A global decline of malaria in recent decades has led to a push for complete national elimination of human malaria parasites in 21 countries by 2020 (WHO, 2018). In elimination and pre-elimination settings, malaria transmission is often highly heterogeneous geographically (Bousema et al., 2012; Carter et al., 2000; Sturrock et al., 2016), and policy-makers must focus on reducing transmission in remaining endemic foci, while protecting areas where malaria has been effectively controlled from imported infections that threaten to reintroduce parasites and reignite transmission (Cotter et al., 2013; Churcher et al., 2014). These tasks require different interventions, so understanding how patterns of regular travel to and from malaria endemic regions of a country contribute to the spread of malaria is a critical component of elimination planning. Measuring the spatial spread of malaria remains technically challenging, however, due to frequent asymptomatic and undetectable infections (Lindblade et al., 2013), as well as the inherent challenges associated with epidemiological surveillance in rural areas among highly mobile populations.

Decisions about how to allocate resources for malaria control and elimination are generally based on reports from hospitals, clinics, community health workers (CHWs) and non-governmental organizations (NGOs) around the country, which provide a measure of the incidence of symptomatic cases (WHO, 2007; Moonen et al., 2010). In areas with high mobility, however, the reporting health facility may not accurately capture local transmission, instead detecting imported infections that have been acquired elsewhere. Traditionally, patient travel surveys have been the principal method for identifying imported cases, but surveys can be unreliable and limited in scope, and importation cannot always be confirmed even when a travel history is accurate (Wesolowski et al., 2014). We and others have used spatially explicit epidemiological models– for example, parameterized using mobile phone data– to estimate ‘sources and sinks’ of parasites and the impact of travel on the spread of malaria (Wesolowski et al., 2012). These methods show promise, but do not provide highly spatially resolved estimates in regions with low cell tower density, such as in forested or sparsely populated regions, and may suffer from ownership biases.

There is also a growing interest in the routine use of sequencing to genotype parasites, which may provide insights into the spatial spread of malaria, particularly in low transmission settings where imported infections that sustain remaining hotspots must be addressed and in places where drug resistance poses a threat (Amato et al., 2018; Busby et al., 2016; Imwong et al., 2017; Karunaweera et al., 2014; MalariaGEN Plasmodium falciparum Community Project et al., 2016; Miotto et al., 2015; Obaldia et al., 2015; Taylor et al., 2017; Tun et al., 2015). However, in spite of decreasing costs, the use of genetic data is currently constrained by the lack of appropriate methods for analysis. Plasmodium falciparum infections are often composed of multiple different parasite clones, which undergo sexual recombination in the mosquito (Mzilahowa et al., 2007). As a result, phylogenetic and population genetic methods normally applied to the analysis of non-recombining organisms cannot be used. In addition to applying human population genetic tools to malaria parasites (Chang et al., 2012; Lawson et al., 2012; Miotto et al., 2013; Patterson et al., 2006; Pritchard et al., 2000), an emerging field of promising new approaches, such as THE REAL McCOIL, hmmIBD, or isoRelate, are being developed to explicitly address these characteristics of malaria infection (Taylor et al., 2017; Chang et al., 2017; Henden et al., 2018; Schaffner et al., 2018).

Conceptually, genetic and epidemiological approaches should complement each other – genetic data encodes information about the relatedness of parasites and mixing patterns between different parasite populations, while epidemiological data provides insights into clinical cases and the patterns of transmission. For viral pathogens like the influenza virus, where recombination does not obscure relationships between lineages, phylodynamic frameworks have been developed for inferring migration patterns and identifying the factors contributing to the migration rate (Lemey et al., 2014; Nelson et al., 2015; De Maio et al., 2015; Vaughan et al., 2014). Although it is possible to measure population differentiation on continental scales for malaria (Miotto et al., 2015), elimination programs often require inferences on smaller spatial scales, which are currently more challenging because of frequent recombination and the complex epidemiological dynamics of the parasite. In particular, on local spatial scales genetic signals may not be easily resolved because parasites are likely to be highly related, and comparisons between many pairs of populations become computationally prohibitive (De Maio et al., 2015; Vaughan et al., 2014; Rasmussen et al., 2014). Developing methods that leverage insights from genomic data, epidemiological information, and mobility data sources such as travel survey or mobile phone data, is therefore an important goal for malaria, particularly if the collection of clinical samples for genetic analysis and travel data can be incorporated into routine surveillance workflows to support decision making for control programs.

The National Malaria Elimination Programme (NMEP) in Bangladesh currently estimates the burden of malaria using symptomatic case counts aggregated at administrative level 3 (upazila) or 4 (union). Typical of malaria in Southeast Asian countries, much of the transmission is thought to occur in forests and forest fringes where the principal vectors abound (Bhatia et al., 2013). The difficulties of surveillance in hard-to-reach forested areas complicate decisions about where to target transmission-reducing interventions (often focused on vector control) and how to find and treat imported cases as Bangladesh moves towards elimination. Moreover, Bangladesh – one of the countries that plan to eliminate malaria nationally – borders the Greater Mekong Subregion (GMS) (WHO, 2017b; Haque et al., 2014), where evidence of resistance to first line treatment for malaria (artemisinin combination therapy, or ACT) has been found in all countries (WHO, 2017a). Although resistance to artemisinin and to ACT partner drugs has not been found to date locally, Bangladesh is a potential point of passage in the spread of drug resistance out of the GMS, so monitoring drug resistance markers is crucial.

