ArticleParasites & vectors2018
Environmental suitability for lymphatic filariasis in Nigeria.
Article in Parasites & vectors, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Modelling approaches for predicting the distribution of skin NTDs: A systematic review.PLoS neglected tropical diseases · 2026Pooled it
- Potential of machine learning for prevention and control of neglected tropical diseases: a scoping review.Communications medicine · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Article
- Bayesian hierarchical spatial ecological model assessing the nexus of vegetation index, insecticide-treated bed-nets and livestock cattle density on lymphatic filariasis transmission.BMC public health · 2025Article
- Geospatial analysis ofJournal of parasitic diseases : official organ of the Indian Society for Parasitology · 2025Article
- Slaying the Serpent: A Research Agenda to Expand Intervention Development and Accelerate Guinea Worm Eradication Efforts.The American journal of tropical medicine and hygiene · 2024Article
- Towards Understanding the Microepidemiology of Lymphatic Filariasis at the Community Level in Ghana.Tropical medicine and infectious disease · 2024Article
- The national distribution of lymphatic filariasis cases in Malawi using patient mapping and geostatistical modelling.PLoS neglected tropical diseases · 2024Article
- Geospatial modelling of lymphatic filariasis and malaria co-endemicity in Nigeria.International health · 2023Article
- Spatial predictive risk mapping of lymphatic filariasis residual hotspots in American Samoa using demographic and environmental factors.PLoS neglected tropical diseases · 2023Article
- Spatially Explicit Environmental Factors Associated with Lymphatic Filariasis Infection in American Samoa.Tropical medicine and infectious disease · 2022Article
- Artificial intelligence and lymphedema: State of the art.Journal of clinical and translational research · 2022Review
- Exploring agricultural land-use and childhood malaria associations in sub-Saharan Africa.Scientific reports · 2022Article
- Progress towards onchocerciasis elimination in Côte d'Ivoire: A geospatial modelling study.PLoS neglected tropical diseases · 2021Article
- Spatial variation in lymphatic filariasis risk factors of hotspot zones in Ghana.BMC public health · 2021Article
- Elimination of Lymphatic Filariasis in Shandong Province, China, 1957-2015.Vector borne and zoonotic diseases (Larchmont, N.Y.) · 2020Review
- Informing Rift Valley Fever preparedness by mapping seasonally varying environmental suitability.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2020Article
- Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models.International journal of environmental research and public health · 2019Article
- Mapping the baseline prevalence of lymphatic filariasis across Nigeria.Parasites & vectors · 2019Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundLymphatic filariasis (LF) is a mosquito-borne parasitic disease and a major cause of disability worldwide. It is one of the neglected tropical diseases identified by the World Health Organization for elimination as a public health problem by 2020. Maps displaying disease distribution are helpful tools to identify high-risk areas and target scarce control resources.
methodsWe used pre-intervention site-level occurrence data from 1192 survey sites collected during extensive mapping surveys by the Nigeria Ministry of Health. Using an ensemble of machine learning modelling algorithms (generalised boosted models and random forest), we mapped the ecological niche of LF at a spatial resolution of 1 km
resultsOur maps demonstrate that there is a heterogeneous distribution of LF risk areas across Nigeria, with large portions of northern Nigeria having more environmentally suitable conditions for the occurrence of LF. Here we estimated that approximately 110 million individuals live in areas at risk of LF transmission.
conclusionsMachine learning and ensemble modelling are powerful tools to map disease risk and are known to yield more accurate predictive models with less uncertainty than single models. The resulting map provides a geographical framework to target control efforts and assess its potential impacts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.