ArticleScientific reports2020
Supporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Reactive case detection can improve the efficiency of lymphatic filariasis surveillance compared to random sampling, Samoa 2023.PLoS neglected tropical diseases · 2025Article
- Progression of lymphatic filariasis antigenaemia and microfilaraemia over 4.5 years in antigen-positive individuals, Samoa 2019-2023.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025Article
- The Utility of Infectious Disease Prevalence Studies to Inform Public Health Decision-Making in the Samoan Islands: A Systematic Review.Tropical medicine and infectious disease · 2025Review
- Epidemiology of Lymphatic Filariasis Antigen and Microfilaria in Samoa, 2019: 7-9 Months Post Triple-Drug Mass Administration.Tropical medicine and infectious disease · 2024Article
- Scabies prevalence after ivermectin-based mass drug administration for lymphatic filariasis, Samoa 2018-2019.PLoS neglected tropical diseases · 2023Article
- Artificial intelligence and lymphedema: State of the art.Journal of clinical and translational research · 2022Review
- Lymphatic filariasis epidemiology in Samoa in 2018: Geographic clustering and higher antigen prevalence in older age groups.PLoS neglected tropical diseases · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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