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.