Epidemiological models of malaria designed to understand spatial transmission are often based on clinical incidence and seroprevalence estimates; data and methods required for integrating insights about importation from other data sources, including genetic data, are currently lacking (Daniels et al., 2015). Mobility patterns derived from mobile phone data are difficult to access, and rarely coincide temporally with epidemiological and genetic data, but could in theory provide real-time estimates of population movements. We collected and analyzed paired genetic, travel survey, and epidemiological malaria case data across the Chittagong Hill Tracts (CHT) region of Bangladesh, in combination with estimated population mobility patterns from mobile phone calling data. We developed a simple metric to quantify genetic mixing from parasite genetic barcode data. Geographic patterns of genetic mixing were then compared to estimates of malaria importation from a mathematical model, based on clinical epidemiological data and on mobility data from travel surveys and mobile phone records. We show that, taken together, these data sources provide evidence of heterogeneous transmission outside the high-incidence forested areas, and of substantial importation of parasites throughout the CHT. We propose that this type of data integration and analytical pipeline could help national control programs effectively target resources for malaria elimination.

The map of clinical incidence from the NMEP for 2015 and 2016 shown in Figure 1A in the CHT supports the hypothesis that transmission is highest in the eastern, forested region, similar to other countries in the GMS with forested border areas (Figure 1B). The location of the reporting health clinic may not reflect local acquisition of infection, however, particularly in regions like Bangladesh where laborers, forest-workers, and military personnel may travel frequently in and out of high transmission areas, but report to clinics in low transmission areas. We aimed to map remaining transmission foci in the CHT; in particular to identify areas outside forested regions where transmission is being sustained, rather than imported from the forest, since these areas should be targeted by transmission reduction interventions. To this end, we enrolled 2090 patients with confirmed malaria, attending 57 hospitals and clinics across the malaria-endemic region (see Figure 1—figure supplement 1 for a map of sample distribution). All patients answered a travel questionnaire and their dried blood spots were collected on filter paper. In total, parasite genotypes of 1412 patients were obtained successfully. These data were combined with reported incidence for the region, and data on aggregated daily movement patterns from 2.4 million mobile phone subscribers over 6 months across the CHT (see Materials and methods).

Figure 1 with 3 supplements see all

Download asset Open asset

Disparate data sets identify broad division of transmission foci

We first analyzed each type of data separately, focusing on quantifying signals of spatial connectivity and on measuring transmission intensity (Figure 1C,D,E). Given the importance of distinguishing between the forested border regions – where most malaria is thought to occur – and less forested areas elsewhere, for clarity we defined forested areas as those with >50% forest coverage. In the travel survey, 31% (N = 654) of malaria positive individuals reported living outside forested areas. Of these individuals, the majority did not report any travel to forested areas (66%, N = 434), suggesting that either individuals were acquiring infections outside these areas, or that the travel survey did not capture all forest travel. The survey indicated a general separation between the north and south portions of the endemic region, with fewer people traveling between these two regions. Within the regions, however, substantial travel was reported between the high incidence forested areas and the lower incidence ones (Figure 1D). Similarly, by simply connecting those pairs of unions where we identified parasites with identical genetic barcodes, a broad north-south division of the CHT region clearly emerged, as did connections between forested and non-forested areas (Figure 1C). Temporally, parasite pairs with identical genetic barcodes were significantly closer in time than those that were not identical (medians = 16.5 vs. 145 days, Mann-Whitney test p-value<2×10⁻¹⁶). Conversely, we found that other genetic diversity metrics, except for drug resistance-related markers, showed limited spatial resolution (see Figure 1—figure supplements 2 and 3). Roughly consistent with the north-south divide, mobility estimates derived from mobile phone calling data suggested a high amount of travel in the coastal areas around the cities of Chittagong and Cox’s Bazar, with little travel between these areas (Figure 1E). This separation into two main transmission foci was consistent with the larger geographic distance between these areas of the CHT, but provided little insight into the extent of transmission heterogeneity on more local spatial scales.

To assess whether patterns of genetic relatedness among parasites showed signals consistent with survey- or mobile phone-based estimates of mobility, we analyzed all parasite pairs, along a continuum of genetic similarity, with respect to mobility outcomes. As expected, genetically similar parasites (fewer differences in genetic barcode comparisons) were more likely to come from the same residence unions (Figure 2A). For parasites from different unions, more related parasites were more likely to come from unions where direct travel had been reported in the survey (Figure 2B; see Materials and methods). To ensure this signal was not obscured by local effects, since both travel and genetic similarity is expected to be high between neighboring unions, we separated the data into unions less than 20 km apart and unions more than 20 km apart (Figure 2—figure supplement 1). The association shown in Figure 2B was mainly driven by unions that were ≥20 km apart, indicating that travel is responsible for closely related parasites in more geographically separated unions. Remarkably, closely related parasites that were neither from the same location nor from locations with direct travel reported were more likely to come from unions with indirect travel reported to a shared third location (Figure 2C). Using the travel survey data at the individual patient level instead of population level, we found that individuals infected with closely related parasites were significantly more likely to report living, working, or traveling to the same locations, confirming the correlation between the genetic and travel survey data (Figure 2—figure supplement 2). Comparing the genetic data to travel measured using mobile phone data, we found that genetically similar parasites that were not from the same locations were more likely to come from locations with greater inferred connectivity between them (Figure 2D), even though the majority of travel measured was in less forested areas. Therefore, although the travel survey and mobile phone data represent different geographic and population coverage, and disparate sample sizes (Figure 2—figure supplement 3), spatial signatures of connectivity were generally consistent with the genetic data.

