Exploring the effective strategies for allocating limited resources to minimize cholera outbreaks.

Despite the near-complete elimination of cholera in many developing countries, numerous lower-income countries continue to face recurring epidemics. Although extensive research has been conducted on cholera transmission dynamics and control, a comprehensive approach to managing outbreaks in resource-limited settings remains elusive. This study introduces a cholera epidemic model incorporating a resource allocation strategy to balance efforts between reducing transmission and enhancing recovery rates. The model is validated using weekly cholera data from the resurgence in Haiti (October 2022-March 2023) to estimate key parameters and the control reproduction number ( R 0 ) . Based on these estimated parameters, the proposed model exhibits rich dynamical behavior, including backward and Hopf bifurcations, highlighting its potential for a multi-wave epidemic pattern. Using a continuous-time Markov chain (CTMC) model and the Gillespie algorithm, we calculate the extinction probability of cholera, comparing it with multitype branching process (MTbp) results, which estimate the analytical form of the probability of cholera extinction and outbreak, showing excellent agreement. Finally, construct a nonlinear programming problem (NLPP) to find the optimal combination of resources to minimize outbreak probability. In solving this NLPP, we find that prioritizing resources to recovery improve interventions is more effective than reducing transmission to minimize the probability of disease outbreak. These insights can guide resource allocation strategies to reduce cholera outbreaks in resource-constrained settings.