Abstract
BACKGROUND: Cholera, caused by Vibrio cholerae, is a global health challenge, spreading through water in areas lacking clean water and sanitation. Since 2021, the reemergence of cholera cases has increased significantly in endemic regions in Africa. In particular, the continent experienced severe outbreaks between 2022 and 2024 due to droughts and cyclones, which have placed additional strain on healthcare systems. OBJECTIVE: This study aims to investigate the dynamics of cholera outbreaks in eight African countries using mathematical modeling and machine learning and to provide information for public health decision making. By estimating key model parameters and epidemiological indicators, such as the basic reproduction number, we aim to identify and quantify the impacts of key transmission drivers. Using this together to socioeconomical factors, we will be classifying cholera persistent countries with similar dynamics using unsupervised learning. In addition, the study seeks to provide information on cholera outbreaks and management across the selected countries, identify key drivers of outbreak intensity, and propose targeted intervention strategies. METHODS: A compartmentalized epidemiological model with indirect transmission routes is analyzed for cholera dynamics in eight African countries with persistent outbreaks. The key parameters and initial values of the model's variables were estimated using a Bayesian framework. We assessed some outcomes such as the reproduction number, "[Formula: see text]," outbreak peak duration and size. Moreover, environmental and socioeconomic data were used in hierarchical clustering to group countries by outbreak characteristics. RESULTS: The study uncovered variation in cholera outbreak dynamics across the considered countries. Based on our model results, the median basic reproduction number ([Formula: see text]) across the endemic countries was 2.0 (SD : 0.454), which ranges from 1.41 in Zimbabwe to 2.80 in Mozambique. Furthermore, the results of the sensitivity analysis emphasized the significance of the maximum infection rate and the bacteria shedding rate in driving cholera outbreaks across the endemic regions in Africa. Hierarchical clustering revealed three distinct groups of countries based on outbreak dynamics and socioeconomic indicators: the chronic sanitation issues cluster (Somalia, Cameroon, and Comoros); the economic and infrastructure challenges cluster (Sudan, Zimbabwe, and Zambia); and the natural disaster cluster (Malawi and Mozambique). CONCLUSION: This study highlights the drivers of cholera outbreaks across African countries, emphasizing the need for tailored interventions that consider underlying socio-demographic and environmental vulnerabilities. The findings underscore the importance of integrating data-driven approaches into cholera preparedness and response efforts to mitigate its impact.