Simulating the Impact of Early Outbreak Uncertainty on Pandemic Response Intervention Policies

Our primary objective is to evaluate the propagation of uncertainty from early outbreak dynamics to its impact on disease control policy. We will develop simulation models of COVID-19 transmission to test multiple forms of early outbreak uncertainty coupled to game theoretic models representing disease control policymaking and compliance.

Simulation modeling has become a key decision support tool for infectious disease outbreak response activities. During the COVID-19 pandemic, state and federal agencies relied on modeling to forecast the burden of disease and project the impact of different control policies. However, this use is complicated by rapid changes in available information as an outbreak
progresses. Effective intervention measures have the potential to attenuate or halt an outbreak if applied soon enough after the first cases are detected1,2, but often there is limited data available in this period to establish important characteristics such as how rapidly the disease is likely to spread, the severity of infection, or how the disease spreads. This uncertainty makes most policy makers unwilling to make decisions to intervene, given the potential for intervention policies to be mis-targeted, consuming limited public health resources for minimal gain. Outbreak uncertainty can also influence public opinion in ways that impact the success or failure of outbreak control policies. For example, strong restrictions on public activities may be viewed as unwarranted when the burden of disease appears low, leading to poor compliance and increased resistance to interventions. Yet, by the time the severity of an outbreak has been established with certainty, control may be impossible with all but the most intrusive policies.
To prepare for the next pandemic, there is a need to develop modeling methods that account for early outbreak uncertainty and its impacts on disease control policy. We propose to address this need using an interdisciplinary approach, drawing from modeling frameworks from both health and social sciences to capture the feedback between policymakers, members of the public, and disease transmission.

Other Researchers

Michael Hayashi (School of Public Health, Epidemiology)

Joseph Eisenberg (School of Public Health, Epidemiology)