Figure 2 with 3 supplements see all

Download asset Open asset

Genetic and model-based evidence of transmission in low incidence areas

To map transmission heterogeneities on smaller spatial scales, these rich data sources can be analyzed using more sophisticated approaches. We developed a metric designed to quantify the amount of parasite genetic mixing – indicative of frequent parasite importation and recombination between different parasites from different locations – in different regions. This index was calculated for each location with respect to all other locations as a risk ratio; specifically, the ratio of risks that parasites were genetically similar given that they were far away and nearby. To identify this index, we used the empirical relationship between genetic similarity and geographic distance for this region, so the thresholds defined would be setting-specific if rolled out routinely as part of surveillance programs (see Materials and methods). In brief, we quantified the decline of parasite genetic similarity with geographic distance, uncovering a robust signal; pairs of parasites sampled from unions that are geographically closer were more likely to be genetically similar (Figure 3A). In addition to the enrichment of genetically similar parasites in spatial proximity, there was a temporal signal: parasite pairs with SNP differences smaller than 10% were significantly closer in time than those with SNP differences greater than 10% (medians = 14 vs. 145 days, Mann-Whitney test p-value<2×10⁻¹⁶). Using the empirical relationship between geographic and genetic distances, we assumed a uniform prior on geographic distance and calculated the probability of geographic distances between patients given different levels of genetic similarity (Figure 3—figure supplement 1; see Materials and methods). This allowed us to define a CHT-specific ‘genetic mixing index’ to identify higher sharing of parasite barcodes between samples from relatively distant locations, indicative of high gene flow or importation (Figure 3B and Figure 3—figure supplement 3; see Materials and methods). Figure 3B maps this index across the CHT, showing heterogeneous patterns of mixing in the southwestern region, with high mixing regions indicating frequently imported infections, and low genetic mixing suggesting predominantly local transmission. In contrast, we did not observe elevated genetic mixing in the north (see robustness analysis in Figure 3—figure supplement 2 and Supplementary file 1 for 95% confidence intervals of the index).

Figure 3 with 3 supplements see all

Download asset Open asset

Genetic measures based on SNP barcodes were able to identify unions with a high likelihood of importation events, but not the origin of imported infections since sharing genetic similarity did not provide information about the direction of movement. Epidemiological models that describe directional travel patterns may be used to combine reported incidence with data from travel surveys or mobile phone data to reconstruct routes of importation. We used mobility estimates from the survey and from mobile phone data to infer the amount of travel between locations in a simple epidemiological model that predicts the rate of infections moving between unions and mapped the resulting malaria source and sink locations (see Materials and methods, Figure 4A,B, and Figure 4—figure supplements 1 and 2). We compared the relative contribution of imported infections to local transmission in each union and predicted that locations in the southwestern area of the CHT had the highest proportion of imported cases, using both the travel survey and mobile phone data (Figure 4A,B). The mobility patterns derived from mobile phone data suggested more infections were being imported overall, however qualitatively both mobile phone data and travel surveys identified overlapping areas of the country where the top locations of importations were occurring. The genetic mixing index therefore provides an independent confirmation that extensive parasite flow is occurring in the highly populated southwestern region of the CHT, while the epidemiological model – parameterized using travel survey data and mobile phone data – provides insights into the sources of imported infections.

Figure 4 with 2 supplements see all

Download asset Open asset

Generating combined transmission maps

Combining unions with a high genetic mixing index together with unions with a high proportion of imported cases estimated from the epidemiological models, we created an updated risk map for malaria transmission in the CHT (Figure 5D). Both the genetic mixing index and the epidemiological models suggest frequent mixing in the southwest of the CHT, in Cox’s Bazar district (Figures 3B and 4A,B). The epidemiological model predictions were roughly consistent with the unions exhibiting a high genetic mixing index (Figure 4C). Additionally, we identified frequent importation within the southwest region from epidemiological models parameterized using both travel survey and mobile phone data. In the model parameterized using travel survey data, parasites were predicted to be frequently imported from the forested areas of the CHT to the more populated, lower transmission areas, particularly unions within Cox’s Bazar district (Figure 5A, Figure 5—figure supplements 1 and 2). The model parameterized by mobile phone data also identified the forested areas as a source, and in addition, suggested that parasites were imported from more populated, lower transmission areas in the west (Figure 5B). Since travel surveys and mobile data had quite different geographic coverage and different biases (Figure 1C,D), some discrepancies are expected. Nevertheless, these analyses support the ideas that a) forested areas are an important source of imported infections, consistent with current dogma, but b) human movement around densely populated regions with relatively low transmission appears to contribute substantially to heterogeneous transmission across the southwest of the CHT (Figure 5C). This means that resources for elimination should be targeted in these areas, in addition to the forested regions. The updated map (Figure 5D) highlights the regions which were not identified in the incidence map (Figure 1A), for example. Our results also suggest that importation routes occur separately in the north and south of the Chittagong region (Figure 5C and Figure 5—figure supplement 1), implying that a staged elimination strategy could be effective.

