Mathematical modeling of cholera epidemics in South Sudan

TL;DR

This paper develops a mathematical model for cholera outbreaks in South Sudan, demonstrating the potential of data assimilation techniques to forecast epidemic dynamics amidst complex social and political challenges.

Contribution

It introduces a calibrated SIRB model using Ensemble Kalman Filter to predict cholera outbreaks in a highly unstable environment.

Findings

Model accurately predicts epidemic peaks in major counties.

Data assimilation improves forecast reliability.

Method shows promise for epidemic management in conflict zones.

Abstract

In this work, we analyze and model the cholera epidemics that affected South Sudan, the newest country in the world, during 2014 and 2015. South Sudan possibly represents one of the most difficult context in which adapt the deterministic mathematical cholera model, due to the unstable social and political situation that clearly affects the fluxes of people and the sanitary conditions, increasing the risk of large outbreaks. Despite the limitation of a static gravity model in describing the chaotic human mobility of South Sudan, the SIRB model, calibrated with a data assimilation technique (Ensemble Kalman Filter), retrieves the epidemic dynamics in the counties with the largest number of infected cases, showing the potentiality of the methodology in forecasting future outbreaks.