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

Shou-Li Li,Christopher J Fonnesbeck,Ottar N Bjørnstad,Michael J Tildesley,Katriona Shea,Michael C Runge,David Pannell,Matthew J Ferrari

doi:10.1098/rspb.2019.0774

Abstract

Determining how best to manage an infectious disease outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). However, these two uncertainties are rarely addressed concurrently in epidemic studies. We present an approach to simultaneously address both sources of uncertainty, to elucidate which source most impedes decision-making. In the case of the 2014 West African Ebola outbreak, epidemiological uncertainty is represented by a large ensemble of published models. Operational uncertainty about three classes of interventions is assessed for a wide range of potential intervention effectiveness. We ranked each intervention by caseload reduction in each model, initially assuming an unlimited budget as a counterfactual. We then assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The improvement in management outcomes to be gained by resolving 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. 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 reductions in caseload 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 the management of other diseases for which multiple models and multiple interventions are available.

Highlights

During infectious disease outbreaks, decision-makers seek to identify and implement interventions to most effectively bring the epidemic under control.& 2019 The Authors
We used the tenets of decision theory to frame our analysis, because we are interested in the applied question of how to inform decision-making regarding management of epidemic outbreaks
During the epidemic decision-making process, a key issue is the identification of interventions that will most effectively bring an outbreak under control

Summary

Introduction

Decision-makers seek to identify and implement interventions to most effectively bring the epidemic under control.& 2019 The Authors. Decision analysts have identified different sources of uncertainty that can impede decision-making [1,2,3,4,5,6] An awareness of such uncertainties, and of how they might affect management outcomes, is essential for planning effective intervention efforts [4,6]. The second type of uncertainty concerns the magnitude of the effect of any intervention that can be achieved in practice. This type of uncertainty is due to limited information on, for example, logistical constraints, behavioural changes that might arise or compliance with the corresponding intervention during the operational process. We will refer to this type of uncertainty as ‘operational uncertainty’, otherwise known as partial control uncertainty or partial controllability in decision theory (figure 1) [2,6]

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Results

Conclusion