Seasonality and Ecological Suitability Modelling for Anthrax (Bacillus anthracis) in Western Africa.

Abstract

Simple SummaryAnthrax is a globally distributed, neglected, underreported, soil-borne zoonotic disease. In West Africa, the disease is hyper-endemic, severely affecting the livestock sector. Many challenges exist to control the disease in this region, particularly constraints on financial and human resources. Therefore, methods that can be utilized to improve reporting, guide and prioritize surveillance and control activities and rationalize the allocation of limited resources are crucial. In this study, we showed how to optimize the use of fragmented, heterogeneous and limited precise reporting data of anthrax in Burkina Faso, Ghana, Togo, Benin and Niger to understand risk periods as well as identify and predict risk areas. To achieve this, we used anthrax data from different databases in combination with environmental and climate variables and geospatial remote sensing techniques. Our study demonstrated that the number of anthrax outbreaks by month increase with the increasing monthly rates of change in precipitation and normalized difference vegetation index (NDVI) during the transition period from the dry to the wet season. Livestock density, precipitation, NDVI and alkaline soils were the main predictors of anthrax suitability in the region. Our findings on anthrax seasonality and ecological suitability can inform surveillance, prevention and control programs undertaken by animal and public health authorities and enhance collaborative One Health strategies.Anthrax is hyper-endemic in West Africa affecting wildlife, livestock and humans. Prediction is difficult due to the lack of accurate outbreak data. However, predicting the risk of infection is important for public health, wildlife conservation and livestock economies. In this study, the seasonality of anthrax outbreaks in West Africa was investigated using climate time series and ecological niche modeling to identify environmental factors related to anthrax occurrence, develop geospatial risk maps and identify seasonal patterns. Outbreak data in livestock, wildlife and humans between 2010 and 2018 were compiled from different sources and analyzed against monthly rates of change in precipitation, normalized difference vegetation index (NDVI) and land surface temperature. Maximum Entropy was used to predict and map the environmental suitability of anthrax occurrence. The findings showed that: (i) Anthrax outbreaks significantly (99%) increased with incremental changes in monthly precipitation and vegetation growth and decremental changes in monthly temperature during January–June. This explains the occurrence of the anthrax peak during the early wet season in West Africa. (ii) Livestock density, precipitation seasonality, NDVI and alkaline soils were the main predictors of anthrax suitability. (iii) Our approach optimized the use of limited and heterogeneous datasets and ecological niche modeling, demonstrating the value of integrated disease notification data and outbreak reports to generate risk maps. Our findings can inform public, animal and environmental health and enhance national and regional One Health disease control strategies.