Environmental suitability for lymphatic filariasis in Nigeria

• Main Authors: Obiora A. Eneanya, Jorge Cano, Ilaria Dorigatti, Ifeoma Anagbogu, Chukwu Okoronkwo, Tini Garske, Christl A. Donnelly
• Format: Article
• Language: English
• Published: BMC 2018-09-01
• Series: Parasites & Vectors
• Subjects: Lymphatic filariasis; Ensemble modelling; Machine learning; Generalised boosted model (GBM); Random forest (RF)
• Online Access: http://link.springer.com/article/10.1186/s13071-018-3097-9

Abstract Background Lymphatic 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. Methods We 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 km2. By overlaying gridded estimates of population density, we estimated the human population living in LF risk areas on a 100 × 100 m scale. Results Our 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. Conclusions Machine 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.