery J
    48. Jittamala P
    49. Hanboonkunupakarn B
    50. Pukrittayakamee S
    51. Phyo AP
    52. Smithuis F
    53. Lin K
    54. Thant M
    55. Hlaing TM
    56. Satpathi P
    57. Satpathi S
    58. Behera PK
    59. Tripura A
    60. Baidya S
    61. Valecha N
    62. Anvikar AR
    63. Ul Islam A
    64. Faiz A
    65. Kunasol C
    66. Drury E
    67. Kekre M
    68. Ali M
    69. Love K
    70. Rajatileka S
    71. Jeffreys AE
    72. Rowlands K
    73. Hubbart CS
    74. Dhorda M
    75. Vongpromek R
    76. Kotanan N
    77. Wongnak P
    78. Almagro Garcia J
    79. Pearson RD
    80. Ariani CV
    81. Chookajorn T
    82. Malangone C
    83. Nguyen T
    84. Stalker J
    85. Jeffery B
    86. Keatley J
    87. Johnson KJ
    88. Muddyman D
    89. Chan XHS
    90. Sillitoe J
    91. Amato R
    92. Simpson V
    93. Gonçalves S
    94. Rockett K
    95. Day NP
    96. Dondorp AM
    97. Kwiatkowski DP
    98. Miotto O

    (2021) Genetic surveillance in the Greater Mekong subregion and South Asia to support malaria control and elimination

    eLife 10:e62997.

    https://doi.org/10.7554/eLife.62997

    • PubMed
    • Google Scholar
68. 1. Jalei AA
    2. Na-Bangchang K
    3. Muhamad P
    4. Chaijaroenkul W

    (2023) Monitoring antimalarial drug-resistance markers in Somalia

    Parasites, Hosts and Diseases 61:78–83.

    https://doi.org/10.3347/PHD.22140

    • PubMed
    • Google Scholar
69. 1. Jeang B
    2. Zhong D
    3. Lee MC
    4. Atieli H
    5. Yewhalaw D
    6. Yan G

    (2024) Molecular surveillance of Kelch 13 polymorphisms in Plasmodium falciparum isolates from Kenya and Ethiopia

    Malaria Journal 23:36.

    https://doi.org/10.1186/s12936-023-04812-y

    • PubMed
    • Google Scholar
70. 1. Kagoro FM
    2. Barnes KI
    3. Marsh K
    4. Ekapirat N
    5. Mercado CEG
    6. Sinha I
    7. Humphreys G
    8. Dhorda M
    9. Guerin PJ
    10. Maude RJ

    (2022) Mapping genetic markers of artemisinin resistance in Plasmodium falciparum malaria in Asia: a systematic review and spatiotemporal analysis

    The Lancet. Microbe 3:e184–e192.

    https://doi.org/10.1016/S2666-5247(21)00249-4

    • PubMed
    • Google Scholar
71. 1. Kayiba NK
    2. Yobi DM
    3. Tshibangu-Kabamba E
    4. Tuan VP
    5. Yamaoka Y
    6. Devleesschauwer B
    7. Mvumbi DM
    8. Okitolonda Wemakoy E
    9. De Mol P
    10. Mvumbi GL
    11. Hayette M-P
    12. Rosas-Aguirre A
    13. Speybroeck N

    (2021) Spatial and molecular mapping of Pfkelch13 gene polymorphism in Africa in the era of emerging Plasmodium falciparum resistance to artemisinin: a systematic review

    The Lancet. Infectious Diseases 21:e82–e92.

    https://doi.org/10.1016/S1473-3099(20)30493-X

    • PubMed
    • Google Scholar
72. 1. Kobasa T
    2. Talundzic E
    3. Sug-aram R
    4. Boondat P
    5. Goldman IF
    6. Lucchi NW
    7. Dharmarak P
    8. Sintasath D
    9. Fukuda M
    10. Whistler T
    11. MacArthur J
    12. Udhayakumar V
    13. Prempree P
    14. Chinanonwait N

    (2018) Emergence and spread of kelch13 mutations associated with artemisinin resistance in Plasmodium falciparum parasites in 12 Thai Provinces from 2007 to 2016

    Antimicrobial Agents and Chemotherapy 62:e02141.

    https://doi.org/10.1128/AAC.02141-17

    • Google Scholar
73. 1. Kokori E
    2. Olatunji G
    3. Akinboade A
    4. Akinoso A
    5. Egbunu E
    6. Aremu SA
    7. Okafor CE
    8. Oluwole O
    9. Aderinto N

    (2024) Triple artemisinin-based combination therapy (TACT): advancing malaria control and eradication efforts

    Malaria Journal 23:25.

    https://doi.org/10.1186/s12936-024-04844-y

    • PubMed
    • Google Scholar
74. Preprint

    1. Lee C
    2. Ünlü ES
    3. White NFD
    4. Almagro-Garcia J
    5. Ariani C
    6. Pearson RD

    (2024) Pf-HaploAtlas: an interactive web app for spatiotemporal analysis of P. falciparum Genes.

    bioRxiv.

    https://doi.org/10.1101/2024.07.16.603783

    • Google Scholar
75. 1. Li EZ
    2. Nguyen TD
    3. Tran TNA
    4. Zupko RJ
    5. Boni MF

    (2024) Assessing emergence risk of double-resistant and triple-resistant genotypes of Plasmodium falciparum

    Nature Communications 15:e1390.

    https://doi.org/10.1038/s41467-024-45547-x

    • PubMed
    • Google Scholar
76. 1. Lubell Y
    2. Dondorp A
    3. Guérin PJ
    4. Drake T
    5. Meek S
    6. Ashley E
    7. Day NPJ
    8. White NJ
    9. White LJ

    (2014) Artemisinin resistance--modelling the potential human and economic costs

    Malaria Journal 13:e452.

    https://doi.org/10.1186/1475-2875-13-452

    • PubMed
    • Google Scholar
77. 1. Lyimo BM
    2. Popkin-Hall ZR
    3. Giesbrecht DJ
    4. Mandara CI
    5. Madebe RA
    6. Bakari C
    7. Pereus D
    8. Seth MD
    9. Ngamba RM
    10. Mbwambo RB
    11. MacInnis B
    12. Mbwambo D
    13. Garimo I
    14. Chacky F
    15. Aaron S
    16. Lusasi A
    17. Molteni F
    18. Njau R
    19. Cunningham JA
    20. Lazaro S
    21. Mohamed A
    22. Juliano JJ
    23. Bailey JA
    24. Ishengoma DS

    (2022) Potential opportunities and challenges of deploying next generation sequencing and CRISPR-Cas systems to support diagnostics and surveillance towards malaria control and elimination in Africa

    Frontiers in Cellular and Infection Microbiology 12:e757844.

    https://doi.org/10.3389/fcimb.2022.757844

    • PubMed
    • Google Scholar
78. 1. Manzoni G
    2. Try R
    3. Guintran JO
    4. Christiansen-Jucht C
    5. Jacoby E
    6. Sovannaroth S
    7. Zhang Z
    8. Banouvong V
    9. Shortus MS
    10. Reyburn R
    11. Chanthavisouk C
    12. Linn NYY
    13. Thapa B
    14. Khine SK
    15. Sudathip P
    16. Gopinath D
    17. Thieu NQ
    18. Ngon MS
    19. Cong DT
    20. Hui L
    21. Kelley J
    22. Valecha NNK
    23. Bustos MD
    24. Rasmussen C
    25. Tuseo L

    (2024) Progress towards malaria elimination in the Greater Mekong Subregion: perspectives from the World Health Organization

