Supporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics

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dc.contributor.authorMayfield, Helen
dc.contributor.authorSturrock, Hugh
dc.contributor.authorArnold, Benjamin F.
dc.contributor.authorAndrade-Pacheco, Ricardo
dc.contributor.authorKearns, Therese
dc.contributor.authorGraves, Patricia
dc.contributor.authorNaseri, Take
dc.contributor.authorThomsen, Robert
dc.contributor.authorGass, Katherine
dc.contributor.authorLau, Colleen
dc.date.accessioned2022-10-11T00:51:59Z
dc.date.available2022-10-11T00:51:59Z
dc.date.issued2020
dc.date.updated2021-11-28T07:22:22Z
dc.description.abstractThe global elimination of lymphatic filariasis (LF) is a major focus of the World Health Organization. One key challenge is locating residual infections that can perpetuate the transmission cycle. We show how a targeted sampling strategy using predictions from a geospatial model, combining random forests and geostatistics, can improve the sampling efficiency for identifying locations with high infection prevalence. Predictions were made based on the household locations of infected persons identified from previous surveys, and environmental variables relevant to mosquito density. Results show that targeting sampling using model predictions would have allowed 52% of infections to be identified by sampling just 17.7% of households. The odds ratio for identifying an infected individual in a household at a predicted high risk compared to a predicted low risk location was 10.2 (95% CI 4.2–22.8). This study provides evidence that a ‘one size fits all’ approach is unlikely to yield optimal results when making programmatic decisions based on model predictions. Instead, model assumptions and definitions should be tailored to each situation based on the objective of the surveillance program. When predictions are used in the context of the program objectives, they can result in a dramatic improvement in the efficiency of locating infected individuals.en_AU
dc.description.sponsorshipTis work received fnancial support from the Coalition for Operational Research on Neglected Tropical Diseases, which is funded at Te Task Force for Global Health primarily by the Bill & Melinda Gates Foundation, by the United States Agency for International Development through its Neglected Tropical Diseases Program, and with UK aid from the British people. Colleen Lau was supported by an Australian National Health and Medical Research Council (NHMRC) Fellowship (1109035).en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn2045-2322en_AU
dc.identifier.urihttp://hdl.handle.net/1885/274427
dc.language.isoen_AUen_AU
dc.provenanceTis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_AU
dc.publisherNature Publishing Groupen_AU
dc.relationhttp://purl.org/au-research/grants/nhmrc/1109035en_AU
dc.rights© The Author(s) 2020en_AU
dc.rights.licenseCreative Commons Attribution 4.0 International Licenseen_AU
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_AU
dc.sourceScientific Reportsen_AU
dc.titleSupporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatisticsen_AU
dc.typeJournal articleen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.issue1en_AU
local.bibliographicCitation.lastpage11en_AU
local.bibliographicCitation.startpage1en_AU
local.contributor.affiliationMayfield, Helen, College of Health and Medicine, ANUen_AU
local.contributor.affiliationSturrock, Hugh, University of Californiaen_AU
local.contributor.affiliationArnold, Benjamin F., University of Californiaen_AU
local.contributor.affiliationAndrade-Pacheco, Ricardo, University of Californiaen_AU
local.contributor.affiliationKearns, Therese, Charles Darwin Universityen_AU
local.contributor.affiliationGraves, Patricia, James Cook Universityen_AU
local.contributor.affiliationNaseri, Take , Ministry of Health, Samoaen_AU
local.contributor.affiliationThomsen , Robert , Ministry of Health, Samoaen_AU
local.contributor.affiliationGass , Katherine , Neglected Tropical Disease Support Centreen_AU
local.contributor.affiliationLau, Colleen, College of Health and Medicine, ANUen_AU
local.contributor.authoremailu5651486@anu.edu.auen_AU
local.contributor.authoruidMayfield, Helen, u1028048en_AU
local.contributor.authoruidLau, Colleen, u5651486en_AU
local.description.notesImported from ARIESen_AU
local.identifier.absfor000000 - Internal ANU use onlyen_AU
local.identifier.ariespublicationa383154xPUB15969en_AU
local.identifier.citationvolume10en_AU
local.identifier.doi10.1038/s41598-020-77519-8en_AU
local.identifier.scopusID2-s2.0-85096622255
local.identifier.uidSubmittedBya383154en_AU
local.publisher.urlhttps://www.nature.com/en_AU
local.type.statusPublished Versionen_AU

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