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We present CRISP (COVID-19 Risk Score Prediction), a probabilistic graphicalmodel for COVID-19 infection spread through a population based on the SEIRmodel where we assume access to (1) mutual contacts between pairs ofindividuals across time across various channels (e.g., Bluetooth contacttraces), as well as (2) test outcomes at given times for infection, exposureand immunity tests. Our micro-level model keeps track of the infection statefor each individual at every point in time, ranging from susceptible, exposed,infectious to recovered. We develop both a Monte Carlo EM as well as a messagepassing algorithm to infer contact-channel specific infection transmissionprobabilities. Our Monte Carlo algorithm uses Gibbs sampling to draw samples ofthe latent infection status of each individual over the entire time period ofanalysis, given the latent infection status of all contacts and test outcomedata. Experimental results with simulated data demonstrate our CRISP model canbe parametrized by the reproduction factor $R_0$ and exhibits population-levelinfectiousness and recovery time series similar to those of the classical SEIRmodel. However, due to the individual contact data, this model allows finegrained control and inference for a wide range of COVID-19 mitigation andsuppression policy measures. Moreover, the block-Gibbs sampling algorithm isable to support efficient testing in a test-trace-isolate approach to containCOVID-19 infection spread. To the best of our knowledge, this is the firstmodel with efficient inference for COVID-19 infection spread based onindividual-level contact data; most epidemic models are macro-level models thatreason over entire populations. The implementation of CRISP is available inPython and C++ at https://github.com/zalandoresearch/CRISP.

CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data