    Malaria Journal 23:64.

    https://doi.org/10.1186/s12936-024-04851-z

    • PubMed
    • Google Scholar
79. 1. Mathieu LC
    2. Cox H
    3. Early AM
    4. Mok S
    5. Lazrek Y
    6. Paquet J-C
    7. Ade M-P
    8. Lucchi NW
    9. Grant Q
    10. Udhayakumar V
    11. Alexandre JS
    12. Demar M
    13. Ringwald P
    14. Neafsey DE
    15. Fidock DA
    16. Musset L

    (2020) Local emergence in Amazonia of Plasmodium falciparum k13 C580Y mutants associated with in vitro artemisinin resistance

    eLife 9:e51015.

    https://doi.org/10.7554/eLife.51015

    • PubMed
    • Google Scholar
80. 1. Mayor A
    2. Ishengoma DS
    3. Proctor JL
    4. Verity R

    (2023) Sampling for malaria molecular surveillance

    Trends in Parasitology 39:954–968.

    https://doi.org/10.1016/j.pt.2023.08.007

    • PubMed
    • Google Scholar
81. Preprint

    1. Meier-Scherling CPG
    2. Watson OJ
    3. Asua V
    4. Ghinai I
    5. Katairo T
    6. Garg S
    7. Conrad M
    8. Rosenthal PJ
    9. Okell LC
    10. Bailey JA

    (2024) Selection of Artemisinin Partial Resistance Kelch13 Mutations in Uganda in 2016-22 Was at a Rate Comparable to That Seen Previously in South-East Asia

    medRxiv.

    https://doi.org/10.1101/2024.02.03.24302209

    • Google Scholar
82. 1. Mensah BA
    2. Akyea-Bobi NE
    3. Ghansah A

    (2022) Genomic approaches for monitoring transmission dynamics of malaria: A case for malaria molecular surveillance in Sub-Saharan Africa

    Frontiers in Epidemiology 2:e939291.

    https://doi.org/10.3389/fepid.2022.939291

    • PubMed
    • Google Scholar
83. 1. Mesén-Ramírez P
    2. Bergmann B
    3. Elhabiri M
    4. Zhu L
    5. von Thien H
    6. Castro-Peña C
    7. Gilberger T-W
    8. Davioud-Charvet E
    9. Bozdech Z
    10. Bachmann A
    11. Spielmann T

    (2021) The parasitophorous vacuole nutrient channel is critical for drug access in malaria parasites and modulates the artemisinin resistance fitness cost

    Cell Host & Microbe 29:1774–1787.

    https://doi.org/10.1016/j.chom.2021.11.002

    • PubMed
    • Google Scholar
84. 1. Mihreteab S
    2. Platon L
    3. Berhane A
    4. Stokes BH
    5. Warsame M
    6. Campagne P
    7. Criscuolo A
    8. Ma L
    9. Petiot N
    10. Doderer-Lang C
    11. Legrand E
    12. Ward KE
    13. Zehaie Kassahun A
    14. Ringwald P
    15. Fidock DA
    16. Ménard D

    (2023) Increasing prevalence of artemisinin-resistant HRP2-negative malaria in eritrea

    New England Journal of Medicine 389:1191–1202.

    https://doi.org/10.1056/NEJMoa2210956

    • Google Scholar
85. 1. Miotto O
    2. Almagro-Garcia J
    3. Manske M
    4. Macinnis B
    5. Campino S
    6. Rockett KA
    7. Amaratunga C
    8. Lim P
    9. Suon S
    10. Sreng S
    11. Anderson JM
    12. Duong S
    13. Nguon C
    14. Chuor CM
    15. Saunders D
    16. Se Y
    17. Lon C
    18. Fukuda MM
    19. Amenga-Etego L
    20. Hodgson AVO
    21. Asoala V
    22. Imwong M
    23. Takala-Harrison S
    24. Nosten F
    25. Su X-Z
    26. Ringwald P
    27. Ariey F
    28. Dolecek C
    29. Hien TT
    30. Boni MF
    31. Thai CQ
    32. Amambua-Ngwa A
    33. Conway DJ
    34. Djimdé AA
    35. Doumbo OK
    36. Zongo I
    37. Ouedraogo J-B
    38. Alcock D
    39. Drury E
    40. Auburn S
    41. Koch O
    42. Sanders M
    43. Hubbart C
    44. Maslen G
    45. Ruano-Rubio V
    46. Jyothi D
    47. Miles A
    48. O’Brien J
    49. Gamble C
    50. Oyola SO
    51. Rayner JC
    52. Newbold CI
    53. Berriman M
    54. Spencer CCA
    55. McVean G
    56. Day NP
    57. White NJ
    58. Bethell D
    59. Dondorp AM
    60. Plowe CV
    61. Fairhurst RM
    62. Kwiatkowski DP

    (2013) Multiple populations of artemisinin-resistant Plasmodium falciparum in Cambodia

    Nature Genetics 45:648–655.

    https://doi.org/10.1038/ng.2624

    • PubMed
    • Google Scholar
86. 1. Miotto O
    2. Amato R
    3. Ashley EA
    4. MacInnis B
    5. Almagro-Garcia J
    6. Amaratunga C
    7. Lim P
    8. Mead D
    9. Oyola SO
    10. Dhorda M
    11. Imwong M
    12. Woodrow C
    13. Manske M
    14. Stalker J
    15. Drury E
    16. Campino S
    17. Amenga-Etego L
    18. Thanh T-NN
    19. Tran HT
    20. Ringwald P
    21. Bethell D
    22. Nosten F
    23. Phyo AP
    24. Pukrittayakamee S
    25. Chotivanich K
    26. Chuor CM
    27. Nguon C
    28. Suon S
    29. Sreng S
    30. Newton PN
    31. Mayxay M
    32. Khanthavong M
    33. Hongvanthong B
    34. Htut Y
    35. Han KT
    36. Kyaw MP
    37. Faiz MA
    38. Fanello CI
    39. Onyamboko M
    40. Mokuolu OA
    41. Jacob CG
    42. Takala-Harrison S
    43. Plowe CV
    44. Day NP
    45. Dondorp AM
    46. Spencer CCA
    47. McVean G
    48. Fairhurst RM
    49. White NJ
    50. Kwiatkowski DP

    (2015) Genetic architecture of artemisinin-resistant Plasmodium falciparum

    Nature Genetics 47:226–234.

    https://doi.org/10.1038/ng.3189

    • PubMed
    • Google Scholar
87. Preprint

    1. Miotto O
    2. Amambua-Ngwa A
    3. Amenga-Etego L
    4. Abdel Hamid MM
    5. Adam I
    6. Aninagyei E
    7. Apinjoh T
    8. Awandare GA
    9. Bejon P
    10. Bertin GI
    11. Bouyou-Akotet M
    12. Claessens A
    13. Conway DJ
    14. D’Alessandro U
    15. Diakite M
    16. Djimdé A
    17. Dondorp AM
    18. Duffy P
    19. Fairhurst RM
    20. Fanello CI
    21. Ghansah A
    22. Ishengoma D
    23. Lawniczak M
    24. Maïga-Ascofaré O
    25. Auburn S
    26. Rosanas-Urgell A
    27. Wasakul V
    28. White NF
    29. Almagro-Garcia J
    30. Pearson RD
    31. Goncalves S
    32. Ariani C
    33. Bozdech Z
    34. Hamilton W
    35. Simpson V
    36. Kwiatkowski DP

