Diagnostic testing of suspected malaria cases has more than doubled in the last 15 years, with 75% of suspected cases seeking treatment from the public health sector receiving a diagnostic test in 2017 (World Health Organization, 2018a). Much of this progress reflects the increased distribution of rapid diagnostic tests (RDTs), with the most commonly used RDTs targeting the P. falciparum protein HRP2 (PfHRP2). In 2014, a review of published reports of pfhrp2/3 deletions was conducted and included a critical assessment of the comprehensiveness of the diagnostic investigation. (Cheng et al., 2014). The findings of this review highlighted a need for a harmonized approach to investigating and confirming or excluding pfhrp2/3 deletions and called for further studies to determine the prevalence and impact of pfhrp2/3 gene deletions. Since that review, false-negative RDT results due to pfhrp2/3 gene deletions have been reported in 10 countries in sub-Saharan Africa (SSA) (World Health Organization, 2018b). The frequency of pfhrp2/3 deletions varies across SSA, with the highest burden observed in Eritrea where 80.8% of samples from Ghindae Hospital were both pfhrp2-negative and pfhrp3-negative in 2016 (Berhane et al., 2018).

Mathematical modelling has predicted that the continued use of only PfHRP2 RDTs will quickly select for parasites without the pfhrp2 gene (Gatton et al., 2017). This selection pressure occurs due to the misdiagnosis of infections caused by parasites lacking the pfhrp2 gene, which will subsequently contribute more towards onwards transmission than wild-type parasites that are correctly diagnosed due to the expression of pfhrp2. In 2017, we conducted an analysis of the drivers of pfhrp2 gene deletion selection, identifying the administrative regions in SSA with the greatest potential for selecting for pfhrp2-deleted parasites (Watson et al., 2017). The regions identified were areas with both a low prevalence of malaria and a high frequency of people seeking treatment and being treated on the basis of PfHRP2-based RDT diagnosis. The precise strength of selection, however, is not known, with other factors such as the rate of non-malarial fevers and non-adherence to RDT outcomes likely to impact the number of misdiagnosed cases receiving treatment.

In February 2018, the World Health Organization (WHO) issued guidance for national malaria control programmes on how to investigate suspected false-negative RDTs with an emphasis on pfhrp2/3 gene deletions. (World Health Organization, 2018c). The primary study outcome to be calculated in the guidance is as follows:

The guidance recommends that a national change to non PfHRP2-based RDTs should be made if the estimated proportion of P. falciparum cases with false-negative HRP2 RDT results due to pfhrp2/3 deletions is above 5%. If the estimated proportion is less than 5% the country is recommended to establish a monitoring scheme whereby the study is repeated in two years if the 95% confidence interval does not include 5%, or one year if it does include 5%. The 5% threshold approximates the point at which the number of cases missed due to false-negative PfHRP2-based RDTs caused by pfhrp2/3 deletions may become greater than the number of cases that would be missed due to the decreased sensitivity of non PfHRP2-based RDTs. The guidance also specifies a sampling scheme to be used when estimating the prevalence of pfhrp2/3 gene deletions. Samples are to be collected from at least 10 health facilities per province to be tested, with sampling focussed on symptomatic P. falciparum patients presenting at the health facilities. All samplings are to be ideally completed within an 8-week period.

The 8-week interval permits for a rapid turnaround and allows for efficient investigations and policy responses. However, the timing of the 8-week interval chosen within a transmission season is important. The chosen interval could lead to estimates of the proportion of P. falciparum cases with false-negative HRP2 RDT results due to pfhrp2/3 deletions that are not representative of the annual average proportion. Subsequently, any recorded estimate may not be predictive of the number of cases that may be misdiagnosed due to pfhrp2/3 deletions in the years between sampling intervals. For example, an overestimation of the annual average proportion of false-negative RDTs due to pfhrp2/3 deletions could result in a switch to a less sensitive RDT, resulting in an increase in the number of malaria cases misdiagnosed if the annual average proportion of false-negative RDTs due to pfhrp2/3 deletions is less than 5%. The alternative RDT may also be both more expensive and complicated to implement. Similarly, an underestimation of the annual average proportion of P. falciparum cases with false-negative HRP2 RDT results due to pfhrp2/3 deletions would result in continued use of an overall less effective test and could provide pfhrp2/3 deleted parasite populations an opportunity to expand.

