CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection
Risk Estimation Based on Contact Data
We present CRISP (COVID-19 Risk Score Prediction), a probabilistic graphical
model for COVID-19 infection spread through a population based on the SEIR
model where we assume access to (1) mutual contacts between pairs of
individuals across time across various channels (e.g., Bluetooth contact
traces), as well as (2) test outcomes at given times for infection, exposure
and immunity tests. Our micro-level model keeps track of the infection state
for each individual at every point in time, ranging from susceptible, exposed,
infectious to recovered. We develop a Monte Carlo EM algorithm to infer
contact-channel specific infection transmission probabilities. Our algorithm
uses Gibbs sampling to draw samples of the latent infection status of each
individual over the entire time period of analysis, given the latent infection
status of all contacts and test outcome data. Experimental results with
simulated data demonstrate our CRISP model can be parametrized by the
reproduction factor R_0 and exhibits population-level infectiousness and
recovery time series similar to those of the classical SEIR model. However, due
to the individual contact data, this model allows fine grained control and
inference for a wide range of COVID-19 mitigation and suppression policy
measures. Moreover, the algorithm is able to support efficient testing in a
test-trace-isolate approach to contain COVID-19 infection spread. To the best
of our knowledge, this is the first model with efficient inference for COVID-19
infection spread based on individual-level contact data; most epidemic models
are macro-level models that reason over entire populations. The implementation
of CRISP is available in Python and C++ at
https://github.com/zalandoresearch/CRISP.
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