As recently acknowledged by the Institute of Medicine, the existing pandemic mitigation models lack dynamic decision support capabilities. This paper develops a simulation optimization model for generating dynamic resource distribution strategies over a network of regions exposed to a pandemic. While the underlying simulation mimics the disease and population dynamics of the affected regions, the optimization model generates progressive allocations of mitigation resources, including vaccines, antivirals, healthcare capacities, and social distancing enforcement measures. The model strives to minimize the impact of ongoing outbreaks and the expected impact of the potential outbreaks, considering measures of morbidity, mortality, and social distancing, translated into the cost of lost productivity and medical expenses. The model was implemented on a simulated outbreak involving four million inhabitants. The strategy was compared to pro-rata and myopic strategies. The model is intended to assist public health policy makers in developing effective distribution policies during influenza pandemics.
Peer Review #3 of "Effects of elevated CO2 on predator avoidance behaviour by reef fishes is not altered by experimental test water (v0.1)"