In response to these concerns, we extended our original methods (Watson et al., 2017) to characterise the impact of seasonal variations in transmission intensity on the proportion of false-negative RDTs due to pfhrp2-deleted parasites. We present an extended version of our previous model, which predicts that more false-negative RDTs due to pfhrp2 gene deletions are observed when monoclonal infections are more prevalent, with the highest proportion observed when sampling from younger children at the start of the rainy season. We continue to assess how samples collected within an 8-week interval can both over- and underestimate this proportion when compared to the annual average, which reflects the monitoring scheme recommended by the WHO for follow up studies if the outcomes of the original study are inconclusive. Lastly, we map the administrative regions in SSA with the greatest potential for estimates of the proportion of P. falciparum cases with false-negative HRP2 RDT results due to pfhrp2 deletions to be not predictive of the annual average. In addition, we identify the optimum sampling intervals for each level one administrative region, which are most representative of the annual average.

Using our model, we first explored how the proportion of clinical cases only infected with pfhrp2-deleted parasites varies throughout a transmission season. We recorded the proportion of clinical cases that are PfHRP2-negative in four settings (a low and moderate transmission setting with both a low and highly seasonal transmission dynamic), which had a starting pfhrp2 deletion frequency of 6%. 6% was chosen to reflect our previously estimated frequency of pfhrp2 deletions prior to the introduction of RDTs in the Democratic Republic of the Congo (DRC) (Watson et al., 2017). We initially assumed that the frequency of pfhrp2 deletions was not increasing over time before considering scenarios in which the selective pressure for pfhrp2 deletions causes an increase in the population frequency of phrp2 deletions. This decision allowed for the impact of seasonality on the proportion of clinical cases that are pfhrp2-negative to be isolated, before allowing comparisons to scenarios in which the proportion of clinical cases that are pfhrp2-negative is increasing also due to changes in the population frequency of phrp2 deletions.

Our predictions suggest that the misdiagnosis of clinical cases due to pfhrp2-negative RDT results is heavily dependent on transmission intensity (Figure 1). For the same population frequency of pfhrp2 gene deletions (Figure 1Q–T), the observed proportion of clinical cases that are pfhrp2-negative is predicted to be higher in lower transmission settings (Figure 1I–P). The annual average proportion of clinical cases that are pfhrp2-negative was equal to 5% and 3.25% in the low and moderate transmission setting, respectively. This observation is attributable to the lower rate of superinfection in low transmission settings. The lower rate of superinfection reduces the number of polyclonal infections and increases the chance that an individual is only infected with pfhrp2-negative parasites (Figure 1—figure supplement 1). When we considered scenarios with a selective advantage for pfhrp2-deletions (Figure 1—figure supplement 2), the population frequency of pfhrp2 gene deletions increased over the two years observed (Figure 1—figure supplement 2Q–T) with a corresponding increase in the proportion of clinical cases that are pfhrp2-negative (Figure 1—figure supplement 2I–P).

