A data-driven optimization approach for multi-period resource allocation in cholera outbreak control

Cholera, a water-borne bacteria infectious disease, shows clear spatial variation in its transmission pattern. It is thus important to incorporate understanding on the spatial variability of its transmission when making transmission prediction and intervention decisions. However, for an emerging cholera outbreak, transmission dynamics models are often uncertain as model parameters are indeterminate and epidemic state can only be partially observed. Hence, ensuing intervention decisions have to be made under uncertainty and thus the resultant optimization problem is challenging. In this paper, we study a multi-period location-specific resource allocation problem for cholera outbreak intervention with periodically acquired state information from different locations and increasingly understood transmission parameters over time. We formulate the problem as a nonlinear optimization model on a set of ordinary-differential-equations governing location-specific disease transmission dynamics. We propose a data-driven optimization approach to determine the optimal strategy of intervention resource allocation at each period and each community in a rolling-horizon manner. At each period, we integrate single-period model parameter fitting and scenario-based stochastic programming to make decisions under uncertainty with newly acquired system understanding. We conduct comparative studies to assess the performance of our data-driven optimization approach and offer insights into intervention resource allocation policy development. We conclude that our data-driven optimization approach is effective to multi-period decision problems under system dynamics with indeterminate parameters.