A DYNAMIC DECISION MODEL TO IDENTIFY COST-EFFECTIVE POLICIES FOR CONTROLLING EPIDEMICS

Purpose: The cost-effectiveness of specific interventions to control epidemics may change dramatically over time. School closure, for example, may have an important role in reducing disease transmission during early stages of an influenza outbreak, but its benefit would diminish as the pool of susceptible individuals is depleted (either because of deployment of an immunizing vaccine or unabated progression of the epidemic). Our goal is to develop and evaluate a decision tool to adaptively guide the cost-effective use of control interventions throughout the epidemic.

Method: We develop a dynamic decision model to optimize control policies that utilize the observed, but likely imperfect, measures of the epidemic to inform decisions over time. To illustrate the potential uses of this model, we consider a question that would be asked during novel influenza epidemics: When should schools be closed and re-opened? We apply our decision model to optimize alternative school-closure policies that differ in the manner they use epidemiological data to recommend whether schools should be open or closed each week. To evaluate the comparative performance of these policies, we built a simulation model for influenza outbreaks and calibrated it using data from the 2009 U.S. H1N1 influenza pandemic. To optimize a policy, we use net health benefit as the objective function.

Result: Although Policies A-C rely on the same single source epidemic data (i.e. the weekly number of hospitalized cases) to guide decisions, different uses of this data result in different health and financial outcomes (see Figure). Policy A, which only uses the time elapsed since the first hospitalized case to inform decisions (e.g. close schools during weeks 10-14 after observing the first hospitalized case), is dominated by Policies B and C each of which allow more adaptive responses to the accumulating observations. Policy C, by leveraging two measures related to hospitalizations as well as information about previous school closings, better identifies the current epidemic state, and dominates Policies A and B.

Conclusion: While simple control policies (such as Policies A and B) are easy to optimize using mathematical or simulation models, policies that utilize observations in a more intelligent way (such as Policy C) can yield better outcomes. Our modeling approach provides a flexible framework for generating adaptive policies that leverage real-time observations to inform decision making.