Decision Aid Models for Resource Sharing Strategies During Global Influenza Pandemics

Pandemic influenza outbreaks have historically entailed significant societal and economic disruptions. Today, our quality of life is threatened by our inadequate preparedness for the imminent pandemic. The key challenges we are facing stem from a significant uncertainty in virus epidemiology, limited response resources, inadequate international collaboration, and the lack of appropriate science-based decision support tools. The existing literature falls short of comprehensive models for global pandemic spread and mitigation which incorporate the heterogeneity of the world regions and realistic travel networks. In addition, there exist virtually no studies which quantify the impact of resource sharing strategies among multiple countries. This dissertation presents three related models that contribute to filling the existing vacuum. The first model develops optimal capacity management strategies for multi-region pandemic surveillance. The second model estimates the pandemic propagation time from the onset to a likely pandemic export region, such as a major transportation hub. The model builds on a large-scale agent-based simulation and geographic information systems (GIS). The model is tested on a hypothetical outbreak in Mexico involving 155 regions and over 100 million people. The third model develops an empirical relationship to quantify the impact of various U.S. - Mexico antiviral sharing strategies under several pandemic detection and response scenarios