Medico-Geographical Forecasting and Modeling of New Climate-Related Infections in Kazakhstan

Modern research and forecasting of disease outbreaks combines different fields of science to evaluate how environmental factors affect where infections spread. Climate conditions play the most critical role, as they are currently shifting across Eurasia due to global climate trends. Due to these consequences, studying how infections spread over time and space in arid regions is highly important. These dry zones are the most dependent on climate conditions. We used Kazakhstan as a case study because its diverse landscapes range from arid to temperate zones. In this region, we examined two vector-borne tropical diseases: Crimean-Congo hemorrhagic fever (CCHF) and West Nile fever (WNF). We analyzed CCHF case data in Kazakhstan from its first emergence to the present day. For WNF, we evaluated the stage where the disease establishes itself in nature without human cases, as well as how different climate conditions influence the potential reach of both infections. We combined medical-geographical, biogeographical, and statistical methods with geographic information systems (GIS) to map the spread of both infections. We then evaluated the optimal climate conditions for the pathogens and vectors, identifying regional geographic patterns under current and future climate scenarios. Modeling the ecological niches of WNF vectors under two climate scenarios revealed the current and future ranges of the infection. This allowed us to identify the areas in Kazakhstan with the most favorable climate for the disease to establish itself. This work delivers original maps, summary tables of optimal bioclimatic parameters for the species involved in transmission, and projections showing how climate change will expand the potential ecological niches of WNF vectors. A significant expansion of both tropical fevers into the central and northern regions of the country is projected over the coming decades. The practical outcome of this work provides cartographic models of the distribution of these two poorly studied infections under various climate conditions.