A multilayer network model for studying the impact of non-pharmaceutical interventions implemented in response to COVID-19

Abstract

Non-pharmaceutical interventions (NPIs) are essential for the effective prevention and control of the COVID-19 pandemic. However, the scenarios for disease transmission are complicated and varied, and it remains unclear how real-world networks respond to the changes in NPIs. Here, we propose a multi-layer network combining structurally fixed social contact networks with a time-varying mobility network, select the COVID-19 outbreak in two metropolitans in China as case studies, and assess the effectiveness of NPIs. Human mobility, both in relatively fixed places and in urban commuting, is considered. Enclosed places are simulated by three different types of social contact networks, while urban commuting is represented by a time-varying commute network. We provide a composite framework that captures the heterogeneity and time variation of the real world and enables us to simulate large populations with low computational costs. We give out a thorough evaluation of the effectiveness of NPIs (i.e., work from home, school closure, close-off management, public transit limitation, quarantine, and mask use) under certain vaccine coverage varying with implementation timing and intensity. Our results highlight the strong correlation between the NPI pattern and the epidemic mitigation effect and suggest important operational strategies for epidemic control.

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

“…Big data can be used to understand patterns and trends in human mobility, which can inform public health and transportation policies. For example, big data can be used to identify high-risk areas for the spread of infectious diseases and to optimize the distribution of healthcare resources [66][67][68]. vi.…”

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China

“…Many predictive epidemiological models have been established prior to or during the outbreak of COVID-19 ( Ciufolini & Paolozzi, 2020 ; Dehning et al, 2020 ; Dowd et al, 2020 ; Enserink & Kupferschmidt, 2020 ; S. Guo et al, 2020 ). The experiences and conclusions from the models are in consensus: reliable short-term prediction is the key to the success of combatting the spread, flattening the curve especially in the initial stages of the spread, regardless of where the prediction was made and how it was made ( Baker et al, 2020 ; Chen et al, 2022 ; Ciufolini & Paolozzi, 2020 ; Davies et al, 2020 ; Dayaratna et al, 2022 ).…”

A novel approach to model the role of mobility suppression and vaccinations in containing epidemics in a network of cities

“…Data-driven network-based models were frequently combined with machine learning and deep learning techniques to uncover important patterns and conclusions in specific geographical districts ( Anno, Hirakawa, Sugita, & Yasumoto, 2022 ; Ojugo & Nwankwo, 2021 ; Pinheiro, Galati, Summerville, & Lambrecht, 2021 ; Roy, Biswas, & Ghosh, 2021 ). Another common modeling method that has been used in epidemiological studies is agent-based modeling ( Chen et al., 2022 ; Patel et al., 2021 ; Vedam & Ghose, 2022 ). It represents a detailed description of contagions spread by tracing agents (individuals) and can be highly adjustable to include different nonpharmaticual interventions and assess their relative effects.…”

“…It represents a detailed description of contagions spread by tracing agents (individuals) and can be highly adjustable to include different nonpharmaticual interventions and assess their relative effects. A former study ( Chen et al., 2022 ) proposed a multi-layer network that considers both social contact (using an agent-based approach) and urban commuting (using a time-varying network). Employing available vaccine cases, the study examined several control strategies individually and reported their effectiveness.…”