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

ABSTRACT:

Background

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.

Methods

Large-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide tes

SUBMITTER: Klein B 

PROVIDER: S-EPMC9927044 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature