  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.
