Papers › COVID-19 Risk Estimation using a Time-varying SIR-model
COVID-19 Risk Estimation using a Time-varying SIR-model
Mehrdad Kiamari, Gowri Ramachandran, Quynh Nguyen, Eva Pereira, Jeanne Holm, Bhaskar Krishnamachari
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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 Γₜ 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, Rₜ, 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 Rₜ and Γₜ 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.
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