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

Mayfield, Helen J., Sturrock, Hugh, Arnold, Benjamin F., Andrade‑Pacheco, Ricardo, Kearns, Therese, Graves, Patricia, Naseri, Take, Thomsen, Robert, Gass, Katherine, and Lau, Colleen L. (2020) Supporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics. Scientific Reports, 10. 20570.

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The 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.

Item ID: 66292
Item Type: Article (Research - C1)
ISSN: 2045-2322
Copyright Information: This 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. The 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 © The Author(s) 2020
Funders: Coalition for Operational Research on Neglected Tropical Diseases (CORNTD), Bill and Melinda Gates Foundation (BMGF), United States Agency for International Development (USAID), UKaid, Australian National Health and Medical Research Council (NHMRC)
Projects and Grants: USAID Neglected Tropical Diseases Program, NHMRC Fellowship 1109035
Date Deposited: 01 Mar 2021 02:50
FoR Codes: 42 HEALTH SCIENCES > 4202 Epidemiology > 420202 Disease surveillance @ 34%
45 INDIGENOUS STUDIES > 4516 Pacific Peoples health and wellbeing > 451605 Pacific Peoples epidemiology @ 33%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3207 Medical microbiology > 320704 Medical parasitology @ 33%
SEO Codes: 20 HEALTH > 2003 Provision of health and support services > 200303 Health surveillance @ 50%
20 HEALTH > 2005 Specific population health (excl. Indigenous health) > 200599 Specific population health (excl. Indigenous health) not elsewhere classified @ 50%
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