Why COVID-19 models should incorporate the network of social interactions

Helena A Herrmann,Jean-Marc Schwartz

doi:10.1088/1478-3975/aba8ec

Abstract

The global spread of coronavirus disease 2019 (COVID-19) is overwhelming many health-care systems. As a result, epidemiological models are being used to inform policy on how to effectively deal with this pandemic. The majority of existing models assume random diffusion but do not take into account differences in the amount of interactions between individuals, i.e. the underlying human interaction network, whose structure is known to be scale-free. Here, we demonstrate how this network of interactions can be used to predict the spread of the virus and to inform policy on the most successful mitigation and suppression strategies. Using stochastic simulations in a scale-free network, we show that the epidemic can propagate for a long time at a low level before the number of infected individuals suddenly increases markedly, and that this increase occurs shortly after the first hub is infected. We further demonstrate that mitigation strategies that target hubs are far more effective than strategies that randomly decrease the number of connections between individuals. Although applicable to infectious disease modelling in general, our results emphasize how network science can improve the predictive power of current COVID-19 epidemiological models.

Highlights

The coronavirus disease 2019 (COVID-19), first identified in Wuhan, China in December 2019, has spread globally (Sun et al 2020), overwhelming many national health care systems with an increasing number of serious and potentially life-threatening infections (Anderson et al 2020)
Applicable to infectious disease modelling in general, our results emphasize how network science can improve the predictive power of current COVID-19 epidemiological models
We propose that network topology should be combined with dynamic approaches in order to strengthen the predictive power of future pandemic models (Piccardi and Casagrandi 2009)

Summary

Introduction

The coronavirus disease 2019 (COVID-19), first identified in Wuhan, China in December 2019, has spread globally (Sun et al 2020), overwhelming many national health care systems with an increasing number of serious and potentially life-threatening infections (Anderson et al 2020). To ensure the effectiveness of these strategies, various epidemiological models are used to predict the spread of COVID-19 and to inform government policies (Ferguson et al 2020, Hellewell et al 2020, Sameni, 2020, Radulescu and Cavanagh 2020, Simha et al 2020, Zhao and Chen 2020). Most commonly, these models follow a general susceptible-infected-removed (SIR) framework (Kermack and McKendrick 1927, Bailey 1975, Sameni 2020). There are models that consider an inherent randomness or stochasticity in the events that influence the model outcomes (Hellewell et al 2020, Kucharski et al 2020, Simha et al 2020)

Methods

Results

Conclusion