Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions
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Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale. Here we demonstrate a scalable improvement to TTI that uses data assimilation (DA) on a contact network to learn about individual risks of infection. Network DA exploits diverse sources of health data together with proximity data from mobile devices. In simulations of the early COVID-19 epidemic in New York City, network DA identifies up to a factor 2 more infections than contact tracing when harnessing the same diagnostic test data. Targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI, provided compliance reaches around 75 backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control the ongoing or future epidemics while minimizing economic disruption.
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