A spatio-temporal analysis of climate variability and a selected zoonotic disease distribution in Binga District, Zimbabwe. A quantitative research protocol

Introduction Climate warming in the coming years is anticipated to greatly influence the relationships between pathogens and their animal and human hosts. Vector-borne illnesses are particularly vulnerable to rising temperatures, as temperature changes can modify development rates, geographical spread, and transmission patterns of vectors. Trypanosomiasis remains a neglected zoonotic disease still prevalent in Zimbabwe, with Binga being significantly impacted by it. Consequently, this study aims to explore how climate variability and change have influenced the epidemiology of this disease among the human population in Binga from 2000 to 2025. Methods As this is protocol for proposed research, a quantitative cross-sectional survey will be designed using secondary data sources and the administration of a mapping guide (2000–2025) to uncover the effects of climate variability and change on trypanosomiasis in the Binga District. The data collection checklist and questionnaire will be administered using Kobo Collect. The collected data will be analyzed both descriptively and inferentially to determine the incidence and prevalence rates, as well as their association with climate variability, including rainfall and temperature, and various socio-demographic characteristics. Regression analysis will be employed to evaluate the association between changes in temperature, variations in rainfall, and the distribution of cases. Rainfall and temperature will be mapped for specific three-year intervals (2000, 2003, 2006, 2009, 2012, 2015, 2018, 2021, 2025) from 2000 to 2025 and overlaid with cases of trypanosomiasis and hotspot maps generated using ArcGIS Pro. Predictive maps of climate variability and disease prevalence will be developed. Discussion The outcome of this proposed research has the potential to influence decision-making to plan for disease control, thus resulting in an improvement in programming. Knowledge of how climate variations and change affect the epidemiology of the disease will enable decision-makers to plan control strategies to mitigate the effects of the disease.