An LSTM + Model for Managing Epidemics: Using Population Mobility and Vulnerability for Forecasting COVID-19 Hospital Admissions

Arindam Ray (), Wolfgang Jank (), Kaushik Dutta () and Matthew Mullarkey ()
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Arindam Ray: University of South Florida, Tampa, Florida 33620
Wolfgang Jank: University of South Florida, Tampa, Florida 33620
Kaushik Dutta: University of South Florida, Tampa, Florida 33620
Matthew Mullarkey: University of South Florida, Tampa, Florida 33620

INFORMS Journal on Computing, 2023, vol. 35, issue 2, 440-457

Abstract: Worldwide epidemics, such as corona virus disease 2019 (COVID-19), cause unprecedented challenges for society and its healthcare systems. Governments attempt to mitigate those challenges by either reducing healthcare demand (“flattening the curve” by imposing restrictions, e.g., on travel or social gatherings) or by increasing healthcare capacity, for example, by canceling elective procedures or setting up field hospitals. To implement these mitigation procedures efficiently, accurate and timely forecasts of the epidemic’s progression are necessary. In this paper, we develop an innovative forecasting methodology based on the ideas of long short-term memory (LSTM) recurrent neural networks. LSTM models are shown to outperform traditional forecasting models, especially when the relationship between input and output is complex and not available in closed form. However, whereas LSTM models perform well for data that changes dynamically over time, one shortcoming is that they are not directly applicable when the data also includes static, nontemporal components. In this work, we propose an LSTM + model that overcomes this limitation. Our model leverages a private partnership with a mobile data company in order to capture population mobility (using mobility indices derived from mobile device data), which allows us to anticipate an epidemic’s spread early and accurately. In addition, we also leverage a public partnership with a consortium of hospitals. Using hospital admissions (rather than, say, positive caseload) results in an unbiased measure of the severity of an epidemic because patients seek and are admitted to hospital care only when symptoms worsen beyond a critical point. We illustrate the effectiveness of our method on forecasting COVID-19 for a major U.S. metropolitan area where it has aided decision makers of the emergency policy group. Our model improves the predictive accuracy of hospital admission by a factor of 2.5× as compared with competing models in the same analytical space.

Keywords: epidemic; COVID-19; LSTM; forecasting; hospital demand management; population mobility; social vulnerability (search for similar items in EconPapers)
Date: 2023
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