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dc.contributor.authorJohn, Learnmore
dc.contributor.authorShekede, Munyaradzi Davis
dc.contributor.authorGwitira, Isaiah
dc.contributor.authorMazhindu, Aldridge Nyasha
dc.contributor.authorPfukenyi, Davies Mubika
dc.contributor.authorChikerema, Silvester
dc.date.accessioned2024-03-19T08:31:54Z
dc.date.available2024-03-19T08:31:54Z
dc.date.issued2024-02-28
dc.identifier.citationJohn, L., Shekede, M. D., Gwitira, I., Mazhindu, A. N., Pfukenyi, D. M., & Chikerema, S. (2024). Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe. BMC Public Health, 24(1), 632.en_US
dc.identifier.issn14712458
dc.identifier.uri10.1186/s12889-024-17856-9
dc.identifier.urihttps://hdl.handle.net/13049/733
dc.descriptionThis is an open access article with full access.en_US
dc.description.abstractIn Zimbabwe, anthrax is endemic with outbreaks being reported almost annually in livestock, wildlife, and humans over the past 40 years. Accurate modelling of its spatial distribution is key in formulating effective control strategies. In this study, an Ensemble Species Distribution Model was used to model the current and future distribution of anthrax occurrence in Zimbabwe. Methods: Bioclimatic variables derived from the Beijing Climate Centre Climate System Model were used to model the disease. Collinearity testing was conducted on the 19 bioclimatic variables and elevation to remove redundancy. Variables that had no collinearity were used for anthrax habitat suitability modelling. Two future climate change scenarios for different Representative Concentration Pathways (RCP), RCP4.5 and RCP8.5 were used. Model evaluation was done using true skill, Kappa statistics and receiver operating characteristics. Results: The results showed that under current bioclimatic conditions, eastern and western districts of Zimbabwe were modelled as highly suitable, central districts moderately suitable and southern parts marginally suitable for anthrax occurrence. Future predictions demonstrated that the suitable (8%) and highly suitable (7%) areas for anthrax occurrence would increase under RCP4.5 scenario. In contrast, a respective decrease (11%) and marginal increase (0.6%) of suitable and highly suitable areas for anthrax occurrence were predicted under the RCP8.5 scenario. The percentage contribution of the predictors varied for the different scenarios; Bio6 and Bio18 for the current scenario, Bio2, Bio4 and Bio9 for the RCP4.5 and Bio3 and Bio15 for the RCP8.5 scenarios. Conclusions: The study revealed that areas currently suitable for anthrax should be targeted for surveillance and prevention. The predicted future anthrax distribution can be used to guide and prioritise surveillance and control activities and optimise allocation of limited resources. In the marginally to moderately suitable areas, effective disease surveillance systems and awareness need to be put in place for early detection of outbreaks. Targeted vaccinations and other control measures including collaborative ‘One Health’ strategies need to be implemented in the predicted highly suitable areas. In the southern part where a high decrease in suitability was predicted, continued monitoring would be necessary to detect incursions early.en_US
dc.language.isoenen_US
dc.publisherBioMed Central Ltden_US
dc.relation.ispartofseriesBMC Public Health;24(1), 632
dc.subjectAnthraxen_US
dc.subjectBioclimatic variablesen_US
dc.subjectHabitat suitabilityen_US
dc.subjectModellingen_US
dc.subjectZimbabween_US
dc.titleModelling climate change impacts on the spatial distribution of anthrax in Zimbabwe.en_US
dc.typeArticleen_US


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