Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Lack of knowledge about the disease dynamics, and about the effects of our control strategies on those dynamics, means that we rarely do the best job possible managing such epidemiological problems. Adaptive Management (AM), long-used in natural resource management, is an iterative, structured decision-making approach for dynamic problems that allows for the resolution of uncertainty that limits optimal management via real-time evaluation of alternative models. We propose an AM approach to intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds. Adaptive management thus integrates scientific discovery with policy-making. We have worked on management of foot-and-mouth (FMD) outbreaks in livestock, and measles vaccination strategies. Adaptive Management has the potential simultaneously to reduce uncertainty and improve management outcomes for a wide range of public health issues, with significant positive financial and health benefits.
As part of this work I interact with the Center for Infectious Disease Dynamics.
See a talk I gave to the Ashtekar Frontiers lecture series on Predicting the Future in early 2020 about Disease outbreak control: Harnessing the power of multiple models to work smarter, not harder
Current Projects
Follow these links for details or contact us at scenariohub@midasnetwork.us:
Multi-Model Outbreak Decision Support (MMODS)
Covid-19 Scenario Modeling Hub (see associated GitHub)
Katriona Shea's Lab 