Epidemic mitigation by statistical inference from contact tracing data
Lenka Zdeborova, Institut
de Physique Théorique,
CEA/Saclay
Abstract: Contact tracing mobile applications are clear candidates
enabling us to slow down the epidemics and keep the society running
while holding the health risks down. Most of the currently discussed
and developed mobile applications aim to notify individuals who were
recently in a significant contact with an individual who tested
COVID-19 positive. The contacted individuals would then be tested
or/and put in isolation. In our work, we aim to quantify the
epidemiological gain one would obtain if, additionally, individuals who
were recently in contact could exchange messages of information. With
such a message passing the risk of contracting COVID-19 could be
estimated with much better accuracy than simple contact tracing. Our
results show that in some range of epidemic spreading (typically when
the manual tracing of all contacts of infected people becomes
practically impossible, but before the fraction of infected people
reaches the scale where a lock-down becomes unavoidable), this
inference of individuals at risk could be an efficient way to mitigate
the epidemic. Our approaches translate into fully distributed
algorithms that only require communication between individuals who have
recently been in contact. We conclude that probabilistic risk
estimation is capable of enhancing performance of digital contact
tracing and should be considered in the currently developed mobile
applications.