Figure 5 with 2 supplements see all

Download asset Open asset

Routine surveillance for malaria among control programs is primarily based on prevalence estimates and/or clinical incidence reported by hospitals, clinics, community health workers and non-governmental organizations. The geographic distribution of clinical cases may not reflect patterns of transmission, particularly in areas hosting highly mobile populations, where importation of parasites is common. Identifying the true foci of transmission is particularly important in elimination settings, such as Bangladesh. A number of approaches have been suggested to augment risk maps based on incidence data, including molecular surveillance and epidemiological models parameterized by new data streams, such as mobile phone data. Here, our goal was to assess whether a combination of these approaches could provide actionable insights for control programs. As a complement to a simple map of clinical incidence, or model-based geospatial maps based on incidence and prevalence estimates (https://map.ox.ac.uk), this combined approach illustrated that, while the eastern forested regions are indeed contributing imported infections to the lower transmission areas in the southwest, substantial local importation and genetic mixing is also occurring in these highly populated areas along the southwestern part of the CHT. This suggests that, while targeting interventions to forested areas is a key strategy for elimination, it might not be successful unless the mixing between low transmission settings in the southwest region is also addressed. We propose that, with appropriate sampling strategies, parasite genetic analysis could provide actionable insights for national control programs and help evaluate the success of elimination programs.

Our samples came from symptomatic patients who presented at a health facility, which would be the norm for routine reporting to the NMEP. We therefore did not include asymptomatic or subclinical infections, and it is unknown to what extent the parasite in symptomatic patients are representative of the entire parasite population in this area (Searle et al., 2017). Although we were able to enroll patients at 57 health facilities that covered a wide geographic area, these study sites only represent 59% of all CHT health facilities which report data to the NMEP. In several health facility catchment areas, non-government organizations provide malaria treatment, testing, and education outside health facilities as part of a malaria outreach initiative. In these areas, we may be under-estimating true incidence, since fewer people seek malaria treatment at the government health clinics. Although these biases may impact epidemiological modeling results, the genetic mixing index – which may lack power when sample size is small, but is not biased by underestimating incidence – can provide complementary insights.

In this transmission setting and geography, genetic differentiation based on genetic barcodes between parasite populations cannot be easily distinguished by commonly used methods, such as average pairwise difference or F_ST (Figure 1—figure supplement 3). Consistent with our previous studies showing that the proportion of nearly identical genetic sequences is more sensitive to recent migration of pathogens (Taylor et al., 2017; Chang et al., 2016), in our data the proportion of parasites with nearly identical genetic barcodes was highly associated with geographic distance. This allowed us to develop a genetic mixing index, which may provide context-specific information about gene flow. However, the thresholds used for calculating this index would need to be determined in each setting independently, because the association between geographic distance and genetic similarity can vary between settings. This index could also be used at the individual level, to identify infections that are likely to be imported. We found three cases with a genetic mixing index greater than 1, suggesting that they were likely to be imported and, consistently, there was reported travel between their residence unions and the unions they shared similar barcodes with. The reliance of the genetic mixing index and other spatial approaches on nearly-identical parasites – which are rare, but hold the most meaningful genetic signals – means that small sample sizes and large uncertainty are inevitable challenges to their routine use. Although increasing sample size would improve our estimates, adding more SNPs to the analysis is unlikely to reduce uncertainty of the index, since the patterns of relatedness are an intrinsic feature of malaria epidemiology in this region. Nevertheless, in combination with other modeling approaches, genetic data can yield insights that are not attainable any other way – particularly in the context of elimination planning. As molecular surveillance becomes more widespread, clearly establishing the strengths and limitations of genetic data will be critical.

Mobile phone data and travel surveys are different from each other in terms of scale, geographic coverage, and granularity of data about human mobility in this context (Wesolowski et al., 2018). Our travel survey provides detailed, individual-level information about infected individuals, although its quality is dependent on the respondent’s ability to accurately recall their travel over the past two months. Although we included specific questions to help with recall bias, such as asking explicitly about travel to the forested areas, these biases likely still exist and may have limited our ability to infer parasite movement. Overall, travel survey data were sparse at the union level, and many individuals reported no travel (287 out of 2,090), or only travel within a single union (1240 out of 2,090). In contrast, mobile phones provide large volumes of data about the connectivity of the general population, but no individual-level information, and only in locations where cell towers exist. The contrasting geographic coverage and scale of the two sources of data on human mobility – mobile phone and travel survey data – lead to different estimates of parasite importation. Mobile phone data provides insights into large-scale population flows, but is limited to regions with cell towers, while travel survey data provide detailed information about a small number of malaria-infected individuals. Both data sources are biased, but together can provide complementary insights into the population dynamics underlying the spread of parasites in the region. Given the difficulties associated with measuring population mobility and the importance of understanding importation in countries aiming for elimination, multiple (albeit biased) estimates of human travel provide the most complete picture of parasite flows. Given the biases in the incidence data (discussed above), in the epidemiological model, we chose a simple approach without having to estimate the underlying transmission or directly incorporating vectoral capacity. Remarkably, travel patterns from both data sources were consistent with results from parasite genetic analyses, confirming their complementarity.

