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?:abstract
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It is of critical importance to estimate changing transmission rates and their dependence on population mobility. A common approach to this problem involves fitting daily transmission rates using a Susceptive Exposed Infected Recovered (SEIR) model (regularizing them to avoid overfitting), and then computing the relationship between the estimated transmission rate and mobility. Unfortunately, there are often several, very different transmission rate trajectories that can fit the reported cases well, meaning that the choice of regularization determines the final solution (and thus the mobility-transmission rate relationship) selected by the SEIR model. Moreover, the classical approaches to regularization-penalizing the derivative of the transmission rate trajectory-do not correspond to realistic properties of pandemic spread. Consequently, models fit using derivative-based regularization are often biased toward underestimating the current transmission rate and future deaths. In this work, we propose mobility-driven regularization of the SEIR transmission rate trajectory. This method rectifies the artificial regularization problem, produces more accurate and unbiased forecasts of future deaths, and estimates a highly interpretable relationship between mobility and the transmission rate. Mobility data for this analysis was collected by Safegraph (San Francisco, CA) from major US cities between March and August 2020.
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