Kavli Affiliate: Chiara Daraio
| First 5 Authors: Tapio Schneider, Oliver R. A. Dunbar, Jinlong Wu, Lucas Böttcher, Dmitry Burov
| Summary:
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%. Network DA can be implemented by expanding the
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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