Every epidemiological and genetic data set has inherent limitations and biases, but multiple data layers can complement each other effectively to provide insights into different components of malaria transmission. This study represents a first example of the utility of combining these sources of information in the context of identifying imported infections, and we believe it shows that cost-effective sequencing approaches can be combined with simple epidemiological models to provide a more complete picture of malaria transmission on a subnational level.

All genetic data are included in Supplementary file 4 and all travel matrices are included in Supplementary file 5.

1. 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
2. Report

   1. Bangladesh Bureau of Statistics

   (2011) Bangladesh - Population and Housing Census 2011

   Bangladesh Bureau of Statistics.

   http://microdata.worldbank.org/index.php/catalog/1617

   • Google Scholar
3. 1. Bhatia R
   2. Rastogi RM
   3. Ortega L

   (2013)

   Malaria successes and challenges in asia

   Journal of Vector Borne Diseases 50:239–247.

   • PubMed
   • Google Scholar
4. 1. Bousema T
   2. Griffin JT
   3. Sauerwein RW
   4. Smith DL
   5. Churcher TS
   6. Takken W
   7. Ghani A
   8. Drakeley C
   9. Gosling R

   (2012) Hitting hotspots: spatial targeting of malaria for control and elimination

   PLoS Medicine 9:e1001165.

   https://doi.org/10.1371/journal.pmed.1001165

   • PubMed
   • Google Scholar
5. 1. Busby GB
   2. Band G
   3. Si Le Q
   4. Jallow M
   5. Bougama E
   6. Mangano VD
   7. Amenga-Etego LN
   8. Enimil A
   9. Apinjoh T
   10. Ndila CM
   11. Manjurano A
   12. Nyirongo V
   13. Doumba O
   14. Rockett KA
   15. Kwiatkowski DP
   16. Spencer CC
   17. Malaria Genomic Epidemiology Network

   (2016) Admixture into and within sub-Saharan africa

   eLife 5:e15266.

   https://doi.org/10.7554/eLife.15266

   • PubMed
   • Google Scholar
6. 1. Carter R
   2. Mendis KN
   3. Roberts D

   (2000)

   Spatial targeting of interventions against malaria

   Bulletin of the World Health Organization 78:1401–1411.

   • PubMed
   • Google Scholar
7. 1. Chang HH
   2. Park DJ
   3. Galinsky KJ
   4. Schaffner SF
   5. Ndiaye D
   6. Ndir O
   7. Mboup S
   8. Wiegand RC
   9. Volkman SK
   10. Sabeti PC
   11. Wirth DF
   12. Neafsey DE
   13. Hartl DL

   (2012) Genomic sequencing of Plasmodium falciparum malaria parasites from Senegal reveals the demographic history of the population

   Molecular Biology and Evolution 29:3427–3439.

   https://doi.org/10.1093/molbev/mss161

   • PubMed
   • Google Scholar
8. 1. Chang HH
   2. Dordel J
   3. Donker T
   4. Worby CJ
   5. Feil EJ
   6. Hanage WP
   7. Bentley SD
   8. Huang SS
   9. Lipsitch M

   (2016) Identifying the effect of patient sharing on between-hospital genetic differentiation of methicillin-resistant Staphylococcus aureus

   Genome Medicine 8:18.

   https://doi.org/10.1186/s13073-016-0274-3

   • PubMed
   • Google Scholar
9. 1. Chang HH
   2. Worby CJ
   3. Yeka A
   4. Nankabirwa J
   5. Kamya MR
   6. Staedke SG
   7. Dorsey G
   8. Murphy M
   9. Neafsey DE
   10. Jeffreys AE
   11. Hubbart C
   12. Rockett KA
   13. Amato R
   14. Kwiatkowski DP
   15. Buckee CO
   16. Greenhouse B

   (2017) THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites

   PLOS Computational Biology 13:e1005348.

   https://doi.org/10.1371/journal.pcbi.1005348

   • PubMed
   • Google Scholar
10. 1. Churcher TS
    2. Cohen JM
    3. Novotny J
    4. Ntshalintshali N
    5. Kunene S
    6. Cauchemez S

    (2014) Public health. Measuring the path toward malaria elimination

    Science 344:1230–1232.

    https://doi.org/10.1126/science.1251449

    • PubMed
    • Google Scholar
11. 1. Cotter C
    2. Sturrock HJ
    3. Hsiang MS
    4. Liu J
    5. Phillips AA
    6. Hwang J
    7. Gueye CS
    8. Fullman N
    9. Gosling RD
    10. Feachem RG

    (2013) The changing epidemiology of malaria elimination: new strategies for new challenges

    The Lancet 382:900–911.

    https://doi.org/10.1016/S0140-6736(13)60310-4

    • PubMed
    • Google Scholar
12. 1. Daniels RF
    2. Schaffner SF
    3. Wenger EA
    4. Proctor JL
    5. Chang HH
    6. Wong W
    7. Baro N
    8. Ndiaye D
    9. Fall FB
    10. Ndiop M
    11. Ba M
    12. Milner DA
    13. Taylor TE
    14. Neafsey DE
    15. Volkman SK
    16. Eckhoff PA
    17. Hartl DL
    18. Wirth DF

    (2015) Modeling malaria genomics reveals transmission decline and rebound in Senegal

    PNAS 112:7067–7072.

    https://doi.org/10.1073/pnas.1505691112

    • PubMed
    • Google Scholar
13. 1. De Maio N
    2. Wu CH
    3. O'Reilly KM
    4. Wilson D

