Identifying cost‐effective dynamic policies to control epidemics

Abstract

We describe a mathematical decision model for identifying dynamic health policies for controlling epidemics. These dynamic policies aim to select the best current intervention based on accumulating epidemic data and the availability of resources at each decision point. We propose an algorithm to approximate dynamic policies that optimize the population’s net health benefit, a performance measure which accounts for both health and monetary outcomes. We further illustrate how dynamic policies can be defined and optimized for the control of a novel viral pathogen, where a policy maker must decide (i) when to employ or lift a transmission-reducing intervention (e.g. school closure) and (ii) how to prioritize population members for vaccination when a limited quantity of vaccines first become available. Within the context of this application, we demonstrate that dynamic policies can produce higher net health benefit than more commonly described static policies that specify a pre-determined sequence of interventions to employ throughout epidemics.

Adaptive Policies to Balance Health Benefits and Economic Costs of Physical Distancing Interventions during the COVID-19 Pandemic

“…As we have shown in previous studies of epidemics, the performance of policies to guide decision making depends on how they use surveillance data to inform decisions. 13–15 Adaptive polices (such as “Adaptive: ICU Capacity” and “Adaptive: Minimize Loss of NMB” in our analysis), which use surveillance data to inform decisions, are expected to outperform static policies (such as “Static: Periodic”), which prespecify the timing of future actions. 13,14 Furthermore, the performance of adaptive policies is affected by how their parameters (e.g., thresholds to switch on/off PD interventions in our analysis) are determined.…”

Infectious disease pandemic planning and response: Incorporating decision analysis

“…This approach to decision-making has been applied in other settings in which decisions must be made in real time, under conditions of high complexity or uncertainty, including aviation [45], engineering [46], wildfire management [47], and livestock disease control [48][49][50][51]. In the context of human disease, although some have considered how to optimize interventions given dynamic knowledge of a system (including emerging epidemic data and resource availability), they tend to ignore the broader context in which decisions are made [52]. Fig 1 depicts a proposed decision support system for pandemic response, featuring a statistical decision model that combines dynamic information from situational awareness tools and intervention models, along with the static information in response plans, and provides dynamic advice on optimal response strategies.…”

Adaptive policies for use of physical distancing interventions during the COVID-19 pandemic

“…As we have shown in previous studies of epidemics (7)(8)(9), the performance of policies to guide decision-making depends on the specific features of surveillance data (e.g. ) selected to inform decisions.…”