A simulation-based optimization approach for mitigation of pandemic influenza

ABSTRACT

In preparation for an influenza pandemic, knowledge of how disease spreads as well as having effective intervention strategies in place are critical to mitigate its impacts. We propose a simulation-based optimization model that minimizes the costs associated with the pandemic occurrence, while capturing how influenza spreads among individuals based on the socio-demographic characteristics of the population. Multiple intervention strategies, including school closure and home confinement, are considered to incorporate the changes of a pandemic course in our model and to measure the corresponding effects on the number of infected people. In addition, we apply the NSGS (Nelson, Swann, Goldsman, Song) procedure to achieve computationally efficient and tractable solutions to the resulting large-scale problem. Using real data from Jefferson County, Kentucky, with a population of more than 700,000 obtained from the US Census Bureau, we present computational results to demonstrate the efficacy of applying the proposed approach.

KEYWORDS:

• Agent-based simulation
• healthcare policy
• pandemic influenza
• simulation-based optimization

Acknowledgments

The authors thank the associate editor and anonymous referees for their valuable and constructive comments that have greatly helped improve the discussion and focus in this article.