    (2015) New Routes to Phylogeography: A Bayesian Structured Coalescent Approximation

    PLOS Genetics 11:e1005421.

    https://doi.org/10.1371/journal.pgen.1005421

    • PubMed
    • Google Scholar
14. 1. Hansen MC
    2. Potapov PV
    3. Moore R
    4. Hancher M
    5. Turubanova SA
    6. Tyukavina A
    7. Thau D
    8. Stehman SV
    9. Goetz SJ
    10. Loveland TR
    11. Kommareddy A
    12. Egorov A
    13. Chini L
    14. Justice CO
    15. Townshend JR

    (2013) High-resolution global maps of 21st-century forest cover change

    Science 342:850–853.

    https://doi.org/10.1126/science.1244693

    • PubMed
    • Google Scholar
15. 1. Haque U
    2. Overgaard HJ
    3. Clements AC
    4. Norris DE
    5. Islam N
    6. Karim J
    7. Roy S
    8. Haque W
    9. Kabir M
    10. Smith DL
    11. Glass GE

    (2014) Malaria burden and control in Bangladesh and prospects for elimination: an epidemiological and economic assessment

    The Lancet Global Health 2:e98–e105.

    https://doi.org/10.1016/S2214-109X(13)70176-1

    • PubMed
    • Google Scholar
16. 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

    • PubMed
    • Google Scholar
17. 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
18. 1. Karney CFF

    (2013) Algorithms for geodesics

    Journal of Geodesy 87:43–55.

    https://doi.org/10.1007/s00190-012-0578-z

    • Google Scholar
19. 1. Karunaweera ND
    2. Galappaththy GN
    3. Wirth DF

    (2014) On the road to eliminate malaria in Sri Lanka: lessons from history, challenges, gaps in knowledge and research needs

    Malaria Journal 13:59.

    https://doi.org/10.1186/1475-2875-13-59

    • PubMed
    • Google Scholar
20. 1. Katz D
    2. Baptista J
    3. Azen SP
    4. Pike MC

    (1978) Obtaining Confidence Intervals for the Risk Ratio in Cohort Studies

    Biometrics 34:469.

    https://doi.org/10.2307/2530610

    • Google Scholar
21. 1. Laskar M
    2. Kabir M
    3. Naher S
    4. Islam M
    5. Parvez M
    6. Siddiqui M

    (2018) Nine Years of Malaria Cases in Bangladesh: A Time Series Analysis

    International Journal of TROPICAL DISEASE & Health 28:1–13.

    https://doi.org/10.9734/IJTDH/2017/37916

    • Google Scholar
22. 1. Lawson DJ
    2. Hellenthal G
    3. Myers S
    4. Falush D

    (2012) Inference of population structure using dense haplotype data

    PLoS Genetics 8:e1002453.

    https://doi.org/10.1371/journal.pgen.1002453

    • PubMed
    • Google Scholar
23. 1. Lemey P
    2. Rambaut A
    3. Bedford T
    4. Faria N
    5. Bielejec F
    6. Baele G
    7. Russell CA
    8. Smith DJ
    9. Pybus OG
    10. Brockmann D
    11. Suchard MA

    (2014) Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2

    PLoS Pathogens 10:e1003932.

    https://doi.org/10.1371/journal.ppat.1003932

    • PubMed
    • Google Scholar
24. 1. Lindblade KA
    2. Steinhardt L
    3. Samuels A
    4. Kachur SP
    5. Slutsker L

    (2013) The silent threat: asymptomatic parasitemia and malaria transmission

    Expert Review of Anti-infective Therapy 11:623–639.

    https://doi.org/10.1586/eri.13.45

    • PubMed
    • Google Scholar
25. 1. MalariaGEN Plasmodium falciparum Community Project
    2. Amato R
    3. Miotto O
    4. Woodrow CJ

    (2016) Genomic epidemiology of artemisinin resistant malaria

    eLife 5:e08714.

    https://doi.org/10.7554/eLife.08714

    • PubMed
    • Google Scholar
26. 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 AV
    21. Asoala V
    22. Imwong M
    23. Takala-Harrison S
    24. Nosten F
    25. Su XZ
    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 JB
    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 CC
    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
27. 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 TN
    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 CC
    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
28. 1. Moonen B
    2. Cohen JM
    3. Snow RW
    4. Slutsker L
    5. Drakeley C
    6. Smith DL
    7. Abeyasinghe RR
    8. Rodriguez MH
    9. Maharaj R
    10. Tanner M
    11. Targett G

    (2010) Operational strategies to achieve and maintain malaria elimination

    The Lancet 376:1592–1603.

    https://doi.org/10.1016/S0140-6736(10)61269-X

    • PubMed
    • Google Scholar
29. 1. Mzilahowa T
    2. McCall PJ
    3. Hastings IM

    (2007) "Sexual" population structure and genetics of the malaria agent P. falciparum

    PLoS One 2:e613.

    https://doi.org/10.1371/journal.pone.0000613

    • PubMed
    • Google Scholar
30. 1. Nelson MI
    2. Viboud C
    3. Vincent AL
    4. Culhane MR
    5. Detmer SE
    6. Wentworth DE
    7. Rambaut A
    8. Suchard MA
    9. Holmes EC
    10. Lemey P

    (2015) Global migration of influenza A viruses in swine

    Nature Communications 6:6696.

