Impacts of Climate Change Scenarios on Fasciola gigantica Risk in Semi-arid West Africa: A Case Study of Sokoto State, Nigeria
Isah Hamisu, Heiko Balzter, Jörg Kaduk
South Asian Journal of Parasitology · pp. 11–25 · Published 13 Apr 2020
Abstract
Aims: Fascioliasis is a parasitic and zoonotic liver disease that reduces the health and productivity of infected cattle and sheep. In recent years, an observed increase in the prevalence of the disease in Western Africa has been attributed to the changes in regional climatic conditions. This study aims to employ climate predictions to predict future seasonal infection risk in Sokoto State, Nigeria and provide a basis for targeted active disease monitoring to inform the need for control measures. Place and Duration of Study: Department of Geography, School of Science and Engineering, University of Leicester, between November 2014 and October 2018. Methodology: This study employs the Ollerenshaw index which is commonly used and was modified by Yilma and Malone (1999) to be more suitable for forecasting annual disease risk for Fasciola hepatica and Fasciola gigantica. Relationships of the annual F. gigantica infection risk between historic climate data from WorldClim for 1970-2000 and future climate scenarios from HADGEM2-ES based on the IPCC greenhouse gas emission scenarios RCP2.6 and RCP8.5 from 2050 to 2070 are analysed based on the observed relationships between disease prevalence and climatic conditions in the region. Results: This study reports on the first analysis of the future seasonal infection risk and shows that seasonal infection risk is expected to spread across Sokoto State from a small patch of outbreaks observed in recent years to larger regions under future climate scenarios. Furthermore, the southern part of the study region exhibits the greatest risk of outbreaks due to its higher rainfall compared to other provinces of Nigeria. This pattern is consistent with the prevalence record obtained during field work in the region in 2016. Conclusion: This study provides a basis upon which active disease monitoring can be targeted on highlighted areas where control measures need to be put in place.
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