DYNAMICS OF CHOLERA TRANSMISSION: A MATHEMATICAL MODELING FRAMEWORK FOR ANALYZING EPIDEMIC PROPAGATION

Cholera remains a significant public health concern globally, offering an opportunity to construct robust transmission models that elucidate its dynamics and guide intervention strategies. In this study, we develop a refined cholera transmission model that accounts for time-dependent recovery rates and persistent environmental reservoirs, extending beyond the assumptions of traditional SIR-type frameworks. The model segments the population into compartments—susceptible, infected, and statistical storage of relevant variables—allowing for dynamic epidemic progression under specified parameter values. Historical data on cholera cases, fatalities, and case-fatality rates spanning multiple years underwent rigorous preprocessing, including linear interpolation, to ensure robustness. We employed a least-squares curve-fitting approach to estimate key parameters, which optimizes model accuracy and allows simulation of disease progression and intervention effectiveness over time. Results from our model yield critical insights into cholera transmission, including the roles of environmental bacterial reservoirs and drug treatments in moderating infection rates. These estimated parameters provide policymakers with actionable data for designing targeted interventions, enhancing public health responses, and mitigating cholera's impact on vulnerable populations. This work emphasizes the value of mathematical modeling as a tool for understanding infectious disease dynamics and developing strategies to reduce epidemic impacts.