Lori R. Dauelsberg

An Integrated Simulation of Pandemic Influenza Evolution, Mitigation and Infrastructure Response

Decision makers, faced with highly complex alternatives for protecting our nations critical infrastructures must understand the consequences of policy options before they enact solutions to prevent and mitigate disasters. An effective way to examine these tradeoffs is to use a computer simulation that integrates high level representations of each infrastructure, their interdependencies and reactions to a variety of potential disruptions. To address this need, the Critical Infrastructure Protection Decision Support System (CIPDSS) project, funded by the Department of Homeland Security Science and Technology Directorate (DHS S&T), has developed a decision support tool that provides insights to help decision makers make risk-informed decisions. With the addition of a disease progression simulation, the CIPDSS tool has a unique ability to provide a high-level, integrated analysis of a pandemic influenza outbreak while representing the impact on critical infrastructures. This simulation models the time-dependent evolution of the disease and can be calibrated to prior data or to other higher fidelity models as appropriate. Mitigation options such as the use of antivirals and vaccines as prophylaxis, treatment or some combination as well as quarantine options can be assessed. Special attention is given to impacts to the population through sickness, targeted quarantine, or fear-based self-isolation and the resulting impacts on critical infrastructure operations.

Measuring the uncertainties of pandemic influenza

It has become critical to assess the potential range of consequences of a pandemic influenza outbreak given the uncertainty about its disease characteristics while investigating risks and mitigation strategies of vaccines, antivirals, and social distancing measures. Here, we use a simulation model and rigorous experimental design with sensitivity analysis that incorporates uncertainty in the pathogen behaviour and epidemic response to show the extreme variation in the consequences of a potential pandemic outbreak in the USA. Using sensitivity analysis we found the most important disease characteristics are the fraction of the transmission that occur prior to symptoms, the reproductive number, and the length of each disease stage. Using data from the historical pandemics and for potential viral evolution, we show that response planning may underestimate the pandemic consequences by a factor of two or more.