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

Brennan Klein1,2, Ana C Zenteno3, Daisha Joseph4

  • 1Network Science Institute, Northeastern University, Boston, MA, USA. b.klein@northeastern.edu.

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Summary
This summary is machine-generated.

Hospitals can now forecast COVID-19 admissions 21 days in advance using a new model that combines mobility data with hospitalization and testing information. This allows for better resource allocation during pandemic surges.

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Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • Hospitals face challenges in managing surge capacity during COVID-19 pandemic waves.
  • Accurate hospital-level forecasting tools are limited, despite efforts in regional trend prediction.

Purpose of the Study:

  • To develop a novel forecasting model for predicting individual hospital COVID-19 admissions.
  • To enhance hospital preparedness for pandemic-related surges.

Main Methods:

  • Developed a multi-step, recursive forecasting model.
  • Incorporated hospital-level COVID-19 admissions, statewide test positivity rates, and aggregate mobility data (human mobility, contact patterns, commuting volume).
  • Utilized large-scale, anonymized mobile phone data correlated with regional case counts.

Main Results:

  • Achieved accurate, hospital-specific COVID-19 admission forecasts 21 days in advance.
  • Demonstrated high predictive accuracy for five Massachusetts hospitals during the first year of the pandemic.
  • Showcased the effectiveness of incorporating aggregate mobility data as exogenous variables.

Conclusions:

  • Combining anonymized mobility data with COVID hospitalizations and test-positivity data yields high predictive capability.
  • Mobility-informed forecasting models improve lead-time for accurate hospital-specific predictions.
  • Enhanced prediction lead-time allows hospital managers to strategize resource allocation for forthcoming surges.