    (2024) A Complex Plasmodium falciparum Cryptotype Circulating at Low Frequency across the African Continent

    bioRxiv.

    https://doi.org/10.1101/2024.01.20.576496

    • Google Scholar
88. 1. Mishra S
    2. Bharti PK
    3. Shukla MM
    4. Ali NA
    5. Kashyotia SS
    6. Kumar A
    7. Dhariwal AC
    8. Singh N

    (2017) Clinical and molecular monitoring of Plasmodium falciparum resistance to antimalarial drug (artesunate+sulphadoxine-pyrimethamine) in two highly malarious district of Madhya Pradesh, Central India from 2012–2014

    Pathogens and Global Health 111:186–194.

    https://doi.org/10.1080/20477724.2017.1331875

    • Google Scholar
89. 1. Mita T
    2. Tanabe K
    3. Kita K

    (2009) Spread and evolution of Plasmodium falciparum drug resistance

    Parasitology International 58:201–209.

    https://doi.org/10.1016/j.parint.2009.04.004

    • PubMed
    • Google Scholar
90. 1. Mohamed AO
    2. Hussien M
    3. Mohamed A
    4. Suliman A
    5. Elkando NS
    6. Abdelbagi H
    7. Malik EM
    8. Abdelraheem MH
    9. Hamid MMA

    (2020) Assessment of Plasmodium falciparum drug resistance molecular markers from the Blue Nile State, Southeast Sudan

    Malaria Journal 19:78.

    https://doi.org/10.1186/s12936-020-03165-0

    • PubMed
    • Google Scholar
91. 1. Mok S
    2. Ashley EA
    3. Ferreira PE
    4. Zhu L
    5. Lin Z
    6. Yeo T
    7. Chotivanich K
    8. Imwong M
    9. Pukrittayakamee S
    10. Dhorda M
    11. Nguon C
    12. Lim P
    13. Amaratunga C
    14. Suon S
    15. Hien TT
    16. Htut Y
    17. Faiz MA
    18. Onyamboko MA
    19. Mayxay M
    20. Newton PN
    21. Tripura R
    22. Woodrow CJ
    23. Miotto O
    24. Kwiatkowski DP
    25. Nosten F
    26. Day NPJ
    27. Preiser PR
    28. White NJ
    29. Dondorp AM
    30. Fairhurst RM
    31. Bozdech Z

    (2015) Population transcriptomics of human malaria parasites reveals the mechanism of artemisinin resistance

    Science 347:431–435.

    https://doi.org/10.1126/science.1260403

    • PubMed
    • Google Scholar
92. 1. Moriarty LF
    2. Nkoli PM
    3. Likwela JL
    4. Mulopo PM
    5. Sompwe EM
    6. Rika JM
    7. Mavoko HM
    8. Svigel SS
    9. Jones S
    10. Ntamabyaliro NY
    11. Kaputu AK
    12. Lucchi N
    13. Subramaniam G
    14. Niang M
    15. Sadou A
    16. Ngoyi DM
    17. Muyembe Tamfum JJ
    18. Schmedes SE
    19. Plucinski MM
    20. Chowell-Puente G
    21. Halsey ES
    22. Kahunu GM

    (2021) Therapeutic efficacy of artemisinin-based combination therapies in democratic republic of the congo and investigation of molecular markers of antimalarial resistance

    The American Journal of Tropical Medicine and Hygiene 105:1067–1075.

    https://doi.org/10.4269/ajtmh.21-0214

    • Google Scholar
93. 1. Mukherjee A
    2. Bopp S
    3. Magistrado P
    4. Wong W
    5. Daniels R
    6. Demas A
    7. Schaffner S
    8. Amaratunga C
    9. Lim P
    10. Dhorda M
    11. Miotto O
    12. Woodrow C
    13. Ashley EA
    14. Dondorp AM
    15. White NJ
    16. Wirth D
    17. Fairhurst R
    18. Volkman SK

    (2017) Artemisinin resistance without pfkelch13 mutations in Plasmodium falciparum isolates from Cambodia

    Malaria Journal 16:195.

    https://doi.org/10.1186/s12936-017-1845-5

    • PubMed
    • Google Scholar
94. 1. Murray CJ
    2. Rosenfeld LC
    3. Lim SS
    4. Andrews KG
    5. Foreman KJ
    6. Haring D
    7. Fullman N
    8. Naghavi M
    9. Lozano R
    10. Lopez AD

    (2012) Global malaria mortality between 1980 and 2010: a systematic analysis

    The Lancet 379:413–431.

    https://doi.org/10.1016/S0140-6736(12)60034-8

    • Google Scholar
95. 1. Myers-Hansen JL
    2. Abuaku B
    3. Oyebola MK
    4. Mensah BA
    5. Ahorlu C
    6. Wilson MD
    7. Awandare G
    8. Koram KA
    9. Ngwa AA
    10. Ghansah A

    (2020) Assessment of antimalarial drug resistant markers in asymptomatic Plasmodium falciparum infections after 4 years of indoor residual spraying in Northern Ghana

    PLOS ONE 15:e0233478.

    https://doi.org/10.1371/journal.pone.0233478

    • PubMed
    • Google Scholar
96. 1. Nair S
    2. Li X
    3. Arya GA
    4. McDew-White M
    5. Ferrari M
    6. Nosten F
    7. Anderson TJC

    (2018) Fitness costs and the rapid spread of kelch13-C580Y substitutions conferring artemisinin resistance

    Antimicrobial Agents and Chemotherapy 62:00605–00618.

    https://doi.org/10.1128/AAC.00605-18

    • PubMed
    • Google Scholar
97. 1. Nayak S
    2. Peto TJ
    3. Kucharski M
    4. Tripura R
    5. Callery JJ
    6. Quang Huy DT
    7. Gendrot M
    8. Lek D
    9. Nghia HDT
    10. van der Pluijm RW
    11. Dong N
    12. Long LT
    13. Vongpromek R
    14. Rekol H
    15. Hoang Chau N
    16. Miotto O
    17. Mukaka M
    18. Dhorda M
    19. von Seidlein L
    20. Imwong M
    21. Roca X
    22. Day NPJ
    23. White NJ
    24. Dondorp AM
    25. Bozdech Z

    (2024) Population genomics and transcriptomics of Plasmodium falciparum in Cambodia and Vietnam uncover key components of the artemisinin resistance genetic background

    Nature Communications 15:10625.

    https://doi.org/10.1038/s41467-024-54915-6

    • PubMed
    • Google Scholar
98. 1. Ndiaye YD
    2. Hartl DL
    3. McGregor D
    4. Badiane A
    5. Fall FB
    6. Daniels RF
    7. Wirth DF
    8. Ndiaye D
    9. Volkman SK

    (2021) Genetic surveillance for monitoring the impact of drug use on Plasmodium falciparum populations

    International Journal for Parasitology. Drugs and Drug Resistance 17:12–22.

    https://doi.org/10.1016/j.ijpddr.2021.07.004

    • PubMed
    • Google Scholar
99. 1. Neafsey DE
    2. Taylor AR
    3. MacInnis BL

    (2021) Advances and opportunities in malaria population genomics

    Nature Reviews. Genetics 22:502–517.

    https://doi.org/10.1038/s41576-021-00349-5

    • PubMed
    • Google Scholar
100. 1. Newton PN
     2. McGready R
     3. Fernandez F
     4. Green MD
     5. Sunjio M
     6. Bruneton C
     7. Phanouvong S
     8. Millet P
     9. Whitty CJM
     10. Talisuna AO
     11. Proux S
     12. Christophel EM
     13. Malenga G
     14. Singhasivanon P
     15. Bojang K
     16. Kaur H
     17. Palmer K
     18. Day NPJ
     19. Greenwood BM
     20. Nosten F
     21. White NJ

     (2006) Manslaughter by fake artesunate in Asia--will Africa be next?

