Global Change Drivers and Their Long-Term Effects on Zoonotic Disease Ecology in Nigeria: A Modelling Approach

Global change drivers including climate change, land use transformation, biodiversity loss, and urbanization are reshaping zoonotic disease ecology in ways that require prospective long-term modelling to inform public health preparedness. This study modelled the long-term effects of global change drivers on zoonotic disease ecology in Nigeria using a coupled climate-ecological-epidemiological modelling framework. Historical climate, land-use, biodiversity, and disease incidence data spanning 1990 to 2023 were compiled from multiple national and international databases. MaxEnt species distribution models were developed for primary reservoir hosts of rabies, Lassa fever, and Rift Valley fever. Climate projections from the CMIP6 ensemble were integrated to model future reservoir habitat suitability under RCP 4.5 and RCP 8.5 scenarios through 2050. Results projected that Lassa fever reservoir habitat in southern Nigeria will expand northward by 200 to 350 kilometers under the high emissions scenario, driven by shifting rainfall patterns. Rift Valley fever risk zones were projected to intensify in flood-prone North Central and North West areas. Rabies reservoir suitability showed minimal climatic change but was highly sensitive to urbanization projections. Novel risk emergence zones were identified in areas currently considered non-endemic. The study provides a methodologically innovative modelling framework specifically calibrated for Nigerian ecological and epidemiological conditions, offering a novel tool for public health scenario planning. A climate-responsive zoonotic disease preparedness protocol is proposed for adoption within Nigeria's national health security framework. Keywords: global change, zoonotic disease ecology, climate modelling, Nigeria, disease emergence