ArticleParasites & vectors2019
Mapping the baseline prevalence of lymphatic filariasis across Nigeria.
Article in Parasites & vectors, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A review of epidemiology of lymphatic filariasis in Nigeria.The Pan African medical journal · 2024Pooled 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
- Using ESPEN data for evidence-based control of neglected tropical diseases in sub-Saharan Africa: A comprehensive model-based geostatistical analysis of soil-transmitted helminths.PLoS neglected tropical diseases · 2025Article
- Alternative approaches for monitoring and evaluation of lymphatic filariasis following mass drug treatment with ivermectin, diethylcarbamazine and albendazole in East New Britain Province, Papua New Guinea.PLoS neglected tropical diseases · 2025Observational
- Burden, knowledge and perception of lymphatic filariasis in resource - poor communities in north central Nigeria.Journal of parasitic diseases : official organ of the Indian Society for Parasitology · 2024Article
- Prediction on the spatial distribution of the seropositive rate of schistosomiasis in Hunan Province, China: a machine learning model integrated with the Kriging method.Parasitology research · 2024Article
- Towards Understanding the Microepidemiology of Lymphatic Filariasis at the Community Level in Ghana.Tropical medicine and infectious disease · 2024Article
- Integration of services for Neglected Tropical Diseases and mental health in Nigeria: development of a practical model informed by international recommendations, contextual factors and service-user perspectives.International health · 2023Article
- Entomological surveillance reveals transmission of malaria but not lymphatic filariasis in two communities in North-West Nigeria.Parasitology research · 2023Article
- Geospatial modelling of lymphatic filariasis and malaria co-endemicity in Nigeria.International health · 2023Article
- Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria.PloS one · 2023Article
- Persistence of onchocerciasis in villages in Enugu and Ogun states in Nigeria following many rounds of mass distribution of ivermectin.BMC infectious diseases · 2022Article
- Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease.PLoS neglected tropical diseases · 2022Article
- Geospatial modeling of pre-intervention nodule prevalence of Onchocerca volvulus in Ethiopia as an aid to onchocerciasis elimination.PLoS neglected tropical diseases · 2022Article
- Artificial intelligence and lymphedema: State of the art.Journal of clinical and translational research · 2022Review
- Progress towards onchocerciasis elimination in Côte d'Ivoire: A geospatial modelling study.PLoS neglected tropical diseases · 2021Article
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7 authors.
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Abstract
introductionThe baseline endemicity profile of lymphatic filariasis (LF) is a key benchmark for planning control programmes, monitoring their impact on transmission and assessing the feasibility of achieving elimination. Presented in this work is the modelled serological and parasitological prevalence of LF prior to the scale-up of mass drug administration (MDA) in Nigeria using a machine learning based approach.
methodsLF prevalence data generated by the Nigeria Lymphatic Filariasis Control Programme during country-wide mapping surveys conducted between 2000 and 2013 were used to build the models. The dataset comprised of 1103 community-level surveys based on the detection of filarial antigenemia using rapid immunochromatographic card tests (ICT) and 184 prevalence surveys testing for the presence of microfilaria (Mf) in blood. Using a suite of climate and environmental continuous gridded variables and compiled site-level prevalence data, a quantile regression forest (QRF) model was fitted for both antigenemia and microfilaraemia LF prevalence. Model predictions were projected across a continuous 5 × 5 km gridded map of Nigeria. The number of individuals potentially infected by LF prior to MDA interventions was subsequently estimated.
resultsMaps presented predict a heterogeneous distribution of LF antigenemia and microfilaraemia in Nigeria. The North-Central, North-West, and South-East regions displayed the highest predicted LF seroprevalence, whereas predicted Mf prevalence was highest in the southern regions. Overall, 8.7 million and 3.3 million infections were predicted for ICT and Mf, respectively.
conclusionsQRF is a machine learning-based algorithm capable of handling high-dimensional data and fitting complex relationships between response and predictor variables. Our models provide a benchmark through which the progress of ongoing LF control efforts can be monitored.
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