     PLOS Medicine 3:e197.

     https://doi.org/10.1371/journal.pmed.0030197

     • PubMed
     • Google Scholar
101. 1. Nguyen TD
     2. Olliaro P
     3. Dondorp AM
     4. Baird JK
     5. Lam HM
     6. Farrar J
     7. Thwaites GE
     8. White NJ
     9. Boni MF

     (2015) Optimum population-level use of artemisinin combination therapies: a modelling study

     The Lancet. Global Health 3:e758.

     https://doi.org/10.1016/S2214-109X(15)00162-X

     • PubMed
     • Google Scholar
102. 1. Nguyen TD
     2. Gao B
     3. Amaratunga C
     4. Dhorda M
     5. Tran TN-A
     6. White NJ
     7. Dondorp AM
     8. Boni MF
     9. Aguas R

     (2023a) Preventing antimalarial drug resistance with triple artemisinin-based combination therapies

     Nature Communications 14:4568.

     https://doi.org/10.1038/s41467-023-39914-3

     • PubMed
     • Google Scholar
103. 1. Nguyen TD
     2. Tran TNA
     3. Parker DM
     4. White NJ
     5. Boni MF

     (2023b) Antimalarial mass drug administration in large populations and the evolution of drug resistance

     PLOS Global Public Health 3:e0002200.

     https://doi.org/10.1371/journal.pgph.0002200

     • PubMed
     • Google Scholar
104. 1. Noedl H
     2. Se Y
     3. Schaecher K
     4. Smith BL
     5. Socheat D
     6. Fukuda MM
     7. Artemisinin Resistance in Cambodia 1 Study Consortium

     (2008) Evidence of artemisinin-resistant malaria in western Cambodia

     The New England Journal of Medicine 359:2619–2620.

     https://doi.org/10.1056/NEJMc0805011

     • PubMed
     • Google Scholar
105. 1. Nosten F
     2. White NJ

     (2007) Artemisinin-based combination treatment of falciparum malaria

     The American Journal of Tropical Medicine and Hygiene 77:181–192.

     https://doi.org/10.4269/ajtmh.2007.77.181

     • PubMed
     • Google Scholar
106. 1. Nsanzabana C
     2. Djalle D
     3. Guérin PJ
     4. Ménard D
     5. González IJ

     (2018) Tools for surveillance of anti-malarial drug resistance: an assessment of the current landscape

     Malaria Journal 17:e75.

     https://doi.org/10.1186/s12936-018-2185-9

     • PubMed
     • Google Scholar
107. 1. Nsanzabana C

     (2021) Time to scale up molecular surveillance for anti-malarial drug resistance in sub-saharan Africa

     Malaria Journal 20:e401.

     https://doi.org/10.1186/s12936-021-03942-5

     • PubMed
     • Google Scholar
108. 1. Ouattara A
     2. Kone A
     3. Adams M
     4. Fofana B
     5. Maiga AW
     6. Hampton S
     7. Coulibaly D
     8. Thera MA
     9. Diallo N
     10. Dara A
     11. Sagara I
     12. Gil JP
     13. Bjorkman A
     14. Takala-Harrison S
     15. Doumbo OK
     16. Plowe CV
     17. Djimde AA

     (2015) Polymorphisms in the K13-propeller gene in artemisinin-susceptible Plasmodium falciparum parasites from Bougoula-Hameau and Bandiagara, Mali

     The American Journal of Tropical Medicine and Hygiene 92:1202–1206.

     https://doi.org/10.4269/ajtmh.14-0605

     • PubMed
     • Google Scholar
109. 1. Owoloye A
     2. Olufemi M
     3. Idowu ET
     4. Oyebola KM

     (2021) Prevalence of potential mediators of artemisinin resistance in African isolates of Plasmodium falciparum

     Malaria Journal 20:451.

     https://doi.org/10.1186/s12936-021-03987-6

     • PubMed
     • Google Scholar
110. 1. Pandit K
     2. Surolia N
     3. Bhattacharjee S
     4. Karmodiya K

     (2023) The many paths to artemisinin resistance in Plasmodium falciparum

     Trends in Parasitology 39:1060–1073.

     https://doi.org/10.1016/j.pt.2023.09.011

     • PubMed
     • Google Scholar
111. 1. Parobek CM
     2. Parr JB
     3. Brazeau NF
     4. Lon C
     5. Chaorattanakawee S
     6. Gosi P
     7. Barnett EJ
     8. Norris LD
     9. Meshnick SR
     10. Spring MD
     11. Lanteri CA
     12. Bailey JA
     13. Saunders DL
     14. Lin JT
     15. Juliano JJ

     (2017) PArtner-drug resistance and population substructuring of artemisinin-resistant Plasmodium falciparum in Cambodia

     Genome Biology and Evolution 9:1673–1686.

     https://doi.org/10.1093/gbe/evx126

     • PubMed
     • Google Scholar
112. 1. Peto TJ
     2. Tripura R
     3. Callery JJ
     4. Lek D
     5. Nghia HDT
     6. Nguon C
     7. Thuong NTH
     8. van der Pluijm RW
     9. Dung NTP
     10. Sokha M
     11. Van Luong V
     12. Long LT
     13. Sovann Y
     14. Duanguppama J
     15. Waithira N
     16. Hoglund RM
     17. Chotsiri P
     18. Chau NH
     19. Ruecker A
     20. Amaratunga C
     21. Dhorda M
     22. Miotto O
     23. Maude RJ
     24. Rekol H
     25. Chotivanich K
     26. Tarning J
     27. von Seidlein L
     28. Imwong M
     29. Mukaka M
     30. Day NPJ
     31. Hien TT
     32. White NJ
     33. Dondorp AM

     (2022) Triple therapy with artemether-lumefantrine plus amodiaquine versus artemether-lumefantrine alone for artemisinin-resistant, uncomplicated falciparum malaria: an open-label, randomised, multicentre trial

     The Lancet. Infectious Diseases 22:867–878.

     https://doi.org/10.1016/S1473-3099(21)00692-7

     • PubMed
     • Google Scholar
113. 1. Pongtavornpinyo W
     2. Yeung S
     3. Hastings IM
     4. Dondorp AM
     5. Day NPJ
     6. White NJ

     (2008) Spread of anti-malarial drug resistance: mathematical model with implications for ACT drug policies

     Malaria Journal 7:e229.

     https://doi.org/10.1186/1475-2875-7-229

     • PubMed
     • Google Scholar
114. 1. Rasmussen C
     2. Alonso P
     3. Ringwald P

     (2022) Current and emerging strategies to combat antimalarial resistance

     Expert Review of Anti-Infective Therapy 20:353–372.

     https://doi.org/10.1080/14787210.2021.1962291

     • PubMed
     • Google Scholar
115. Preprint

     1. Redding DW
     2. Gibb R
     3. Jones KE

     (2024) Ecological impacts of climate change will transform public health priorities for zoonotic and vector-borne disease

     medRxiv.

     https://doi.org/10.1101/2024.02.09.24302575

     • Google Scholar
116. 1. Rocamora F
     2. Winzeler EA

     (2020) Genomic approaches to drug resistance in malaria

     Annual Review of Microbiology 74:761–786.

     https://doi.org/10.1146/annurev-micro-012220-064343

     • PubMed
     • Google Scholar
117. 1. Rodó X
     2. Martinez PP
     3. Siraj A
     4. Pascual M

     (2021) Malaria trends in Ethiopian highlands track the 2000 “slowdown” in global warming

     Nature Communications 12:1555.

     https://doi.org/10.1038/s41467-021-21815-y

     • PubMed
     • Google Scholar
118. 1. Rosenthal PJ

     (2013) The interplay between drug resistance and fitness in malaria parasites

     Molecular Microbiology 89:1025–1038.

     https://doi.org/10.1111/mmi.12349

     • PubMed
     • Google Scholar
119. 1. Rosenthal PJ
     2. John CC
     3. Rabinovich NR

     (2019) Malaria: how are we doing and how can we do better?

