Transmission Dynamics And Control Of Cholera In Africa: A Mathematical Modelling Approach

Background: Cholera, caused by Vibrio cholerae, is a global health threat, with outbreaks surging since 2021, particularly in Africa. In 2024, over 13 African countries faced outbreaks worsened by climatic events, poverty, and weak healthcare systems. A shortage of vaccines further complicates control efforts.

Objective: This study uses data science, machine learning, and modelling to analyze cholera dynamics, identify outbreak drivers, and propose targeted interventions.

Methods: A compartmental model with Bayesian estimation analyzed cholera data from eight African countries. Sensitivity analysis identified key transmission parameters, and hierarchical clustering grouped countries by outbreak characteristics.

Results: Average R0 was 2.0, ranging from 1.41 (Zimbabwe) to 2.80 (Mozambique). Factors like infection rate and human shedding increased R0, while recovery rate reduced it. Clustering identified three outbreak drivers: natural disasters, conflict, and sanitation issues.

Conclusion: Tailored, data-driven interventions are critical for effective cholera management across diverse contexts.