Figure 1 with 2 supplements see all

Download asset Open asset

An increased proportion of individuals only infected with pfhrp2 gene deletions is predicted to occur at the beginning of the rainy season just before incidence starts to increase. During the rainy season, the observed proportion of cases expected to yield a false-negative RDT due to pfhrp2-deleted parasites (PfHRP2-negative) falls, with the lowest proportion observed after the end of the rainy season. These dynamics are more pronounced in highly seasonal transmission regions (Figure 1B, F, J, N, R, D, H, L, P and T). In the highly seasonal settings, the observed proportion of clinical cases that are PfHRP2-negative is predicted to fluctuate above and below the 5% threshold for switching RDT provided by the WHO (Figure 1J, L, N and P). Smaller fluctuations are seen in less seasonal transmission regions (Figure 1A, E, I, M, Q, C, G, K, O and S), with no fluctuations in the observed proportion of clinical cases that are PfHRP2-negative occurring above 5% in the moderate transmission setting (Figure 1K and O). Similar patterns were observed in scenarios with an increasing frequency of pfhrp2-deletions, with fluctuations in the proportion of clinical cases that were PfHRP2-negative observed in the highly seasonal settings (Figure 1—figure supplement 2J, L, N and P). The highest proportion of cases expected to yield a false-negative RDT due to pfhrp2-deleted parasites was still observed at the beginning of the rainy season.

The specific 8-week interval during which samples are collected is predicted to impact the observed proportion of false-negative RDTs due to pfhrp2 gene deletions (Figure 2). In a moderate transmission setting, a clear seasonal pattern is predicted (Figure 2C), with sampling at the beginning of the transmission seasons resulting in significant overestimation of the annual average proportion of false-negative RDTs. Subsequently, sampling at the end of the rainy season is predicted to yield estimates that are most representative of the annual average. In comparison, surveillance in regions with low seasonality is predicted to yield estimates representative of the annual average throughout the transmission season (Figure 2B and D). In all settings, using a sampling scheme spanning the entire transmission season produced estimates that accurately estimated the annual average. A moderate increase in the proportion of false-negative RDTs is also predicted when sampling younger individuals, with the same patterns also seen within asymptomatic individuals. This observation reflects the increased probability that children younger than 5 years old yield symptoms after the first infection, due to their comparatively lower acquired clinical immunity. Similar seasonal dynamics were observed in the highly seasonal settings when we considered scenarios with a selective advantage for pfhrp2-deletions (Figure 2—figure supplement 1A and C).

Figure 2 with 1 supplement see all

Download asset Open asset

Using data from a national survey of pfhrp2 gene deletions in the DRC, we found that the model-predicted outcomes above were similar to those observed in the field (Figure 3) (Parr et al., 2017). Among 2752 PCR-positive P. falciparum cases in the DRC, individuals were more likely to be infected with only pfhrp2-negative parasites if the clinical incidence in the month prior to sample collection was lower (p=4.1×10⁻⁶), and if the individuals were younger (p=0.016). These findings were maintained when comparing across age and transmission groups, with samples collected during periods of lower transmission found to be more likely to be pfhrp2-negative in both older and younger age groups (p=6.6×10⁻⁵ and 5.6×10⁻⁴, respectively). Samples collected in younger individuals were more likely to be pfhrp2-negative in both lower and higher transmission groups when compared to older individuals (p=0.06 and 0.06, respectively).

Figure 3

Download asset Open asset

Lastly, we predicted and mapped the potential for estimates collected within 8-week intervals to be unrepresentative of the annual average proportion of false-negative RDTs due to phrp2 gene deletions across 598 first administrative regions in SSA (Figure 4). We predict that 66 regions possess at least one 8-week interval for which a premature switch to a non PfHRP2-based RDT would have been made in more than 75% of simulations (Figure 4A) and 29 regions are predicted to possess at least one 8-week interval for which a premature decision to continue using PfHRP2-based RDTs would have been made in more than 75% of simulations (Figure 4B). Out of these 29 regions, 25 are also present within the formerly identified 66 regions. The data for each administrative region can be viewed online at the following interactive database https://shiny.dide.imperial.ac.uk/seasonal_hrp2/.

Figure 4

Download asset Open asset

This research characterises the potential for surveillance in highly seasonal areas within sub-Saharan Africa to produce estimates that fail to represent the annual average proportion of P. falciparum cases with false-negative HRP2 RDT results due to pfhrp2 deletions. These findings highlight the impact of both the seasonal timing and the age of individuals sampled when estimating the proportion of false-negative RDTs due to pfhrp2 deletions. Policy decisions based on the proportion of clinical cases presenting with false-negative RDTs due to pfhrp2 gene deletions should thus be made with an awareness of the seasonal transmission dynamics of the region considered.