     The American Journal of Tropical Medicine and Hygiene 100:239–241.

     https://doi.org/10.4269/ajtmh.18-0997

     • PubMed
     • Google Scholar
120. 1. Rosenthal PJ

     (2021) Has artemisinin resistance emerged in Africa?

     The Lancet Infectious Diseases 21:1056–1057.

     https://doi.org/10.1016/S1473-3099(21)00168-7

     • Google Scholar
121. 1. Rosenthal PJ
     2. Asua V
     3. Conrad MD

     (2024) Emergence, transmission dynamics and mechanisms of artemisinin partial resistance in malaria parasites in Africa

     Nature Reviews Microbiology 22:373–384.

     https://doi.org/10.1038/s41579-024-01008-2

     • Google Scholar
122. 1. Ross LS
     2. Dhingra SK
     3. Mok S
     4. Yeo T
     5. Wicht KJ
     6. Kümpornsin K
     7. Takala-Harrison S
     8. Witkowski B
     9. Fairhurst RM
     10. Ariey F
     11. Menard D
     12. Fidock DA

     (2018) Emerging Southeast Asian PfCRT mutations confer Plasmodium falciparum resistance to the first-line antimalarial piperaquine

     Nature Communications 9:e3314.

     https://doi.org/10.1038/s41467-018-05652-0

     • Google Scholar
123. 1. Rossi G
     2. De Smet M
     3. Khim N
     4. Kindermans J-M
     5. Menard D

     (2017) Emergence of Plasmodium falciparum triple mutant in Cambodia

     The Lancet. Infectious Diseases 17:e1233.

     https://doi.org/10.1016/S1473-3099(17)30635-7

     • PubMed
     • Google Scholar
124. 1. Roux AT
     2. Maharaj L
     3. Oyegoke O
     4. Akoniyon OP
     5. Adeleke MA
     6. Maharaj R
     7. Okpeku M

     (2021) Chloroquine and sulfadoxine-pyrimethamine resistance in sub-Saharan Africa-a review

     Frontiers in Genetics 12:668574.

     https://doi.org/10.3389/fgene.2021.668574

     • PubMed
     • Google Scholar
125. 1. Ryan SJ
     2. Lippi CA
     3. Zermoglio F

     (2020) Shifting transmission risk for malaria in Africa with climate change: a framework for planning and intervention

     Malaria Journal 19:170.

     https://doi.org/10.1186/s12936-020-03224-6

     • Google Scholar
126. 1. Satimai W
     2. Sudathip P
     3. Vijaykadga S
     4. Khamsiriwatchara A
     5. Sawang S
     6. Potithavoranan T
     7. Sangvichean A
     8. Delacollette C
     9. Singhasivanon P
     10. Kaewkungwal J
     11. Lawpoolsri S

     (2012) Artemisinin resistance containment project in Thailand II: responses to mefloquine-artesunate combination therapy among falciparum malaria patients in provinces bordering Cambodia

     Malaria Journal 11:300.

     https://doi.org/10.1186/1475-2875-11-300

     • PubMed
     • Google Scholar
127. 1. Scott N
     2. Ataide R
     3. Wilson DP
     4. Hellard M
     5. Price RN
     6. Simpson JA
     7. Fowkes FJI

     (2018) Implications of population-level immunity for the emergence of artemisinin-resistant malaria: a mathematical model

     Malaria Journal 17:279.

     https://doi.org/10.1186/s12936-018-2418-y

     • PubMed
     • Google Scholar
128. 1. Sherrard-Smith E
     2. Ngufor C
     3. Sanou A
     4. Guelbeogo MW
     5. N’Guessan R
     6. Elobolobo E
     7. Saute F
     8. Varela K
     9. Chaccour CJ
     10. Zulliger R
     11. Wagman J
     12. Robertson ML
     13. Rowland M
     14. Donnelly MJ
     15. Gonahasa S
     16. Staedke SG
     17. Kolaczinski J
     18. Churcher TS

     (2022) Inferring the epidemiological benefit of indoor vector control interventions against malaria from mosquito data

     Nature Communications 13:e3862.

     https://doi.org/10.1038/s41467-022-30700-1

     • Google Scholar
129. 1. Sibley CH
     2. Barnes KI
     3. Plowe CV

     (2007) The rationale and plan for creating a World Antimalarial Resistance Network (WARN)

     Malaria Journal 6:e118.

     https://doi.org/10.1186/1475-2875-6-118

     • Google Scholar
130. 1. Siddiqui FA
     2. Liang X
     3. Cui L

     (2021) Plasmodium falciparum resistance to ACTs: Emergence, mechanisms, and outlook

     International Journal for Parasitology. Drugs and Drug Resistance 16:102–118.

     https://doi.org/10.1016/j.ijpddr.2021.05.007

     • PubMed
     • Google Scholar
131. 1. Sinka ME
     2. Bangs MJ
     3. Manguin S
     4. Coetzee M
     5. Mbogo CM
     6. Hemingway J
     7. Patil AP
     8. Temperley WH
     9. Gething PW
     10. Kabaria CW
     11. Okara RM
     12. Van Boeckel T
     13. Godfray HCJ
     14. Harbach RE
     15. Hay SI

     (2010) The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis

     Parasites & Vectors 3:117.

     https://doi.org/10.1186/1756-3305-3-117

     • PubMed
     • Google Scholar
132. 1. Slater HC
     2. Griffin JT
     3. Ghani AC
     4. Okell LC

     (2016) Assessing the potential impact of artemisinin and partner drug resistance in sub-Saharan Africa

     Malaria Journal 15:10.

     https://doi.org/10.1186/s12936-015-1075-7

     • PubMed
     • Google Scholar
133. 1. Smith DL
     2. Klein EY
     3. McKenzie FE
     4. Laxminarayan R

     (2010) Prospective strategies to delay the evolution of anti-malarial drug resistance: weighing the uncertainty

     Malaria Journal 9:217.

     https://doi.org/10.1186/1475-2875-9-217

     • Google Scholar
134. 1. Stokes BH
     2. Dhingra SK
     3. Rubiano K
     4. Mok S
     5. Straimer J
     6. Gnädig NF
     7. Deni I
     8. Schindler KA
     9. Bath JR
     10. Ward KE
     11. Striepen J
     12. Yeo T
     13. Ross LS
     14. Legrand E
     15. Ariey F
     16. Cunningham CH
     17. Souleymane IM
     18. Gansané A
     19. Nzoumbou-Boko R
     20. Ndayikunda C
     21. Kabanywanyi AM
     22. Uwimana A
     23. Smith SJ
     24. Kolley O
     25. Ndounga M
     26. Warsame M
     27. Leang R
     28. Nosten F
     29. Anderson TJ
     30. Rosenthal PJ
     31. Ménard D
     32. Fidock DA

