Active Adaptive Management for the Control of Infectious Disease

• Activity Number: 675
• Type: Contributed
• Date/Time: Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Christopher J. Fonnesbeck*+ and Matthew Ferrari and Katriona Shea and Michael Tildesley and Michael Runge and Petra Klepac and Dylan George and Scott Isard and Andrew Flack
• Companies: Vanderbilt University School of Medicine and Penn State University and Penn State University and University of Edinburgh and U.S. Geological Survey and Princeton University and Department of Defense and Penn State University and Defense Threat Reduction Agency
• Keywords: adaptive management ; infectious disease ; decision analysis ; optimization ; learning ; policy

Abstract

In the control of disease, agencies are required to make critical decisions in the face of uncertainty. The classic epidemiological approach to the rollout of new technologies or strategies involves conducting extensive clinical trials prior to broad-scale application. However, during outbreaks or the emergence of new pathogens, management interventions are often applied based on the best available knowledge at the start of the intervention, followed by retrospective analyses to evaluate the impact of the intervention and make recommendations for future actions. Adaptive management (AM) links decision-making with monitoring such that optimal strategies can be derived and updated in near real time. Though previously applied successfully in conservation and pest management, the AM framework has not been generalized for dealing with the management of infectious disease spread. We illustrate the technical implementation of AM to the control of infectious disease, including the specification of models and decision alternatives, the formal incorporation of monitoring information to reduce epistemic uncertainty, and the derivation of dynamic optimal control policies.