{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T03:48:11Z","timestamp":1784000891741,"version":"3.55.0"},"update-to":[{"DOI":"10.1371\/journal.pgen.1009335","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000}}],"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32AI007061"],"award-info":[{"award-number":["T32AI007061"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM12471"],"award-info":[{"award-number":["R35GM12471"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007225","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["MOST 110-2636-B-007-009"],"award-info":[{"award-number":["MOST 110-2636-B-007-009"]}],"id":[{"id":"10.13039\/100007225","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosgenetics.org"],"crossmark-restriction":false},"short-container-title":["PLoS Genet"],"abstract":"<jats:p>\n                    Measuring gene flow between malaria parasite populations in different geographic locations can provide strategic information for malaria control interventions. Multiple important questions pertaining to the design of such studies remain unanswered, limiting efforts to operationalize genomic surveillance tools for routine public health use. This report examines the use of population-level summaries of genetic divergence (\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>ST<\/jats:italic>\n                    <\/jats:sub>\n                    ) and relatedness (identity-by-descent) to distinguish levels of gene flow between malaria populations, focused on field-relevant questions about data size, sampling, and interpretability of observations from genomic surveillance studies. To do this, we use\n                    <jats:italic>P. falciparum<\/jats:italic>\n                    whole genome sequence data and simulated sequence data approximating malaria populations evolving under different current and historical epidemiological conditions. We employ mobile-phone associated mobility data to estimate parasite migration rates over different spatial scales and use this to inform our analysis. This analysis underscores the complementary nature of divergence- and relatedness-based metrics for distinguishing gene flow over different temporal and spatial scales and characterizes the data requirements for using these metrics in different contexts. Our results have implications for the design and implementation of malaria genomic surveillance studies.\n                  <\/jats:p>","DOI":"10.1371\/journal.pgen.1009335","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T14:09:07Z","timestamp":1640009347000},"page":"e1009335","update-policy":"https:\/\/doi.org\/10.1371\/journal.pgen.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["Distinguishing gene flow between malaria parasite populations"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2559-2789","authenticated-orcid":true,"given":"Tyler 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