Lineage-informative microhaplotypes for recurrence classification and spatio-temporal surveillance of Plasmodium vivax malaria parasites.

Challenges in classifying recurrent Plasmodium vivax infections constrain surveillance of antimalarial efficacy and transmission. Recurrent infections may arise from activation of dormant liver stages (relapse), blood-stage treatment failure (recrudescence) or reinfection. Molecular inference of familial relatedness (identity-by-descent or IBD) can help resolve the probable origin of recurrences. As whole genome sequencing of P. vivax remains challenging, targeted genotyping methods are needed for scalability. We describe a P. vivax marker discovery framework to identify and select panels of microhaplotypes (multi-allelic markers within small, amplifiable segments of the genome) that can accurately capture IBD. We evaluate panels of 50-250 microhaplotypes discovered in a global set of 615 P. vivax genomes. A candidate global 100-microhaplotype panel exhibits high marker diversity in the Asia-Pacific, Latin America and horn of Africa (median HE = 0.70-0.81) and identifies 89% of the polyclonal infections detected with genome-wide datasets. Data simulations reveal lower error in estimating pairwise IBD using microhaplotypes relative to traditional biallelic SNP barcodes. The candidate global panel also exhibits high accuracy in predicting geographic origin and captures local infection outbreak and bottlenecking events. Our framework is open-source enabling customised microhaplotype discovery and selection, with potential for porting to other species or data resources.

Using serological diagnostics to characterize remaining high-incidence pockets of malaria in forest-fringe Cambodia.

BACKGROUND: Over the last decades, the number of malaria cases has drastically reduced in Cambodia. As the overall prevalence of malaria in Cambodia declines, residual malaria transmission becomes increasingly fragmented over smaller remote regions. The aim of this study was to get an insight into the burden and epidemiological parameters of Plasmodium infections on the forest-fringe of Cambodia. METHODS: 950 participants were recruited in the province of Mondulkiri in Cambodia and followed up from 2018 to 2020. Whole-blood samples were processed for Plasmodium spp. identification by PCR as well as for a serological immunoassay. A risk factor analysis was conducted for Plasmodium vivax PCR-detected infections throughout the study, and for P. vivax seropositivity at baseline. To evaluate the predictive effect of seropositivity at baseline on subsequent PCR-positivity, an analysis of P. vivax infection-free survival time stratified by serological status at baseline was performed. RESULTS: Living inside the forest significantly increased the odds of P. vivax PCR-positivity by a factor of 18.3 (95% C.I. 7.7-43.5). Being a male adult was also a significant predictor of PCR-positivity. Similar risk profiles were identified for P. vivax seropositivity. The survival analysis showed that serological status at baseline significantly correlated with subsequent infection. Serology is most informative outside of the forest, where 94.0% (95% C.I. 90.7-97.4%) of seronegative individuals survived infection-free, compared to 32.4% (95% C.I.: 22.6-46.6%) of seropositive individuals. CONCLUSION: This study justifies the need for serological diagnostic assays to target interventions in this region, particularly in demographic groups where a lot of risk heterogeneity persists, such as outside of the forest.

Retrospective study and outcome of 307 cats with feline infectious peritonitis treated with legally sourced veterinary compounded preparations of remdesivir and GS-441524 (2020-2022).

