Forecasting hospital-level COVID-19 admissions using real-time mobility data

Brennan Klein, Ana C. Zenteno, Daisha Joseph, Mohammadmehdi Zahedi, Michael Hu, Martin Copenhaver, Moritz U.G. Kraemer, Matteo Chinazzi, Michael Klompas, Alessandro Vespignani, Samuel V. Scarpino, Hojjat Salmasian


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.

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