Optimal policies for mitigating pandemic costs: a tutorial model. (1st September 2022)

• Record Type: Journal Article
• Authors: Serra, M; al-Mosleh, S; Ganga Prasath, S; Raju, V; Mantena, S; Chandra, J; Iams, S; Mahadevan, L
• Is Part Of: Physical biology. Volume 19:Number 5(2022)
• Publication Date: 2022-09-01
• Subjects: epidemiology -- optimal control -- COVID 19; Biophysics -- Periodicals; Biochemistry -- Periodicals; Biology -- Data processing -- Periodicals; 570.5
• DOI: 10.1088/1478-3975/ac7e9e
• Languages: English
• ISSNs: 1478-3967

Abstract

Abstract: There have been a number of pharmaceutical and non-pharmaceutical interventions associated with COVID-19 over the past two years. Various non-pharmaceutical interventions were proposed and implemented to control the spread of the COVID-19 pandemic. Most common of these were partial and complete lockdowns that were used in an attempt to minimize the costs associated with mortality, economic losses and social factors, while being subject to constraints such as finite hospital capacity. Here, we use a minimal model posed in terms of optimal control theory to understand the costs and benefits of such strategies. This allows us to determine top-down policies for how to restrict social contact rates given an age-structured model for the dynamics of the disease. Depending on the relative weights allocated to mortality and socioeconomic losses, we see that the optimal strategies range from long-term social-distancing only for the most vulnerable, partial lockdown to ensure not over-running hospitals, and alternating-shifts, all of which lead to significant reduction in mortality and/or socioeconomic losses. Crucially, commonly used strategies that involve long periods of broad lockdown are almost never optimal, as they are highly unstable to reopening and entail high socioeconomic costs. Using parameter estimates from data available for Germany and the USA early in the pandemic, we quantify these policies and use sensitivity analysis in the relevant model parameters and initial conditions to determine the range of robustness of our policies. Finally we also discuss how bottom-up behavioral changes affect the dynamics of the pandemic and show how they can work in tandem with top-down control policies to mitigate pandemic costs even more effectively.