Presentation description

Cholera remains a major global health concern, particularly in regions with limited access to clean water, sanitation, and medical care. Children under five are especially vulnerable to cholera, experiencing disproportionately high rates of infection and mortality. Despite this, they are often underrepresented in both research and intervention strategies. Antibiotics are among the most effective treatments for cholera, but to prevent antimicrobial resistance, they are typically reserved for severe cases. However, emerging research suggests that targeted antibiotic use in moderately symptomatic individuals may help reduce overall transmission and total antibiotic use.

For this project, I use an age-structured SEIIR model to simulate cholera outbreaks in a hypothetical population. The model has compartments for two age groups to test the effects of antibiotic expansion targeted at moderately symptomatic children under 5. By altering parameters such as the probability of death, the model shows how this intervention might reduce disease burden, limit transmission, and prevent deaths.

Latin Hypercube Sampling is used to explore a wide range of parameter values, and incidence data is analyzed to assess how expanded antibiotic treatment influences disease spread in children under five. This research provides a computational framework to address public health strategies and evaluate the potential benefits of targeted treatment in vulnerable populations.

This study highlights the importance of age-specific modeling in understanding infectious disease dynamics and supporting data-driven policy decisions. By focusing on the youngest and most at-risk age group, I hope to contribute to the development of more equitable and effective approaches to cholera control.