Christopher J Fonnesbeck | Geology Reports

USGS2019

Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study

Determining how to best manage an epidemiological outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). These two uncertainties are rarely addressed concurrently in epidemic studies, impeding decision-making. We present an approach to simultaneously address both sources of uncertainty. Epidemiological uncertainty is represented by a large ensemble of models of the 2014 West African Ebola outbreak. Operational uncertainty about the effectiveness of three classes of intervention is assessed for a wide range of potential effectiveness for each intervention. We ranked each intervention in terms of caseload reduction in each model, initially assuming an unlimited budget. To explore the role of budget limitation, we assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The Value of Information (VoI) to resolve uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget available for the program. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant public health improvements could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for management of other diseases for which multiple models and multiple interventions are available.

Proceedings of the Royal Society B↗

USGS2019

Context matters: Using reinforcement learning to develop human-readable, state-dependent outbreak response policies

The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all possible epidemics are also best for each possible epidemic. Reinforcement learning (RL) has been used to develop machine-readable context-dependent solutions for complex problems with many possible realisations ranging from video-games to the game of Go. RL could be a valuable tool to generate context-dependent policies for outbreak response, though translating the resulting policies into simple rules that can be read and interpreted by human decision-makers remains a challenge. Here we illustrate the application of RL to the development of context-dependent outbreak response policies to minimise outbreaks of foot-and-mouth disease. We show that control based on the resulting context-dependent policies, which adapt interventions to the specific outbreak, result in smaller outbreaks than static policies. We further illustrate two approaches for translating the complex machine-readable policies into simple heuristics that can be evaluated by human decision-makers.

Philosophical Transactions of the Royal Society B:↗