OBJECTIVES: Feline infectious peritonitis (FIP) is a serious disease that arises due to feline coronavirus infection. The nucleoside analogues remdesivir and GS-441524 can be effective in its treatment, but most studies have used unregulated products of unknown composition. The aim of the present study was to describe the treatment of FIP using legally sourced veterinary-prescribed regulated veterinary compounded products containing known amounts of remdesivir (injectable) or GS-441524 (oral tablets). METHODS: Cats were recruited via email advice services, product sales contacts and study publicity. Cats were excluded if they were deemed unlikely to have FIP, were not treated exclusively with the veterinary compounded products, or if there was a lack of cat and/or treatment (including response) data. Extensive cat and treatment data were collected. RESULTS: Among the 307 cats recruited, the predominant type of FIP was most commonly abdominal effusive (49.5%) and then neurological (14.3%). Three treatment protocols were used; remdesivir alone (33.9%), remdesivir followed by GS-441524 (55.7%) and GS-441524 alone (10.4%). The median (range) initial treatment period duration and longest follow-up time point after starting treatment were 84 (1-330) days and 248 (1-814) days, respectively. The most common side effect was injection pain (in 47.8% of those given subcutaneous remdesivir). Of the 307 cats, 33 (10.8%) relapsed, 15 (45.5%) during and 18 (54.5%) after the initial treatment period. At the longest follow-up time point after completion of the initial treatment period, 84.4% of cats were alive. The cats achieving a complete response within 30 days of starting treatment were significantly more likely to be alive at the end of the initial treatment period than those cats that did not. CONCLUSIONS AND RELEVANCE: Legally sourced remdesivir and GS-441524 products, either alone or used sequentially, were very effective in the treatment of FIP in this group of cats. Variable protocols precluded statistical comparison of treatment regimens.

Lineage-informative microhaplotypes for spatio-temporal surveillance of Plasmodium vivax malaria parasites.

Challenges in understanding the origin of recurrent Plasmodium vivax infections constrains the surveillance of antimalarial efficacy and transmission of this neglected parasite. Recurrent infections within an individual may arise from activation of dormant liver stages (relapse), blood-stage treatment failure (recrudescence) or new inoculations (reinfection). Molecular inference of familial relatedness (identity-by-descent or IBD) based on whole genome sequence data, together with analysis of the intervals between parasitaemic episodes ("time-to-event" analysis), can help resolve the probable origin of recurrences. Whole genome sequencing of predominantly low-density P. vivax infections is challenging, so an accurate and scalable genotyping method to determine the origins of recurrent parasitaemia would be of significant benefit. We have developed a P. vivax genome-wide informatics pipeline to select specific microhaplotype panels that can capture IBD within small, amplifiable segments of the genome. Using a global set of 615 P. vivax genomes, we derived a panel of 100 microhaplotypes, each comprising 3-10 high frequency SNPs within <200 bp sequence windows. This panel exhibits high diversity in regions of the Asia-Pacific, Latin America and the horn of Africa (median HE = 0.70-0.81) and it captured 89% (273/307) of the polyclonal infections detected with genome-wide datasets. Using data simulations, we demonstrate lower error in estimating pairwise IBD using microhaplotypes, relative to traditional biallelic SNP barcodes. Our panel exhibited high accuracy in predicting the country of origin (median Matthew's correlation coefficient >0.9 in 90% countries tested) and it also captured local infection outbreak and bottlenecking events. The informatics pipeline is available open-source and yields microhaplotypes that can be readily transferred to high-throughput amplicon sequencing assays for surveillance in malaria-endemic regions.

Measurably recombining malaria parasites.

Genomic epidemiology has guided research and policy for various viral pathogens and there has been a parallel effort towards using genomic epidemiology to combat diseases that are caused by eukaryotic pathogens, such as the malaria parasite. However, the central concept of viral genomic epidemiology, namely that of measurably mutating pathogens, does not apply easily to sexually recombining parasites. Here we introduce the related but different concept of measurably recombining malaria parasites to promote convergence around a unifying theoretical framework for malaria genomic epidemiology. Akin to viral phylodynamics, we anticipate that an inferential framework developed around recombination will help guide practical research and thus realize the full public health potential of genomic epidemiology for malaria parasites and other sexually recombining pathogens.

Resolving drug selection and migration in an inbred South American Plasmodium falciparum population with identity-by-descent analysis.