    https://doi.org/10.1038/ncomms7696

    • PubMed
    • Google Scholar
31. 1. Obaldia N
    2. Baro NK
    3. Calzada JE
    4. Santamaria AM
    5. Daniels R
    6. Wong W
    7. Chang HH
    8. Hamilton EJ
    9. Arevalo-Herrera M
    10. Herrera S
    11. Wirth DF
    12. Hartl DL
    13. Marti M
    14. Volkman SK

    (2015) Clonal outbreak of Plasmodium falciparum infection in eastern Panama

    Journal of Infectious Diseases 211:1087–1096.

    https://doi.org/10.1093/infdis/jiu575

    • PubMed
    • Google Scholar
32. 1. Patterson N
    2. Price AL
    3. Reich D

    (2006) Population structure and eigenanalysis

    PLoS Genetics 2:e190.

    https://doi.org/10.1371/journal.pgen.0020190

    • PubMed
    • Google Scholar
33. 1. Pritchard JK
    2. Stephens M
    3. Donnelly P

    (2000)

    Inference of population structure using multilocus genotype data

    Genetics 155:945–959.

    • PubMed
    • Google Scholar
34. 1. Rasmussen DA
    2. Volz EM
    3. Koelle K

    (2014) Phylodynamic inference for structured epidemiological models

    PLoS Computational Biology 10:e1003570.

    https://doi.org/10.1371/journal.pcbi.1003570

    • PubMed
    • Google Scholar
35. 1. Rosvall M
    2. Bergstrom CT

    (2011) Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems

    PLoS One 6:e18209.

    https://doi.org/10.1371/journal.pone.0018209

    • PubMed
    • Google Scholar
36. 1. Schaffner SF
    2. Taylor AR
    3. Wong W
    4. Wirth DF
    5. Neafsey DE

    (2018) hmmIBD: software to infer pairwise identity by descent between haploid genotypes

    Malaria Journal 17:196.

    https://doi.org/10.1186/s12936-018-2349-7

    • PubMed
    • Google Scholar
37. 1. Searle KM
    2. Katowa B
    3. Kobayashi T
    4. Siame MNS
    5. Mharakurwa S
    6. Carpi G
    7. Norris DE
    8. Stevenson JC
    9. Thuma PE
    10. Moss WJ
    11. Southern Africa International Centers of Excellence for Malaria Research

    (2017) Distinct parasite populations infect individuals identified through passive and active case detection in a region of declining malaria transmission in southern Zambia

    Malaria Journal 16:154.

    https://doi.org/10.1186/s12936-017-1810-3

    • PubMed
    • Google Scholar
38. 1. Sturrock HJW
    2. Bennett AF
    3. Midekisa A
    4. Gosling RD
    5. Gething PW
    6. Greenhouse B

    (2016) Mapping Malaria Risk in Low Transmission Settings: Challenges and Opportunities

    Trends in Parasitology 32:635–645.

    https://doi.org/10.1016/j.pt.2016.05.001

    • PubMed
    • Google Scholar
39. 1. Taylor AR
    2. Schaffner SF
    3. Cerqueira GC
    4. Nkhoma SC
    5. Anderson TJC
    6. Sriprawat K
    7. Pyae Phyo A
    8. Nosten F
    9. Neafsey DE
    10. Buckee CO

    (2017) Quantifying connectivity between local Plasmodium falciparum malaria parasite populations using identity by descent

    PLOS Genetics 13:e1007065.

    https://doi.org/10.1371/journal.pgen.1007065

    • PubMed
    • Google Scholar
40. 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 TJ
    16. Nair S
    17. McDew-White M
    18. Flegg JA
    19. Grist EP
    20. Guerin P
    21. Maude RJ
    22. Smithuis F
    23. Dondorp AM
    24. Day NP
    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
41. 1. Vaughan TG
    2. Kühnert D
    3. Popinga A
    4. Welch D
    5. Drummond AJ

    (2014) Efficient Bayesian inference under the structured coalescent

    Bioinformatics 30:2272–2279.

    https://doi.org/10.1093/bioinformatics/btu201

    • PubMed
    • Google Scholar
42. 1. Weir BS
    2. Cockerham CC

    (1984) Estimating f-statistics for the analysis of population structure

    Evolution; International Journal of Organic Evolution 38:1358–1370.

    https://doi.org/10.1111/j.1558-5646.1984.tb05657.x

    • PubMed
    • Google Scholar
43. 1. Wesolowski A
    2. Eagle N
    3. Tatem AJ
    4. Smith DL
    5. Noor AM
    6. Snow RW
    7. Buckee CO

    (2012) Quantifying the impact of human mobility on malaria

    Science 338:267–270.

    https://doi.org/10.1126/science.1223467

    • PubMed
    • Google Scholar
44. 1. Wesolowski A
    2. Stresman G
    3. Eagle N
    4. Stevenson J
    5. Owaga C
    6. Marube E
    7. Bousema T
    8. Drakeley C
    9. Cox J
    10. Buckee CO

    (2014) Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones

    Scientific Reports 4:5678.

    https://doi.org/10.1038/srep05678

    • PubMed
    • Google Scholar
45. 1. Wesolowski A
    2. Qureshi T
    3. Boni MF
    4. Sundsøy PR
    5. Johansson MA
    6. Rasheed SB
    7. Engø-Monsen K
    8. Buckee CO