     (2021) Plasmodium falciparum K13 mutations in Africa and Asia impact artemisinin resistance and parasite fitness

     eLife 10:e66277.

     https://doi.org/10.7554/eLife.66277

     • PubMed
     • Google Scholar
135. 1. Stokes BH
     2. Ward KE
     3. Fidock DA

     (2022) Evidence of artemisinin-resistant malaria in Africa

     The New England Journal of Medicine 386:1385–1386.

     https://doi.org/10.1056/NEJMc2117480

     • PubMed
     • Google Scholar
136. 1. Straimer J
     2. Gnädig NF
     3. Witkowski B
     4. Amaratunga C
     5. Duru V
     6. Ramadani AP
     7. Dacheux M
     8. Khim N
     9. Zhang L
     10. Lam S
     11. Gregory PD
     12. Urnov FD
     13. Mercereau-Puijalon O
     14. Benoit-Vical F
     15. Fairhurst RM
     16. Ménard D
     17. Fidock DA

     (2015) Drug resistance: K13-propeller mutations confer artemisinin resistance in Plasmodium falciparum clinical isolates

     Science 347:428–431.

     https://doi.org/10.1126/science.1260867

     • PubMed
     • Google Scholar
137. 1. Straimer J
     2. Gnädig NF
     3. Stokes BH
     4. Ehrenberger M
     5. Crane AA
     6. Fidock DA

     (2017) Plasmodium falciparum K13 mutations differentially impact ozonide susceptibility and parasite fitness in vitro

     mBio 8:e17.

     https://doi.org/10.1128/mBio.00172-17

     • Google Scholar
138. 1. Straimer J
     2. Gandhi P
     3. Renner KC
     4. Schmitt EK

     (2022) High Prevalence of Plasmodium falciparum K13 Mutations in Rwanda Is associated with slow parasite clearance after treatment with Artemether-Lumefantrine

     The Journal of Infectious Diseases 225:1411–1414.

     https://doi.org/10.1093/infdis/jiab352

     • Google Scholar
139. 1. Takala-Harrison S
     2. Jacob CG
     3. Arze C
     4. Cummings MP
     5. Silva JC
     6. Dondorp AM
     7. Fukuda MM
     8. Hien TT
     9. Mayxay M
     10. Noedl H
     11. Nosten F
     12. Kyaw MP
     13. Nhien NTT
     14. Imwong M
     15. Bethell D
     16. Se Y
     17. Lon C
     18. Tyner SD
     19. Saunders DL
     20. Ariey F
     21. Mercereau-Puijalon O
     22. Menard D
     23. Newton PN
     24. Khanthavong M
     25. Hongvanthong B
     26. Starzengruber P
     27. Fuehrer H-P
     28. Swoboda P
     29. Khan WA
     30. Phyo AP
     31. Nyunt MM
     32. Nyunt MH
     33. Brown TS
     34. Adams M
     35. Pepin CS
     36. Bailey J
     37. Tan JC
     38. Ferdig MT
     39. Clark TG
     40. Miotto O
     41. MacInnis B
     42. Kwiatkowski DP
     43. White NJ
     44. Ringwald P
     45. Plowe CV

     (2015) Independent emergence of artemisinin resistance mutations among Plasmodium falciparum in Southeast Asia

     The Journal of Infectious Diseases 211:670–679.

     https://doi.org/10.1093/infdis/jiu491

     • PubMed
     • Google Scholar
140. 1. Taylor SM
     2. Parobek CM
     3. DeConti DK
     4. Kayentao K
     5. Coulibaly SO
     6. Greenwood BM
     7. Tagbor H
     8. Williams J
     9. Bojang K
     10. Njie F
     11. Desai M
     12. Kariuki S
     13. Gutman J
     14. Mathanga DP
     15. Mårtensson A
     16. Ngasala B
     17. Conrad MD
     18. Rosenthal PJ
     19. Tshefu AK
     20. Moormann AM
     21. Vulule JM
     22. Doumbo OK
     23. Ter Kuile FO
     24. Meshnick SR
     25. Bailey JA
     26. Juliano JJ

     (2015) Absence of putative artemisinin resistance mutations among Plasmodium falciparum in Sub-Saharan Africa: a molecular epidemiologic study

     The Journal of Infectious Diseases 211:680–688.

     https://doi.org/10.1093/infdis/jiu467

     • PubMed
     • Google Scholar
141. 1. Tindana P
     2. de Haan F
     3. Amaratunga C
     4. Dhorda M
     5. van der Pluijm RW
     6. Dondorp AM
     7. Cheah PY

     (2021) Deploying triple artemisinin-based combination therapy (TACT) for malaria treatment in Africa: ethical and practical considerations

     Malaria Journal 20:119.

     https://doi.org/10.1186/s12936-021-03649-7

     • PubMed
     • Google Scholar
142. Book

     1. Trape JF

     (2001)

     The intolerable burden of malaria: a new look at the numbers

     In: Andréa Egan E, editors. The Intolerable Burden of Malaria: A New Look at the Numbers: Supplement to Volume 64 of the American Journal of Tropical Medicine and Hygiene. American Society of Tropical Medicine and Hygiene. pp. 121–126.

     • Google Scholar
143. 1. Tumwebaze PK
     2. Conrad MD
     3. Okitwi M
     4. Orena S
     5. Byaruhanga O
     6. Katairo T
     7. Legac J
     8. Garg S
     9. Giesbrecht D
     10. Smith SR
     11. Ceja FG
     12. Nsobya SL
     13. Bailey JA
     14. Cooper RA
     15. Rosenthal PJ

     (2022) Decreased susceptibility of Plasmodium falciparum to both dihydroartemisinin and lumefantrine in northern Uganda

     Nature Communications 13:6353.

     https://doi.org/10.1038/s41467-022-33873-x

     • PubMed
     • Google Scholar
144. 1. Tun KM
     2. Imwong M
     3. Lwin KM
     4. Win AA
     5. Hlaing TM
     6. Hlaing T
     7. Lin K
     8. Kyaw MP
     9. Plewes K
     10. Faiz MA
     11. Dhorda M
     12. Cheah PY
     13. Pukrittayakamee S
     14. Ashley EA
     15. Anderson TJC
     16. Nair S
     17. McDew-White M
     18. Flegg JA
     19. Grist EPM
     20. Guerin P
     21. Maude RJ
     22. Smithuis F
     23. Dondorp AM
     24. Day NPJ
     25. Nosten F
     26. White NJ
     27. Woodrow CJ

     (2015) Spread of artemisinin-resistant Plasmodium falciparum in Myanmar: a cross-sectional survey of the K13 molecular marker

     The Lancet. Infectious Diseases 15:415–421.

     https://doi.org/10.1016/S1473-3099(15)70032-0

     • PubMed
     • Google Scholar
145. 1. Tun STT
     2. von Seidlein L
     3. Pongvongsa T
     4. Mayxay M
     5. Saralamba S
     6. Kyaw SS
     7. Chanthavilay P
     8. Celhay O
     9. Nguyen TD
     10. Tran TN-A
     11. Parker DM
     12. Boni MF
     13. Dondorp AM
     14. White LJ

     (2017) Towards malaria elimination in Savannakhet, Lao PDR: mathematical modelling driven strategy design

     Malaria Journal 16:483.

