  Policy-makers require data-driven tools to assess the spread of COVID-19 and
inform the public of their risk of infection on an ongoing basis. We propose a
rigorous hybrid model-and-data-driven approach to risk scoring based on a
time-varying SIR epidemic model that ultimately yields a simplified color-coded
risk level for each community. The risk score $\Gamma_t$ that we propose is
proportional to the probability of someone currently healthy getting infected
in the next 24 hours. We show how this risk score can be estimated using
another useful metric of infection spread, $R_t$, the time-varying average
reproduction number which indicates the average number of individuals an
infected person would infect in turn. The proposed approach also allows for
quantification of uncertainty in the estimates of $R_t$ and $\Gamma_t$ in the
form of confidence intervals. Code and data from our effort have been
open-sourced and are being applied to assess and communicate the risk of
infection in the City and County of Los Angeles.
