CMX Student/Postdoc Seminar
Testing, contact tracing, and isolation (TTI) for epidemic management is difficult to implement at scale. One recent attempt, in the form of exposure notification apps, automate notification of neighbours of a contact network created from Bluetooth technology. We design a new framework that can act as a backend for current exposure notification apps, using data assimilation in conjunction with an epidemiological model over the contact network to learn about individual risks of infection. Network DA exploits both the diverse sources of health data, together with proximity data from mobile devices. In COVID-19 simulations of New York-style city with a population of 100,000, network DA identifies up to a factor 2 more infections than exposure notification app-based contact tracing. The framework can also be used to feedback to artificial users, and targeting contact interventions with network DA can reduce deaths by up to a factor 4 relative to TTI, at relatively moderate test rates, provided high user compliance.