Our modelling predicted that there would be increased observation of false-negative HRP2 RDT results after periods of lower transmission and within younger individuals. This prediction is consistent with a large, nationally representative survey of pfhrp2-negative samples among asymptomatic subjects in the DRC (Parr et al., 2017). These predictions are also in agreement with other observations from Dioro in the Ségou region of Mali, where in 2012 more than 80% of smear-positive individuals had false-negative RDTs when collected at the end of the dry season (Koita et al., 2013). The proportion of false-negative RDTs then rapidly decreased to 20% within 3–4 weeks after the start of the rainy season. It is, however, likely that a proportion of these false-negative RDTs were due to the increased observation of lower parasitaemia at lower transmission intensities such as at the end of the dry season (Okell et al., 2012). In addition, findings from Eritrea also support our model-predicted outcomes. Eritrea is a region with lower malaria prevalence compared to the Ségou region of Mali. The resultant decrease in transmission intensity is likely to result in an increased proportion of monoclonal infections throughout the transmission season. Consequently, we would predict less variability in the number of false-negative RDTs due to pfhrp2 gene deletions at any given period within a transmission season. We also expect the observed prevalence of pfhrp2 deletions to be more stochastic due to the lower effective population size of the parasite. Indeed, infections due to pfhrp2-deleted parasites identified in Eritrea between November 2013 and November 2014 were not more likely to have occurred after periods of lower transmission intensity (p=0.56, n=144, pfhrp2 deletions at 9.7%) (Menegon et al., 2017).

Similar to the original publication (Watson et al., 2017), there are a number of modelling assumptions in this study. Firstly, there are modelling uncertainties when predicting the dynamics of false-negative RDTs due to pfhrp2-deleted parasites. To account for this uncertainty in this analysis, we have controlled for the drivers characterised in our earlier study by assuming there was no selective advantage associated with pfhrp2-deleted parasites and recording the number of individuals who would have been pfhrp2-negative and subsequently misdiagnosed. The absence of a selective advantage in this way enabled the frequency of pfhrp2 deletions to remain constant, which ensured that any observed dynamics in the estimates of false-negative RDTs due to pfhrp2 deletions were due to the seasonality of transmission and not due to an increase in the population frequency of pfhrp2 deletions. However, we are aware that there is likely a selective advantage for pfhrp2 deleted parasites and subsequently we repeated the analyses with the selective advantage included. In these simulations, we predicted a substantial increase in the frequency of pfhrp2 gene deletions (Figure 1—figure supplement 2Q-T), however clear seasonal dynamics, with an increased proportion of false-negative RDTs due to pfhrp2 deletions at the beginning of the transmission season, were still observed (Figure 2—figure supplement 1C). However, the observed dynamics were less clear in settings with the greatest increase in the frequency of pfhrp2 deletions (Figure 2—figure supplement 1B).

Secondly, we assessed the potential for a region to yield unrepresentative estimates of the proportion of false-negative RDTs due to pfhrp2 deletions through comparisons to the annual average proportion. This decision reflected firstly the monitoring period defined in the WHO technical guidance, with follow-up studies recommended after two years if the 95% CI for the proportion of P. falciparum cases with false-negative HRP2 RDT results due to pfhrp2/3 deletions is less than 5%, or one year if it does include 5%. It also reflected our modelling assumption that the population frequency of pfhrp2 deletions is not increasing over time. However, in simulations in which a selective advantage to pfhrp2 deleted parasites was included, a comparison to the annual average proportion is less suitable. For example, in Figure 2—figure supplement 1B, because we started our simulations in January the optimum sampling interval is simply the interval in the middle of the year, reflecting the constant increase in pfhrp2 deleted parasites. In these scenarios, it could be argued that the correct comparison would be to the average proportion of false-negative RDTs due to pfhrp2/3 gene deletions in the year after sampling, which reflects how many cases could be misdiagnosed between sampling rounds. Unfortunately, this comparison is difficult without knowing how the proportion of false-negative RDTs due to pfhrp2/3 gene deletions will change over time. However, we believe that it is more important to focus on the assumption that the strength of selection is negligible (see Figure 5). Our rationale for this is that it is only in areas with a low selective pressure, for which the frequency of pfhrp2/3 deletions is constant over time, that one could repeatedly make an incorrect decision with regards to whether to switch RDT (Figure 5A). In areas with a selective pressure, it is still possible to incorrectly estimate the annual average for the following year; however, the presence of the selective pressure is likely to cause any decision made to be simply premature as the frequency of pfhrp2/3 deletions and subsequently false-negative PfHRP2 RDTs will increase over time (Figure 5B).