The human malaria parasite Plasmodium falciparum is globally widespread, but its prevalence varies significantly between and even within countries. Most population genetic studies in P. falciparum focus on regions of high transmission where parasite populations are large and genetically diverse, such as sub-Saharan Africa. Understanding population dynamics in low transmission settings, however, is of particular importance as these are often where drug resistance first evolves. Here, we use the Pacific Coast of Colombia and Ecuador as a model for understanding the population structure and evolution of Plasmodium parasites in small populations harboring less genetic diversity. The combination of low transmission and a high proportion of monoclonal infections means there are few outcrossing events and clonal lineages persist for long periods of time. Yet despite this, the population is evolutionarily labile and has successfully adapted to changes in drug regime. Using newly sequenced whole genomes, we measure relatedness between 166 parasites, calculated as identity by descent (IBD), and find 17 distinct but highly related clonal lineages, six of which have persisted in the region for at least a decade. This inbred population structure is captured in more detail with IBD than with other common population structure analyses like PCA, ADMIXTURE, and distance-based trees. We additionally use patterns of intra-chromosomal IBD and an analysis of haplotypic variation to explore past selection events in the region. Two genes associated with chloroquine resistance, crt and aat1, show evidence of hard selective sweeps, while selection appears soft and/or incomplete at three other key resistance loci (dhps, mdr1, and dhfr). Overall, this work highlights the strength of IBD analyses for studying parasite population structure and resistance evolution in regions of low transmission, and emphasizes that drug resistance can evolve and spread in small populations, as will occur in any region nearing malaria elimination.

Design and implementation of multiplexed amplicon sequencing panels to serve genomic epidemiology of infectious disease: A malaria case study.

Multiplexed PCR amplicon sequencing (AmpSeq) is an increasingly popular application for cost-effective monitoring of threatened species and managed wildlife populations, and shows strong potential for the genomic epidemiology of infectious disease. AmpSeq data from infectious microbes can inform disease control in multiple ways, such as by measuring drug resistance marker prevalence, distinguishing imported from local cases, and determining the effectiveness of therapeutics. We describe the design and comparative evaluation of two new AmpSeq assays for Plasmodium falciparum malaria parasites: a four-locus panel ("4CAST") composed of highly diverse antigens, and a 129-locus panel ("AMPLseq") composed of drug resistance markers, highly diverse loci for inferring relatedness, and a locus to detect Plasmodium vivax co-infection. We explore the performance of each panel in various public health use cases with in silico simulations as well as empirical experiments. The 4CAST panel appears highly suitable for evaluating the number of distinct parasite strains within samples (complexity of infection), showing strong performance across a wide range of parasitaemia levels without a DNA pre-amplification step. For relatedness inference, the larger AMPLseq panel performs similarly to two existing panels of comparable size, despite differences in the data and approach used for designing each panel. Finally, we describe an R package (paneljudge) that facilitates the design and comparative evaluation of genetic panels for relatedness estimation, and we provide general guidance on the design and implementation of AmpSeq panels for the genomic epidemiology of infectious disease.

Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study.

BACKGROUND: In early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA. METHODS: In this population-based study, we collected epidemiological data on COVID-19 cases and deaths, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems, from 1396 counties across the USA that had at least 100 laboratory-confirmed cases of COVID-19. We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural, and quantified the changes in COVID-19 transmission using estimates of the effective reproduction number (Rt) between Jan 22 and July 9, 2020, to investigate the relationship between aggregated mobility metrics and epidemic trajectory. For each county, we model the time series of Rt values with mobility proxies. FINDINGS: We show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties (0·757 [95% CI 0·689 to 0·857]), but this relationship primarily holds for counties in the three most urban categories as defined by the NCHS. This relationship weakens considerably after the initial 15 weeks of the epidemic (0·442 [-0·492 to -0·392]), consistent with the emergence of more complex local policies and behaviours, including masking. INTERPRETATION: Our study shows that the integration of mobility metrics into retrospective modelling efforts can be useful in identifying links between these metrics and Rt. Importantly, we highlight potential issues in the data generation process for transmission indicators derived from mobile phone data, representativeness, and equity of access, which must be addressed to improve the interpretability of these data in public health. FUNDING: There was no funding source for this study.

Advances and opportunities in malaria population genomics.

