Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty

TL;DR

This paper develops a stochastic compartmental model with social structure and feedback controls to evaluate the effects of selective lockdown relaxation during COVID-19, accounting for uncertainties in key epidemiological parameters.

Contribution

It introduces a novel modeling approach incorporating social activities, uncertainty, and feedback controls to assess lockdown relaxation impacts during epidemics.

Findings

Different scenarios show varying epidemic outcomes based on relaxation strategies.

Selective containment can effectively balance health risks and economic impacts.

Model results highlight the importance of targeted measures in managing outbreaks.

Abstract

After the introduction of drastic containment measures aimed at stopping the epidemic contagion from SARS-CoV2, many governments have adopted a strategy based on a periodic relaxation of such measures in the face of a severe economic crisis caused by lockdowns. Assessing the impact of such openings in relation to the risk of a resumption of the spread of the disease is an extremely difficult problem due to the many unknowns concerning the actual number of people infected, the actual reproduction number and infection fatality rate of the disease. In this work, starting from a compartmental model with a social structure and stochastic inputs, we derive models with multiple feedback controls depending on the social activities that allow to assess the impact of a selective relaxation of the containment measures in the presence of uncertain data. Specific contact patterns in the home, work, school and other locations have been considered. Results from different scenarios concerning the first wave of the epidemic in some major countries, including Germany, France, Italy, Spain, the United Kingdom and the United States, are presented and discussed.