Figure 5

Download asset Open asset

Lastly, it is important to note again that the true strength of selection is unknown. The precise strength of selection is dependent on a number of factors such as the magnitude of any fitness costs associated with pfhrp2 deletion, the degree to which microscopy-based diagnosis is used, the level of non-adherence to RDT results, the treatment coverage and the prevalence of malaria in the region considered. Consequently, our results should not be interpreted as precise predictions of how unrepresentative future samples may be. They should instead be used to support surveillance efforts and to reinforce the need for longitudinal measures conducted at the same point within a transmission season. In addition, we recommend that if possible, sample collection in highly seasonal regions should not occur at the beginning of the transmission season, as this is predicted to lead to premature decisions to switch RDT irrespective of the strength of selection. It will, however, be possible after the samples have been collected to estimate the likely frequency of pfhrp2 gene deletions by incorporating estimates of the multiplicity of infection within the sampled population. This frequency could then be used to estimate how the proportion of false-negative RDT results due to pfhrp2 deletions could increase in response to decreases in the prevalence of malaria.

In summary, our extended model predicts that highly seasonal dynamics in malaria transmission intensity will cause comparable dynamics in the observed proportion of false-negative RDT results due to pfhrp2 gene deletions. The observed proportion of false-negative RDTs due to pfhrp2 deletions is higher when monoclonal infections are more prevalent, with the highest prevalence observed when sampling at the start of the rainy season as individuals are less likely to already be infected. Similarly, the observed proportion of false-negative RDTs due to pfhrp2 deletions is higher in younger individuals who have lower clinical immunity, as they are more likely to present with clinical symptoms after their first infection event. As the rainy season progresses, individuals are more likely to be superinfected and acquire wild-type parasites, resulting in positive PfHRP2-based RDT results and a decrease in the observed proportion of false-negative RDTs due to pfhrp2 deletions. In response to these dynamics, it may be sensible for national malaria control programmes conducting surveillance for pfhrp2/3 deletions to choose a sampling interval towards the end of the transmission season, which is predicted to be most representative of the annual average proportion of false-negative RDTs due to pfhrp2 deletions. To support surveillance efforts, we have published an online database detailing the optimum sampling interval as well as the fluctuations throughout the transmission season for each administrative region.

All data generated are provided within the online database, hosted through a shiny application at https://ojwatson.shinyapps.io/seasonal_hrp2/. The raw data for the application is available within the GitHub repository at https://github.com/OJWatson/hrp2malaRia (copy archived at https://github.com/eLifeProduction/hrp2malaRia_2019).

