Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions

TL;DR

This paper introduces a scalable data assimilation approach for contact tracing apps that provides continuous individual risk assessments, significantly improving epidemic control effectiveness over traditional methods like contact tracing alone.

Contribution

It presents a novel network data assimilation method that enhances exposure notification apps by providing dynamic risk assessments for targeted interventions.

Findings

Network DA identifies up to twice as many infections as contact tracing.

When widely adopted, network DA can reduce epidemic deaths by up to four times.

The method can be integrated into existing exposure notification apps to improve epidemic management.

Abstract

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City prove the concepts. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population ($\gtrsim 75\%$) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.