Environmental suitability for lymphatic filariasis in Nigeria
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 distribu...
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doaj-a65f40f254214a3ba1e0c7e8c60b1bb12020-11-24T20:53:50ZengBMCParasites & Vectors1756-33052018-09-0111111310.1186/s13071-018-3097-9Environmental suitability for lymphatic filariasis in NigeriaObiora A. Eneanya0Jorge Cano1Ilaria Dorigatti2Ifeoma Anagbogu3Chukwu Okoronkwo4Tini Garske5Christl A. Donnelly6MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College LondonFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical MedicineMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College LondonFederal Ministry of HealthFederal Ministry of HealthMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College LondonMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College LondonAbstract 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.http://link.springer.com/article/10.1186/s13071-018-3097-9Lymphatic filariasisEnsemble modellingMachine learningGeneralised boosted model (GBM)Random forest (RF) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Obiora A. Eneanya Jorge Cano Ilaria Dorigatti Ifeoma Anagbogu Chukwu Okoronkwo Tini Garske Christl A. Donnelly |
spellingShingle |
Obiora A. Eneanya Jorge Cano Ilaria Dorigatti Ifeoma Anagbogu Chukwu Okoronkwo Tini Garske Christl A. Donnelly Environmental suitability for lymphatic filariasis in Nigeria Parasites & Vectors Lymphatic filariasis Ensemble modelling Machine learning Generalised boosted model (GBM) Random forest (RF) |
author_facet |
Obiora A. Eneanya Jorge Cano Ilaria Dorigatti Ifeoma Anagbogu Chukwu Okoronkwo Tini Garske Christl A. Donnelly |
author_sort |
Obiora A. Eneanya |
title |
Environmental suitability for lymphatic filariasis in Nigeria |
title_short |
Environmental suitability for lymphatic filariasis in Nigeria |
title_full |
Environmental suitability for lymphatic filariasis in Nigeria |
title_fullStr |
Environmental suitability for lymphatic filariasis in Nigeria |
title_full_unstemmed |
Environmental suitability for lymphatic filariasis in Nigeria |
title_sort |
environmental suitability for lymphatic filariasis in nigeria |
publisher |
BMC |
series |
Parasites & Vectors |
issn |
1756-3305 |
publishDate |
2018-09-01 |
description |
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. |
topic |
Lymphatic filariasis Ensemble modelling Machine learning Generalised boosted model (GBM) Random forest (RF) |
url |
http://link.springer.com/article/10.1186/s13071-018-3097-9 |
work_keys_str_mv |
AT obioraaeneanya environmentalsuitabilityforlymphaticfilariasisinnigeria AT jorgecano environmentalsuitabilityforlymphaticfilariasisinnigeria AT ilariadorigatti environmentalsuitabilityforlymphaticfilariasisinnigeria AT ifeomaanagbogu environmentalsuitabilityforlymphaticfilariasisinnigeria AT chukwuokoronkwo environmentalsuitabilityforlymphaticfilariasisinnigeria AT tinigarske environmentalsuitabilityforlymphaticfilariasisinnigeria AT christladonnelly environmentalsuitabilityforlymphaticfilariasisinnigeria |
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1716796107603836928 |