1. 1. Bhatt S
   2. Weiss DJ
   3. Cameron E
   4. Bisan- zio D

   (2015) Malaria Atlas Project

   PfPR2-10 in Africa 2000-2015.

   https://map.ox.ac.uk/explorer/#/explorer

1. 1. Baker J
   2. McCarthy J
   3. Gatton M
   4. Kyle DE
   5. Belizario V
   6. Luchavez J
   7. Bell D
   8. Cheng Q

   (2005) Genetic Diversity of Plasmodium falciparum Histidine‐Rich Protein 2 (PfHRP2) and Its Effect on the Performance of PfHRP2‐Based Rapid Diagnostic Tests

   The Journal of Infectious Diseases 192:870–877.

   https://doi.org/10.1086/432010

   • Google Scholar
2. 1. Berhane A
   2. Anderson K
   3. Mihreteab S
   4. Gresty K
   5. Rogier E
   6. Mohamed S
   7. Hagos F
   8. Embaye G
   9. Chinorumba A
   10. Zehaie A
   11. Dowd S
   12. Waters NC
   13. Gatton ML
   14. Udhayakumar V
   15. Cheng Q
   16. Cunningham J

   (2018) Major threat to malaria control programs by Plasmodium falciparum Lacking Histidine-Rich Protein 2, Eritrea

   Emerging Infectious Diseases 24:462–470.

   https://doi.org/10.3201/eid2403.171723

   • PubMed
   • Google Scholar
3. 1. Cairns M
   2. Roca-Feltrer A
   3. Garske T
   4. Wilson AL
   5. Diallo D
   6. Milligan PJ
   7. Ghani AC
   8. Greenwood BM

   (2012) Estimating the potential public health impact of seasonal malaria chemoprevention in african children

   Nature Communications 3:1–9.

   https://doi.org/10.1038/ncomms1879

   • Google Scholar
4. 1. Cairns ME
   2. Walker PG
   3. Okell LC
   4. Griffin JT
   5. Garske T
   6. Asante KP
   7. Owusu-Agyei S
   8. Diallo D
   9. Dicko A
   10. Cisse B
   11. Greenwood BM
   12. Chandramohan D
   13. Ghani AC
   14. Milligan PJ

   (2015) Seasonality in malaria transmission: implications for case-management with long-acting artemisinin combination therapy in sub-Saharan africa

   Malaria Journal 14:1–13.

   https://doi.org/10.1186/s12936-015-0839-4

   • PubMed
   • Google Scholar
5. 1. Cheng Q
   2. Gatton ML
   3. Barnwell J
   4. Chiodini P
   5. McCarthy J
   6. Bell D
   7. Cunningham J

   (2014) Plasmodium falciparum parasites lacking histidine-rich protein 2 and 3: a review and recommendations for accurate reporting

   Malaria Journal 13:283.

   https://doi.org/10.1186/1475-2875-13-283

   • PubMed
   • Google Scholar
6. 1. Gatton ML
   2. Dunn J
   3. Chaudhry A
   4. Ciketic S
   5. Cunningham J
   6. Cheng Q

   (2017) Implications of parasites lacking plasmodium falciparum Histidine-Rich protein 2 on malaria morbidity and control when rapid diagnostic tests are used for diagnosis

   The Journal of Infectious Diseases 215:1156–1166.

   https://doi.org/10.1093/infdis/jix094

   • PubMed
   • Google Scholar
7. 1. Jnk R
   2. Scott AJ

   (1984) On Chi-Squared tests for multiway contingency tables with cell proportions estimated from survey data

   Annals of Statistics 12:46–60.

   https://doi.org/10.1214/aos/1176346391

   • Google Scholar
8. 1. Koita OA
   2. Ndiaye J-L
   3. Nwakanma D
   4. Sangare L
   5. Ndiaye D
   6. Joof F

   (2013)

   Seasonal changes in the frequency of false negative rapid diagnostic tests based on histidine rich protein 2 (HRP2)

   The American Journal of Tropical Medicine and Hygiene 89:1.

