Optimizing time-limited non-pharmaceutical interventions for COVID-19 outbreak control

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’.

Role of mathematical modelling in future pandemic response policy

“…Mathematical modelling provides a framework in which we can formalise our assumptions about the processes we are trying to capture (eg, disease spread and impact), build them into a simplified representation of reality, and simulate forward in time to suggest what might happen in the future under different policy options 2223. Modelling is also extremely useful in understanding the underlying situation when we have incomplete or missing data,2425 and can shed light on what has happened in the past when the picture is murky, such as the effect of different public health mitigations 24…”

Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model

“…In another study [28] , a deterministic approach has been developed using an SEIR-mathematical modelling framework to explore the concept of optimal and robust interventions across a range of different non-pharmaceutical interventions (NPI) scenarios. An epidemiological mathematical model has been proposed in [29] for capturing and predicting the spread of COVID-19 with a simulation model which is performed using the two-step generalized exponential time-differencing method.…”

Toward more realistic social distancing policies via advanced feedback control

“…[Di Lauro et al(2021b)], [Efimov & Ushirobira(2021)], [Esterhuizen et al(2021)], [Fliess et al(2022)], [Gevertz et al(2021)], ], [Ianni & Rossi(2021)], [Jing et al(2021)], [Köhler et al(2021)], [McQuade et al(2021)], [Morato et al(2020a)], [Morato et al(2020b)], [Morgan et al(2021)], [Morris et al(2021)], [O'Sullivan et al(2020)], [Péni et al(2020)], ], [Sadeghi et al(2021)], [Sontag(2021)], [Stella et al(2022)], [Tsay et al(2020)]. Most diverse viewpoints have been developed.…”

“…g ., [Adolph et al(2021)]) have stimulated a huge number of mathematically oriented investigations among which we select control-theoretic publications: See, e . g ., [Al-Radhawi et al(2022)], [Ames et al(2020)], [Angulo et al(2021)], [Berger(2022)], [Bisiacco & Pillonetto(2021)], [Bisiacco et al(2022)], [Bliman & Duprez(2021)], [Bliman et al(2021)], [Bonnans & Gianatti(2020)], [Borri et al(2021)], [Charpentier et al(2020)], [Dias et al(2022)], [Di Lauro et al(2021a)], [Di Lauro et al(2021b)], [Efimov & Ushirobira(2021)], [Esterhuizen et al(2021)], [Fliess et al(2022)], [Gevertz et al(2021)], [Greene & Sontag(2021)], [Ianni & Rossi(2021)], [Jing et al(2021)], [Köhler et al(2021)], [McQuade et al(2021)], [Morato et al(2020a)], [Morato et al(2020b)], [Morgan et al(2021)], [Morris et al(2021)], [O’Sullivan et al(2020)], [Péni et al(2020)], [Pillonetto et al(2021)], [Sadeghi et al(2021)], [Sontag(2021)], [Stella et al(2022)], [Tsay et al(2020)]. Most diverse viewpoints have been developed.…”