Almost 20 years have passed since the first reference genome assemblies were published for Plasmodium falciparum, the deadliest malaria parasite, and Anopheles gambiae, the most important mosquito vector of malaria in sub-Saharan Africa. Reference genomes now exist for all human malaria parasites and nearly half of the ~40 important vectors around the world. As a foundation for genetic diversity studies, these reference genomes have helped advance our understanding of basic disease biology and drug and insecticide resistance, and have informed vaccine development efforts. Population genomic data are increasingly being used to guide our understanding of malaria epidemiology, for example by assessing connectivity between populations and the efficacy of parasite and vector interventions. The potential value of these applications to malaria control strategies, together with the increasing diversity of genomic data types and contexts in which data are being generated, raise both opportunities and challenges in the field. This Review discusses advances in malaria genomics and explores how population genomic data could be harnessed to further support global disease control efforts.

Revisiting the adequacy of infection criteria recommended in VICH GL 7 for anthelmintic effectiveness studies: Retrospective simulations.

Studies conducted to support registration or approval of veterinary anthelmintics generally follow study design recommendations provided by the VICH (International Cooperation on Harmonisation of Technical Requirements for Registration of Veterinary Medicinal Products), "Efficacy of Anthelmintics: General Requirements" (VICH GL7). For dose confirmation studies, VICH GL7 provides recommendations for determining that the control animals had an adequate infection "to permit the appropriate standards of efficacy to be met with acceptable statistical and biological certitude/confidence." In the simulation studies described in this report, we investigated the performance of one method, the statistical criterion given in Section 4.5 of VICH GL7, for evaluating the adequacy of infection in anthelmintic studies, in combination with the conventional criterion of a minimum of six adequately infected animals. We conducted numerical simulations, based on parasite data from previously conducted dose confirmation studies in dogs and cattle, to investigate how the statistical criterion impacts adequacy of infection determinations when used with the conventional criterion at various sample sizes. Simulation studies in common nematode species in both dogs and cattle indicated that under certain circumstances the statistical criterion can guard against overinterpreting the evaluation of adequacy of infection as sample size is increased. However, the statistical criterion may be overly restrictive for samples with adequate infection but containing multiple zero parasite counts and adding it to the conventional criterion does not provide any additional benefit when the sample contains no zero counts. It is important for investigators designing efficacy studies to understand the potential impact this criterion may have when establishing adequacy of infection criteria in study protocols.

Identity-by-descent with uncertainty characterises connectivity of Plasmodium falciparum populations on the Colombian-Pacific coast.

Characterising connectivity between geographically separated biological populations is a common goal in many fields. Recent approaches to understanding connectivity between malaria parasite populations, with implications for disease control efforts, have used estimates of relatedness based on identity-by-descent (IBD). However, uncertainty around estimated relatedness has not been accounted for. IBD-based relatedness estimates with uncertainty were computed for pairs of monoclonal Plasmodium falciparum samples collected from five cities on the Colombian-Pacific coast where long-term clonal propagation of P. falciparum is frequent. The cities include two official ports, Buenaventura and Tumaco, that are separated geographically but connected by frequent marine traffic. Fractions of highly-related sample pairs (whose classification using a threshold accounts for uncertainty) were greater within cities versus between. However, based on both highly-related fractions and on a threshold-free approach (Wasserstein distances between parasite populations) connectivity between Buenaventura and Tumaco was disproportionally high. Buenaventura-Tumaco connectivity was consistent with transmission events involving parasites from five clonal components (groups of statistically indistinguishable parasites identified under a graph theoretic framework). To conclude, P. falciparum population connectivity on the Colombian-Pacific coast abides by accessibility not isolation-by-distance, potentially implicating marine traffic in malaria transmission with opportunities for targeted intervention. Further investigations are required to test this hypothesis. For the first time in malaria epidemiology (and to our knowledge in ecological and epidemiological studies more generally), we account for uncertainty around estimated relatedness (an important consideration for studies that plan to use genotype versus whole genome sequence data to estimate IBD-based relatedness); we also use threshold-free methods to compare parasite populations and identify clonal components. Threshold-free methods are especially important in analyses of malaria parasites and other recombining organisms with mixed mating systems where thresholds do not have clear interpretation (e.g. due to clonal propagation) and thus undermine the cross-comparison of studies.