   • Google Scholar
9. 1. Menegon M
   2. L'Episcopia M
   3. Nurahmed AM
   4. Talha AA
   5. Nour BYM
   6. Severini C

   (2017) Identification of plasmodium falciparum isolates lacking histidine-rich protein 2 and 3 in Eritrea

   Infection, Genetics and Evolution 55:131–134.

   https://doi.org/10.1016/j.meegid.2017.09.004

   • Google Scholar
10. Report

    1. Meshnick S
    2. Janko M
    3. Thwai K
    4. Levitz L
    5. Emch M

    (2013) Demographic And Health Survey (DRC-DHSII) Supplemental Malaria Report 2015

    Adrian Pingstone.

    https://dhsprogram.com/pubs/pdf/FR300/FR300.Mal.pdf

    • Google Scholar
11. Website

    1. National Weather Service Climate Prediction Center

    (2010) Climate Prediction Center - International Desks 

    Accessed July 1, 2010.

    https://www.cpc.ncep.noaa.gov/products/international/
12. 1. Okell LC
    2. Bousema T
    3. Griffin JT
    4. Ouédraogo AL
    5. Ghani AC
    6. Drakeley CJ

    (2012) Factors determining the occurrence of submicroscopic malaria infections and their relevance for control

    Nature Communications 3:1237.

    https://doi.org/10.1038/ncomms2241

    • PubMed
    • Google Scholar
13. 1. Parr JB
    2. Verity R
    3. Doctor SM
    4. Janko M
    5. Carey-Ewend K
    6. Turman BJ
    7. Keeler C
    8. Slater HC
    9. Whitesell AN
    10. Mwandagalirwa K
    11. Ghani AC
    12. Likwela JL
    13. Tshefu AK
    14. Emch M
    15. Juliano JJ
    16. Meshnick SR

    (2017) Pfhrp2-Deleted plasmodium falciparum parasites in the democratic republic of the Congo: a national Cross-sectional survey

    The Journal of Infectious Diseases 216:.

    https://doi.org/10.1093/infdis/jiw538

    • PubMed
    • Google Scholar
14. Software

    1. R Development Core Team

    (2016) R: A Language and Environment for Statistical Computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org/
15. 1. Watson OJ
    2. Slater HC
    3. Verity R
    4. Parr JB
    5. Mwandagalirwa MK
    6. Tshefu A
    7. Meshnick SR
    8. Ghani AC

    (2017) Modelling the drivers of the spread of plasmodium falciparum hrp2 gene deletions in sub-Saharan Africa

    eLife 6:e25008.

    https://doi.org/10.7554/eLife.25008

    • PubMed
    • Google Scholar
16. Software

    1. Watson OJ

    (2019) hrp2malaRia, version 4d992a0

    GitHub.

    https://github.com/OJWatson/hrp2malaRia
17. Report

    1. World Health Organization

    (2018a) World Malaria Report

    World Health Organization.

    https://www.who.int/malaria/publications/world-malaria-report-2018/en/

    • Google Scholar
18. Website

    1. World Health Organization

    (2018b) Malaria threats map

    Accessed May 1, 2018.

    http://www.who.int/malaria/maps/threats-about/en/
19. Website

    1. World Health Organization

    (2018c) Protocol for estimating the prevalence of pfhrp2/pfhrp3 gene deletions among symptomatic falciparum patients with false-negative RDT results

    Accessed March 16, 2018.

    http://www.who.int/malaria/publications/atoz/hrp2-deletion-protocol/en/

Author details

1. Oliver John Watson

   MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   o.watson15@imperial.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2374-0741

2. Robert Verity

   MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom

   Contribution

   Formal analysis, Supervision, Visualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Azra C Ghani

   MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom

   Contribution

   Supervision, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Tini Garske

   MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom

   Contribution

   Data curation, Software, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

5. Jane Cunningham

   Global Malaria Programme, World Health Organization, Geneva, Switzerland

   Contribution

   Supervision, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

6. Antoinette Tshefu

   School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo

   Contribution

   Resources, Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

7. Melchior K Mwandagalirwa

   1. School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
   2. Department of Epidemiology, Gillings School for Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, United States

   Contribution

   Resources, Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

8. Steven R Meshnick

   1. Department of Epidemiology, Gillings School for Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, United States
   2. Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States

   Contribution

   Resources, Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

9. Jonathan B Parr

   Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

10. Hannah C Slater

    MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

• 1,155

  views

• 220

  downloads

• 25

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.