     https://doi.org/10.1186/s12936-017-2130-3

     • PubMed
     • Google Scholar
146. 1. Tun KM
     2. Jeeyapant A
     3. Myint AH
     4. Kyaw ZT
     5. Dhorda M
     6. Mukaka M
     7. Cheah PY
     8. Imwong M
     9. Hlaing T
     10. Kyaw TH
     11. Ashley EA
     12. Dondorp A
     13. White NJ
     14. Day NPJ
     15. Smithuis F

     (2018) Effectiveness and safety of 3 and 5 day courses of artemether-lumefantrine for the treatment of uncomplicated falciparum malaria in an area of emerging artemisinin resistance in Myanmar

     Malaria Journal 17:258.

     https://doi.org/10.1186/s12936-018-2404-4

     • PubMed
     • Google Scholar
147. 1. Uwimana A
     2. Legrand E
     3. Stokes BH
     4. Ndikumana J-LM
     5. Warsame M
     6. Umulisa N
     7. Ngamije D
     8. Munyaneza T
     9. Mazarati J-B
     10. Munguti K
     11. Campagne P
     12. Criscuolo A
     13. Ariey F
     14. Murindahabi M
     15. Ringwald P
     16. Fidock DA
     17. Mbituyumuremyi A
     18. Menard D

     (2020) Emergence and clonal expansion of in vitro artemisinin-resistant Plasmodium falciparum kelch13 R561H mutant parasites in Rwanda

     Nature Medicine 26:1602–1608.

     https://doi.org/10.1038/s41591-020-1005-2

     • PubMed
     • Google Scholar
148. 1. Uwimana A
     2. Umulisa N
     3. Venkatesan M
     4. Svigel SS
     5. Zhou Z
     6. Munyaneza T
     7. Habimana RM
     8. Rucogoza A
     9. Moriarty LF
     10. Sandford R
     11. Piercefield E
     12. Goldman I
     13. Ezema B
     14. Talundzic E
     15. Pacheco MA
     16. Escalante AA
     17. Ngamije D
     18. Mangala J-LN
     19. Kabera M
     20. Munguti K
     21. Murindahabi M
     22. Brieger W
     23. Musanabaganwa C
     24. Mutesa L
     25. Udhayakumar V
     26. Mbituyumuremyi A
     27. Halsey ES
     28. Lucchi NW

     (2021) Association of Plasmodium falciparum kelch13 R561H genotypes with delayed parasite clearance in Rwanda: an open-label, single-arm, multicentre, therapeutic efficacy study

     The Lancet. Infectious Diseases 21:1120–1128.

     https://doi.org/10.1016/S1473-3099(21)00142-0

     • PubMed
     • Google Scholar
149. 1. van der Pluijm RW
     2. Tripura R
     3. Hoglund RM
     4. Pyae Phyo A
     5. Lek D
     6. ul Islam A
     7. Anvikar AR
     8. Satpathi P
     9. Satpathi S
     10. Behera PK
     11. Tripura A
     12. Baidya S
     13. Onyamboko M
     14. Chau NH
     15. Sovann Y
     16. Suon S
     17. Sreng S
     18. Mao S
     19. Oun S
     20. Yen S
     21. Amaratunga C
     22. Chutasmit K
     23. Saelow C
     24. Runcharern R
     25. Kaewmok W
     26. Hoa NT
     27. Thanh NV
     28. Hanboonkunupakarn B
     29. Callery JJ
     30. Mohanty AK
     31. Heaton J
     32. Thant M
     33. Gantait K
     34. Ghosh T
     35. Amato R
     36. Pearson RD
     37. Jacob CG
     38. Gonçalves S
     39. Mukaka M
     40. Waithira N
     41. Woodrow CJ
     42. Grobusch MP
     43. van Vugt M
     44. Fairhurst RM
     45. Cheah PY
     46. Peto TJ
     47. von Seidlein L
     48. Dhorda M
     49. Maude RJ
     50. Winterberg M
     51. Thuy-Nhien NT
     52. Kwiatkowski DP
     53. Imwong M
     54. Jittamala P
     55. Lin K
     56. Hlaing TM
     57. Chotivanich K
     58. Huy R
     59. Fanello C
     60. Ashley E
     61. Mayxay M
     62. Newton PN
     63. Hien TT
     64. Valecha N
     65. Smithuis F
     66. Pukrittayakamee S
     67. Faiz A
     68. Miotto O
     69. Tarning J
     70. Day NPJ
     71. White NJ
     72. Dondorp AM
     73. van der Pluijm RW
     74. Tripura R
     75. Hoglund RM
     76. Phyo AP
     77. Lek D
     78. ul Islam A
     79. Anvikar AR
     80. Satpathi P
     81. Satpathi S
     82. Behera PK
     83. Tripura A
     84. Baidya S
     85. Onyamboko M
     86. Chau NH
     87. Sovann Y
     88. Suon S
     89. Sreng S
     90. Mao S
     91. Oun S
     92. Yen S
     93. Amaratunga C
     94. Chutasmit K
     95. Saelow C
     96. Runcharern R
     97. Kaewmok W
     98. Hoa NT
     99. Thanh NV
     100. Hanboonkunupakarn B
     101. Callery JJ
     102. Mohanty AK
     103. Heaton J
     104. Thant M
     105. Gantait K
     106. Ghosh T
     107. Amato R
     108. Pearson RD
     109. Jacob CG
     110. Gonçalves S
     111. Mukaka M
     112. Waithira N
     113. Woodrow CJ
     114. Grobusch MP
     115. van Vugt M
     116. Fairhurst RM
     117. Cheah PY
     118. Peto TJ
     119. von Seidlein L
     120. Dhorda M
     121. Maude RJ
     122. Winterberg M
     123. Thuy-Nhien NT
     124. Kwiatkowski DP
     125. Imwong M
     126. Jittamala P
     127. Lin K
     128. Hlaing TM
     129. Chotivanich K
     130. Huy R
     131. Fanello C
     132. Ashley E
     133. Mayxay M
     134. Newton PN
     135. Hien TT
     136. Valeche N
     137. Smithuis F
     138. Pukrittayakamee S
     139. Faiz A
     140. Miotto O
     141. Tarning J
     142. Day NP
     143. White NJ
     144. Dondorp AM

     (2020) Triple artemisinin-based combination therapies versus artemisinin-based combination therapies for uncomplicated Plasmodium falciparum malaria: a multicentre, open-label, randomised clinical trial

     The Lancet 395:1345–1360.

     https://doi.org/10.1016/S0140-6736(20)30552-3

     • Google Scholar
150. 1. van der Pluijm RW
     2. Amaratunga C
     3. Dhorda M
     4. Dondorp AM

     (2021) Triple artemisinin-based combination therapies for malaria - a new paradigm?

