Abstract
Retrospective analyses of the non-pharmaceutical interventions (NPIs) used to combat the ongoing COVID-19 outbreak have highlighted the potential of optimizing interventions. These optimal interventions allow policymakers to manage NPIs to minimize the epidemiological and human health impacts of both COVID-19 and the intervention itself. Here, we use a susceptible–infectious–recovered (SIR) mathematical model to explore the feasibility of optimizing the duration, magnitude and trigger point of five different NPI scenarios to minimize the peak prevalence or the attack rate of a simulated UK COVID-19 outbreak. An optimal parameter space to minimize the peak prevalence or the attack rate was identified for each intervention scenario, with each scenario differing with regard to how reductions to transmission were modelled. However, we show that these optimal interventions are fragile, sensitive to epidemiological uncertainty and prone to implementation error. We highlight the use of robust, but suboptimal interventions as an alternative, with these interventions capable of mitigating the peak prevalence or the attack rate over a broader, more achievable parameter space, but being less efficacious than theoretically optimal interventions. This work provides an illustrative example of the concept of intervention optimization across a range of different NPI strategies.This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.
Highlights
The ongoing COVID-19 pandemic has highlighted the vital role of nonpharmaceutical interventions (NPIs) in mitigating the spread of SARS-CoV-2
We explore and compare the existence, patterns and optimal parameter spaces for each intervention to minimize the peak prevalence or the attack rate of a simulated outbreak
Sensitivity analyses were conducted to observe the sensitivity of the peak prevalence, Imax, and the attack rate, Ic(tmax), to the intervention trigger point, magnitude and duration
Summary
The ongoing COVID-19 pandemic has highlighted the vital role of nonpharmaceutical interventions (NPIs) in mitigating the spread of SARS-CoV-2. While an effective tool to drive down disease prevalence, severe NPIs are considered unsustainable and time-limited, with economic, physical and mental health repercussions during and following the cessation of these interventions [3,4,5]. This has driven calls to retrospectively understand the epidemiological and human health impacts of introducing severe NPIs under a different set of circumstances [6,7,8]. This includes insight into how differences in the timing, duration and strength of these interventions could have potentially altered COVID-19-associated mortality and morbidity compared with the actual course of action
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More From: Philosophical Transactions of the Royal Society B: Biological Sciences
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