A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices.

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results.

Quantifying bias of COVID-19 prevalence and severity estimates in Wuhan, China that depend on reported cases in international travelers.

Risk of COVID-19 infection in Wuhan has been estimated using imported case counts of international travelers, often under the assumption that all cases in travelers are ascertained. Recent work indicates variation among countries in detection capacity for imported cases. Singapore has historically had very strong epidemiological surveillance and contact-tracing capacity and has shown in the COVID-19 epidemic evidence of a high sensitivity of case detection. We therefore used a Bayesian modeling approach to estimate the relative imported case detection capacity for other countries compared to that of Singapore. We estimate that the global ability to detect imported cases is 38% (95% HPDI 22% - 64%) of Singapore's capacity. Equivalently, an estimate of 2.8 (95% HPDI 1.5 - 4.4) times the current number of imported cases, could have been detected, if all countries had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, we found that the ability to detect imported cases relative to Singapore among high surveillance locations is 40% (95% HPDI 22% - 67%), among intermediate surveillance locations it is 37% (95% HPDI 18% - 68%), and among low surveillance locations it is 11% (95% HPDI 0% - 42%). Using a simple mathematical model, we further find that treating all travelers as if they were residents (rather than accounting for the brief stay of some of these travelers in Wuhan) can modestly contribute to underestimation of prevalence as well. We conclude that estimates of case counts in Wuhan based on assumptions of perfect detection in travelers may be underestimated by several fold, and severity correspondingly overestimated by several fold. Undetected cases are likely in countries around the world, with greater risk in countries of low detection capacity and high connectivity to the epicenter of the outbreak.

Using observational data to quantify bias of traveller-derived COVID-19 prevalence estimates in Wuhan, China.

BACKGROUND: The incidence of coronavirus disease 2019 (COVID-19) in Wuhan, China, has been estimated using imported case counts of international travellers, generally under the assumptions that all cases of the disease in travellers have been ascertained and that infection prevalence in travellers and residents is the same. However, findings indicate variation among locations in the capacity for detection of imported cases. Singapore has had very strong epidemiological surveillance and contact tracing capacity during previous infectious disease outbreaks and has consistently shown high sensitivity of case-detection during the COVID-19 outbreak. METHODS: We used a Bayesian modelling approach to estimate the relative capacity for detection of imported cases of COVID-19 for 194 locations (excluding China) compared with that for Singapore. We also built a simple mathematical model of the point prevalence of infection in visitors to an epicentre relative to that in residents. FINDINGS: The weighted global ability to detect Wuhan-to-location imported cases of COVID-19 was estimated to be 38% (95% highest posterior density interval [HPDI] 22-64) of Singapore's capacity. This value is equivalent to 2·8 (95% HPDI 1·5-4·4) times the current number of imported and reported cases that could have been detected if all locations had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, the ability to detect imported cases relative to Singapore was 40% (95% HPDI 22-67) among locations with high surveillance capacity, 37% (18-68) among locations with medium surveillance capacity, and 11% (0-42) among locations with low surveillance capacity. Treating all travellers as if they were residents (rather than accounting for the brief stay of some of these travellers in Wuhan) contributed modestly to underestimation of prevalence. INTERPRETATION: Estimates of case counts in Wuhan based on assumptions of 100% detection in travellers could have been underestimated by several fold. Furthermore, severity estimates will be inflated several fold since they also rely on case count estimates. Finally, our model supports evidence that underdetected cases of COVID-19 have probably spread in most locations around the world, with greatest risk in locations of low detection capacity and high connectivity to the epicentre of the outbreak. FUNDING: US National Institute of General Medical Sciences, and Fellowship Foundation Ramon Areces.

Identifying Locations with Possible Undetected Imported Severe Acute Respiratory Syndrome Coronavirus 2 Cases by Using Importation Predictions.

