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• W4281693005 abstract "Abstract For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. At the same time, anonymized phone-collected mobility data proved to correlate well with the number of cases for the first two waves of the pandemic (spring 2020, and fall-winter 2021). In this work, we show how mobility data could bolster hospital-specific COVID-19 admission forecasts for five hospitals in Massachusetts during the initial COVID-19 surge. The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users’ contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. We conclude that mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges." @default.
• W4281693005 created "2022-06-13" @default.
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• W4281693005 date "2022-06-08" @default.
• W4281693005 modified "2023-09-26" @default.
• W4281693005 title "Forecasting hospital-level COVID-19 admissions using real-time mobility data" @default.
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• W4281693005 doi "https://doi.org/10.1101/2022.06.06.22275840" @default.
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