Heterogeneous Interventions Reduce the Spread of COVID-19 in Simulations on Real Mobility Data

TL;DR

This study demonstrates that targeted interventions focusing on highly mobile individuals and venues can effectively reduce COVID-19 spread while maintaining social activity, based on simulations with real mobility data from multiple cities.

Contribution

The paper introduces a novel approach showing that heterogeneous, targeted interventions outperform broad lockdowns in controlling pandemic spread with less societal disruption.

Findings

Targeted interventions reduce peak infection rates.

Fewer total infections with targeted strategies.

Effective across diverse cities and data resolutions.

Abstract

Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large-scale lockdowns 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.