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exported from mainland China could lead to self-sustained outbreaks in other countries. By February 2020, several countries were reporting imported SARS-CoV-2 cases. To contain the virus, early detection of imported SARS-CoV-2 cases is critical. We used air travel volume estimates from Wuhan, China, to international destinations and a generalized linear regression model to identify locations that could have undetected imported cases. Our model can be adjusted to account for exportation of cases from other locations as the virus spreads and more information on importations and transmission becomes available. Early detection and appropriate control measures can reduce the risk for transmission in all locations.

A molecular barcode to inform the geographical origin and transmission dynamics of Plasmodium vivax malaria.

Although Plasmodium vivax parasites are the predominant cause of malaria outside of sub-Saharan Africa, they not always prioritised by elimination programmes. P. vivax is resilient and poses challenges through its ability to re-emerge from dormancy in the human liver. With observed growing drug-resistance and the increasing reports of life-threatening infections, new tools to inform elimination efforts are needed. In order to halt transmission, we need to better understand the dynamics of transmission, the movement of parasites, and the reservoirs of infection in order to design targeted interventions. The use of molecular genetics and epidemiology for tracking and studying malaria parasite populations has been applied successfully in P. falciparum species and here we sought to develop a molecular genetic tool for P. vivax. By assembling the largest set of P. vivax whole genome sequences (n = 433) spanning 17 countries, and applying a machine learning approach, we created a 71 SNP barcode with high predictive ability to identify geographic origin (91.4%). Further, due to the inclusion of markers for within population variability, the barcode may also distinguish local transmission networks. By using P. vivax data from a low-transmission setting in Malaysia, we demonstrate the potential ability to infer outbreak events. By characterising the barcoding SNP genotypes in P. vivax DNA sourced from UK travellers (n = 132) to ten malaria endemic countries predominantly not used in the barcode construction, we correctly predicted the geographic region of infection origin. Overall, the 71 SNP barcode outperforms previously published genotyping methods and when rolled-out within new portable platforms, is likely to be an invaluable tool for informing targeted interventions towards elimination of this resilient human malaria.

Resolving the cause of recurrent Plasmodium vivax malaria probabilistically.

Relapses arising from dormant liver-stage Plasmodium vivax parasites (hypnozoites) are a major cause of vivax malaria. However, in endemic areas, a recurrent blood-stage infection following treatment can be hypnozoite-derived (relapse), a blood-stage treatment failure (recrudescence), or a newly acquired infection (reinfection). Each of these requires a different prevention strategy, but it was not previously possible to distinguish between them reliably. We show that individual vivax malaria recurrences can be characterised probabilistically by combined modelling of time-to-event and genetic data within a framework incorporating identity-by-descent. Analysis of pooled patient data on 1441 recurrent P. vivax infections in 1299 patients on the Thailand-Myanmar border observed over 1000 patient follow-up years shows that, without primaquine radical curative treatment, 3 in 4 patients relapse. In contrast, after supervised high-dose primaquine only 1 in 40 relapse. In this region of frequent relapsing P. vivax, failure rates after supervised high-dose primaquine are significantly lower (∼3%) than estimated previously.

Estimating Relatedness Between Malaria Parasites.

Understanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. Therefore, it remains unclear how to compare different studies and which measures to use. Here, we systematically compare measures based on identity-by-state (IBS) and identity-by-descent (IBD) using a globally diverse data set of malaria parasites, Plasmodium falciparum and P. vivax, and provide marker requirements for estimates based on IBD. We formally show that the informativeness of polyallelic markers for relatedness inference is maximized when alleles are equifrequent. Estimates based on IBS are sensitive to allele frequencies, which vary across populations and by experimental design. For portability across studies, we thus recommend estimates based on IBD. To generate estimates with errors below an arbitrary threshold of 0.1, we recommend ∼100 polyallelic or 200 biallelic markers. Marker requirements are immediately applicable to haploid malaria parasites and other haploid eukaryotes. C.I.s facilitate comparison when different marker sets are used. This is the first attempt to provide rigorous analysis of the reliability of, and requirements for, relatedness inference in malaria genetic epidemiology. We hope it will provide a basis for statistically informed prospective study design and surveillance strategies.