    (2015) Impact of human mobility on the emergence of dengue epidemics in Pakistan

    PNAS 112:11887–11892.

    https://doi.org/10.1073/pnas.1504964112

    • PubMed
    • Google Scholar
46. 1. Wesolowski A
    2. Taylor AR
    3. Chang HH
    4. Verity R
    5. Tessema S
    6. Bailey JA
    7. Alex Perkins T
    8. Neafsey DE
    9. Greenhouse B
    10. Buckee CO

    (2018) Mapping malaria by combining parasite genomic and epidemiologic data

    BMC Medicine 16:190.

    https://doi.org/10.1186/s12916-018-1181-9

    • PubMed
    • Google Scholar
47. Report

    1. WHO

    (2007)

    Malaria Elimination: A Field Manual for Low and Moderate Endemic Countries

    Geneva, Switzerland: World Health Organization.

    • Google Scholar
48. Report

    1. WHO

    (2017a)

    Status Report on Artemisinin and ACT Resistance

    Geneva, Switzerland: World Health Organization.

    • Google Scholar
49. Report

    1. WHO

    (2017b)

    World Malaria Report

    Geneva, Switzerland: World Health Organization.

    • Google Scholar
50. Report

    1. WHO

    (2018)

    Update on the E-2020 Intiative of 21 Malaria-Eliminating Countries

    Geneva, Switzerland: World Health Organization.

    • Google Scholar

Author details

1. Hsiao-Han Chang

   1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States
   2. The Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, United States

   Contribution

   Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Amy Wesolowski, Richard J Maude and Caroline Buckee

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8016-1530

2. Amy Wesolowski

   Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States

   Contribution

   Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Hsiao-Han Chang, Richard J Maude and Caroline Buckee

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6320-3575

3. Ipsita Sinha

   1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
   2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6574-310X

4. Christopher G Jacob

   Wellcome Sanger Institute, Cambridge, United Kingdom

   Contribution

   Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

5. Ayesha Mahmud

   1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States
   2. The Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, United States

   Contribution

   Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

6. Didar Uddin

   Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

   Contribution

   Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

7. Sazid Ibna Zaman

   Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

   Contribution

   Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

8. Md Amir Hossain

   Department of Medicine, Chittagong Medical College, Chittagong, Bangladesh

   Contribution

   Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

9. M Abul Faiz

   1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
   2. Dev Care Foundation, Dhaka, Bangladesh

   Contribution

   Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

10. Aniruddha Ghose

    Chittagong Medical College Hospital, Chittagong, Bangladesh

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

11. Abdullah Abu Sayeed

    Chittagong Medical College Hospital, Chittagong, Bangladesh

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

12. M Ridwanur Rahman

    Shaheed Suhrawardy Medical College, Dhaka, Bangladesh

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

13. Akramul Islam

    BRAC Centre, Dhaka, Bangladesh

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

14. Mohammad Jahirul Karim

    National Malaria Elimination Programme, Dhaka, Bangladesh

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

15. M Kamar Rezwan

    Vector-Borne Disease Control, World Health Organization, Dhaka, Bangladesh

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

16. Abul Khair Mohammad Shamsuzzaman

    Communicable Disease Control, Directorate General of Health Services, Dhaka, Bangladesh

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

17. Sanya Tahmina Jhora

    Communicable Disease Control, Directorate General of Health Services, Dhaka, Bangladesh

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

18. M M Aktaruzzaman

    National Malaria Elimination Programme, Dhaka, Bangladesh

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

19. Eleanor Drury

    Wellcome Sanger Institute, Cambridge, United Kingdom

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

20. Sonia Gonçalves

    Wellcome Sanger Institute, Cambridge, United Kingdom

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

21. Mihir Kekre

    Wellcome Sanger Institute, Cambridge, United Kingdom

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

22. Mehul Dhorda

    1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    3. Worldwide Antimalarial Resistance Network, Asia Regional Centre, Bangkok, Thailand

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

23. Ranitha Vongpromek

    Worldwide Antimalarial Resistance Network, Asia Regional Centre, Bangkok, Thailand

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

24. Olivo Miotto

    1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    2. Wellcome Sanger Institute, Cambridge, United Kingdom
    3. Big Data Institute, Oxford University, Oxford, United Kingdom

    Contribution

    Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

25. Kenth Engø-Monsen

    Telenor Research, Telenor Group, Fornebu, Norway

    Contribution

    Data curation, Formal analysis, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1618-7597

26. Dominic Kwiatkowski

    1. Wellcome Sanger Institute, Cambridge, United Kingdom
    2. Big Data Institute, Oxford University, Oxford, United Kingdom

    Contribution

    Data curation, Supervision, Funding acquisition, Writing—review and editing

    Competing interests

    No competing interests declared

27. Richard J Maude

    1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States
    2. The Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, United States
    3. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    4. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Data curation, Supervision, Project administration, Writing—review and editing

    Contributed equally with

    Hsiao-Han Chang, Amy Wesolowski and Caroline Buckee

    Competing interests

    No competing interests declared

28. Caroline Buckee

    1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States
    2. The Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Writing—original draft, Project administration, Writing—review and editing

    Contributed equally with

    Hsiao-Han Chang, Amy Wesolowski and Richard J Maude

    For correspondence

    cbuckee@hsph.harvard.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8386-5899

• 4,013

  views

• 583

  downloads

• 52

  citations

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