The impact of social interventions on COVID-19 spreading based on multilayer commuter networks

Chaos, Solitons and Fractals. 2024. Vol. 185. Article 115160.

Zeng L., Chen Y., Liu Y., Tang M., Liu Y., Jin Z., Do Y., E. Pelinovsky

From March to June 2022, Shanghai was struck by a new coronavirus variant, Omicron, resulting in the
infected cases of at least 600,000 people. Despite implementing a strict containment policy of city-wide silence
(i.e., residents were not allowed to go out unless necessary), the outbreak cannot be effectively prevented
within a short period of time. A significant academic and practical question is: how could we prevent and
control outbreak of COVID-19 in large, densely populated cities like Shanghai? It is necessary to develop a
rational epidemic spreading model for large cities, in order to accurately predict the trend of disease and
quantitatively assess the impact of non-pharmaceutical interventions. In this paper, a multilayer commuter
metapopulation network model is constructed to capture commuting flows and the size of epidemic outbreak
during commuting between districts. The model accurately predicts epidemic spreading in each district of
Shanghai. Assuming strict city-wide lockdowns, with each district locked down and limited inter-district
commuting as social zones, simulations demonstrate significant suppression of outbreaks due to social-level
interventions. For example, a 1-fold increase in PCR (Polymerase Chain Reaction) testing efficiency reduces
the size of epidemic outbreak by approximately 70%. Larger districts require stricter controls to prevent
exponential growth. Lockdowns effectively prevent epidemic outbreak at low disease rates but less so at
high rates. Liberalized policies lead to varied outbreak trends, with economically developed regions peaking
earlier due to higher population densities. This study provides a comprehensive framework for quantitatively
evaluating the impact of social and regional controls on urban epidemics.

Keywords: Ковид-19, COVID19