Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data

Haotian Wang,Rik Sarkar,Abhirup Ghosh,Jiaxin Ding,Jie Gao

doi:10.1038/s41598-021-87034-z

Abstract

Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.

Highlights

Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus
We found that mobility statistics are heterogeneous across individuals and venues (Fig. 1), with few agents being highly active and some venues attracting many visitors. This heterogeneity and consequent network effects influence the spread of COVID-19
We have shown that the diversity of human movement influences the spread of the virus, making the behavior different from homogeneous models

Summary

Introduction

Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Due to the high infection rate and high demand for medical resources, most countries have adopted large-scale interventions such as business lockdowns and restricted movements These intervention methods have proven to be effective—successfully reducing the number of peak daily infected cases and the total number of infected cases so far, as shown by both direct observation from real d ata[1,2] as well as indirect metapopulation models and s imulations[3,4]. We consider movements of the real people captured in three types of mobility data—check-ins at seven different cities, WiFi connection events on a university campus, and GPS traces of electric bikes—representing different scales, behaviors, and modalities Across these diverse circumstances, we observe several common features in human movement, the spread of the epidemic, and the effect of interventions

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