     Trends in Parasitology 37:15–24.

     https://doi.org/10.1016/j.pt.2020.09.011

     • PubMed
     • Google Scholar
151. 1. Verity R
     2. Aydemir O
     3. Brazeau NF
     4. Watson OJ
     5. Hathaway NJ
     6. Mwandagalirwa MK
     7. Marsh PW
     8. Thwai K
     9. Fulton T
     10. Denton M
     11. Morgan AP
     12. Parr JB
     13. Tumwebaze PK
     14. Conrad M
     15. Rosenthal PJ
     16. Ishengoma DS
     17. Ngondi J
     18. Gutman J
     19. Mulenga M
     20. Norris DE
     21. Moss WJ
     22. Mensah BA
     23. Myers-Hansen JL
     24. Ghansah A
     25. Tshefu AK
     26. Ghani AC
     27. Meshnick SR
     28. Bailey JA
     29. Juliano JJ

     (2020) The impact of antimalarial resistance on the genetic structure of Plasmodium falciparum in the DRC

     Nature Communications 11:2107.

     https://doi.org/10.1038/s41467-020-15779-8

     • PubMed
     • Google Scholar
152. 1. Wang LT
     2. Bwambale R
     3. Keeler C
     4. Reyes R
     5. Muhindo R
     6. Matte M
     7. Ntaro M
     8. Mulogo E
     9. Sundararajan R
     10. Boyce RM

     (2018) Private sector drug shops frequently dispense parenteral anti-malarials in a rural region of Western Uganda

     Malaria Journal 17:305.

     https://doi.org/10.1186/s12936-018-2454-7

     • PubMed
     • Google Scholar
153. 1. Wasakul V
     2. Disratthakit A
     3. Mayxay M
     4. Chindavongsa K
     5. Sengsavath V
     6. Thuy-Nhien N
     7. Pearson RD
     8. Phalivong S
     9. Xayvanghang S
     10. Maude RJ
     11. Gonçalves S
     12. Day NP
     13. Newton PN
     14. Ashley EA
     15. Kwiatkowski DP
     16. Dondorp AM
     17. Miotto O

     (2023) Malaria outbreak in Laos driven by a selective sweep for Plasmodium falciparum kelch13 R539T mutants: a genetic epidemiology analysis

     The Lancet. Infectious Diseases 23:568–577.

     https://doi.org/10.1016/S1473-3099(22)00697-1

     • PubMed
     • Google Scholar
154. 1. Wellems TE
     2. Plowe CV

     (2001) Chloroquine-resistant malaria

     The Journal of Infectious Diseases 184:770–776.

     https://doi.org/10.1086/322858

     • PubMed
     • Google Scholar
155. 1. White NJ

     (2004) Antimalarial drug resistance

     The Journal of Clinical Investigation 113:1084–1092.

     https://doi.org/10.1172/JCI21682

     • PubMed
     • Google Scholar
156. 1. White NJ
     2. Pongtavornpinyo W
     3. Maude RJ
     4. Saralamba S
     5. Aguas R
     6. Stepniewska K
     7. Lee SJ
     8. Dondorp AM
     9. White LJ
     10. Day NPJ

     (2009) Hyperparasitaemia and low dosing are an important source of anti-malarial drug resistance

     Malaria Journal 8:253.

     https://doi.org/10.1186/1475-2875-8-253

     • PubMed
     • Google Scholar
157. 1. Whitlock AOB
     2. Juliano JJ
     3. Mideo N

     (2021) Immune selection suppresses the emergence of drug resistance in malaria parasites but facilitates its spread

     PLOS Computational Biology 17:e1008577.

     https://doi.org/10.1371/journal.pcbi.1008577

     • PubMed
     • Google Scholar
158. Website

     1. World Health Organization

     (2006) Guidelines for the treatment of malaria

     Accessed August 13, 2025.

     https://www.who.int/publications/i/item/guidelines-for-malaria
159. Website

     1. World Health Organization

     (2009) Minutes of the drug resistance and containment technical expert group

     Accessed October 13, 2009.

     https://www.who.int/publications/i/item/9789241597531
160. Website

     1. World Health Organization

     (2013) Minutes of the drug resistance and containment technical expert group

     Accessed June 27, 2013.

     https://www.who.int/publications/m/item/minutes-of-the-drug-resistance-and-containment-technical-expert-group-june-2013
161. Website

     1. World Health Organization

     (2015a) Guidelines for the treatment of malaria

     Accessed November 7, 2015.

     https://www.paho.org/en/node/64471
162. Website

     1. World Health Organization

     (2015b) Strategy for malaria elimination in the greater mekong subregion

     Accessed May 21, 2015.

     https://www.who.int/publications/i/item/9789290617181
163. Website

     1. World Health Organization

     (2016) Minutes of the evidence review group meeting on the emergence and spread of multidrugresistant Plasmodium falciparum lineages in the greater mekong subregion

     Accessed December 29, 2016.

     https://www.who.int/publications/m/item/minutes-of-the-evidence-review-group-meeting-on-the-emergence-and-spread-of-multidrug-resistant-plasmodium-falciparum-lineages-in-the-greater-mekong-subregion
164. Website

     1. World Health Organization

     (2021) World Malaria Report

     Accessed December 6, 2021.

     https://www.who.int/publications/i/item/9789240040496
165. Website

     1. World Health Organization

     (2022a) WHO guidelines for malaria

     Accessed November 18, 2022.

     https://www.who.int/news/item/18-11-2022-tackling-emerging-antimalarial-drug-resistance-in-africa
166. Website

     1. World Health Organization

     (2022b) WHO guidelines for malaria

     Accessed June 3, 2022.

     https://www.who.int/publications/i/item/guidelines-for-malaria
167. Website

     1. World Health Organization

     (2023) World malaria report

     Accessed November 30, 2023.

     https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2023
168. Website

     1. World Health Organization, Global Malaria Programme

     (2024) WHO malaria threats map.world health organisation

     Accessed April 23, 2024.

     https://www.who.int/publications/i/item/9789240090149
169. 1. WWARN K13 Genotype-Phenotype Study Group

     (2019) Association of mutations in the Plasmodium falciparum Kelch13 gene (Pf3D7_1343700) with parasite clearance rates after artemisinin-based treatments—a WWARN individual patient data meta-analysis

     BMC Medicine 17:1.

     https://doi.org/10.1186/s12916-018-1207-3

     • Google Scholar
170. 1. Yang T
     2. Yeoh LM
     3. Tutor MV
     4. Dixon MW
     5. McMillan PJ
     6. Xie SC
     7. Bridgford JL
     8. Gillett DL
     9. Duffy MF
     10. Ralph SA
     11. McConville MJ
     12. Tilley L
     13. Cobbold SA

     (2019) Decreased K13 abundance reduces Hemoglobin catabolism and proteotoxic stress, underpinning artemisinin resistance

     Cell Reports 29:2917–2928.

     https://doi.org/10.1016/j.celrep.2019.10.095

     • PubMed
     • Google Scholar
171. 1. Yeka A
     2. Wallender E
     3. Mulebeke R
     4. Kibuuka A
     5. Kigozi R
     6. Bosco A
     7. Kyambadde P
     8. Opigo J
     9. Kalyesubula S
     10. Senzoga J
     11. Vinden J
     12. Conrad M
     13. Rosenthal PJ

     (2019) Comparative efficacy of Artemether-Lumefantrine and Dihydroartemisinin-Piperaquine for the treatment of uncomplicated malaria in ugandan children

     The Journal of Infectious Diseases 219:1112–1120.

     https://doi.org/10.1093/infdis/jiy637

     • PubMed
     • Google Scholar
172. 1. Yeung S
     2. Pongtavornpinyo W
     3. Hastings IM
     4. Mills AJ
     5. White NJ

     (2004)

     Antimalarial drug resistance, artemisinin-based combination therapy, and the contribution of modeling to elucidating policy choices

     The American Journal of Tropical Medicine and Hygiene 71:179–186.

     • PubMed
     • Google Scholar
173. 1. Zupko RJ
     2. Nguyen TD
     3. Ngabonziza JCS
     4. Kabera M
     5. Li H
     6. Tran TN-A
     7. Tran KT
     8. Uwimana A
     9. Boni MF

     (2023) Modeling policy interventions for slowing the spread of artemisinin-resistant pfkelch R561H mutations in Rwanda

     Nature Medicine 29:2775–2784.

     https://doi.org/10.1038/s41591-023-02551-w

     • PubMed
     • Google Scholar