Mapping the Burden of COVID-19 in the United States

Ian F Miller; Alexander D Becker; Bryan T Grenfell; C. Jessica E Metcalf

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.05.20054700 doi: medRxiv preprint ahead temporal resolution. While precise projections of peak disease burden would be highly illuminating for informing preparation efforts, such predictions are currently unreliable given the sparsity of data, particularly serological surveys. Uncertainty in region-specific transmission rates and the efficacy of mitigation efforts (e.g., timing, or absence, of shelter-in-place orders) further complicate long-term forecasting. In contrast, projections of cumulative disease burden are considerably more tractable. Such estimates will miss the nuance of the intensity and timing of outbreaks, yet their projection of the spatial footprint of disease burden contains core information relevant to resource distribution. In particular, comparing the total expected number of critical and severe infections against healthcare resources in each county in the U.S. allows for the identification of regions which may experience relatively high disease burden. Furthermore, analyzing simulations of multiple transmission scenarios (e.g., different contact patterns) makes it possible to identify regions of consistently high disease burden without needing to project an exact epidemic trajectory over time.