{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T01:43:49Z","timestamp":1784857429958,"version":"3.55.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009287","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000}}],"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["206194"],"award-info":[{"award-number":["206194"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["090770"],"award-info":[{"award-number":["090770"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["204911"],"award-info":[{"award-number":["204911"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["100956\/Z\/13\/Z"],"award-info":[{"award-number":["100956\/Z\/13\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["109107\/Z\/15\/Z"],"award-info":[{"award-number":["109107\/Z\/15\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007421","name":"Li Ka Shing Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007421","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["203141\/Z\/16\/Z"],"award-info":[{"award-number":["203141\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013373","name":"NIHR Oxford Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013373","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>There is an abundance of malaria genetic data being collected from the field, yet using these data to understand the drivers of regional epidemiology remains a challenge. A key issue is the lack of models that relate parasite genetic diversity to epidemiological parameters. Classical models in population genetics characterize changes in genetic diversity in relation to demographic parameters, but fail to account for the unique features of the malaria life cycle. In contrast, epidemiological models, such as the Ross-Macdonald model, capture malaria transmission dynamics but do not consider genetics. Here, we have developed an integrated model encompassing both parasite evolution and regional epidemiology. We achieve this by combining the Ross-Macdonald model with an intra-host continuous-time Moran model, thus explicitly representing the evolution of individual parasite genomes in a traditional epidemiological framework. Implemented as a stochastic simulation, we use the model to explore relationships between measures of parasite genetic diversity and parasite prevalence, a widely-used metric of transmission intensity. First, we explore how varying parasite prevalence influences genetic diversity at equilibrium. We find that multiple genetic diversity statistics are correlated with prevalence, but the strength of the relationships depends on whether variation in prevalence is driven by host- or vector-related factors. Next, we assess the responsiveness of a variety of statistics to malaria control interventions, finding that those related to mixed infections respond quickly (\u223cmonths) whereas other statistics, such as nucleotide diversity, may take decades to respond. These findings provide insights into the opportunities and challenges associated with using genetic data to monitor malaria epidemiology.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009287","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T15:07:01Z","timestamp":1629385621000},"page":"e1009287","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":29,"title":["Elucidating relationships between P.falciparum prevalence and measures of genetic diversity with a combined genetic-epidemiological model of malaria"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4164-3179","authenticated-orcid":true,"given":"Jason A.","family":"Hendry","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5023-0176","authenticated-orcid":true,"given":"Dominic","family":"Kwiatkowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5012-4162","authenticated-orcid":true,"given":"Gil","family":"McVean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"issue":"5","key":"pcbi.1009287.ref001","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1038\/nrg3187","article-title":"Harnessing genomics and genome biology to understand malaria biology","volume":"13","author":"SK Volkman","year":"2012","journal-title":"Nat Rev Genet"},{"issue":"22","key":"pcbi.1009287.ref002","doi-asserted-by":"crossref","first-page":"6782","DOI":"10.1073\/pnas.1507727112","article-title":"Malaria genotyping for epidemiologic surveillance","volume":"112","author":"B Greenhouse","year":"2015","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"3 Suppl","key":"pcbi.1009287.ref003","doi-asserted-by":"crossref","first-page":"79","DOI":"10.4269\/ajtmh.15-0005","article-title":"Malaria Molecular Epidemiology: Lessons from the International Centers of Excellence for Malaria Research Network","volume":"93","author":"AA Escalante","year":"2015","journal-title":"Am J Trop Med Hyg"},{"issue":"8","key":"pcbi.1009287.ref004","doi-asserted-by":"crossref","DOI":"10.1101\/cshperspect.a025544","article-title":"Malaria Genomics in the Era of Eradication","volume":"7","author":"DE Neafsey","year":"2017","journal-title":"Cold Spring Harb Perspect Med"},{"issue":"2","key":"pcbi.1009287.ref005","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ijpara.2016.08.006","article-title":"Dissecting malaria biology and epidemiology using population genetics and genomics","volume":"47","author":"S Auburn","year":"2017","journal-title":"International Journal for Parasitology"},{"issue":"1","key":"pcbi.1009287.ref006","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1186\/s12916-018-1181-9","article-title":"Mapping malaria by combining parasite genomic and epidemiologic data","volume":"16","author":"A Wesolowski","year":"2018","journal-title":"BMC Medicine"},{"issue":"1","key":"pcbi.1009287.ref007","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1186\/s12936-019-2880-1","article-title":"Applying next-generation sequencing to track falciparum malaria in sub-Saharan Africa","volume":"18","author":"SK Tessema","year":"2019","journal-title":"Malaria Journal"},{"key":"pcbi.1009287.ref008","unstructured":"WHO. 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