Open AccessReal-time pandemic surveillance using hospital admissions and mobility data
Author(s) -
Spencer J. Fox,
Michael Lachmann,
Mauricio Tec,
Remy Pasco,
Spencer Woody,
Zhanwei Du,
Xutong Wang,
Tanvi Ingle,
Emily Javan,
Maytal Dahan,
Kelly Gaither,
Mark E A Escott,
Stephen I. Adler,
S. Claiborne Johnston,
James G. Scott,
Lauren Ancel Meyers
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2111870119
Subject(s) - pandemic , medicine , covid-19 , lagging , demography , public health , demographics , confidence interval , transmission (telecommunications) , census , public health surveillance , retrospective cohort study , emergency medicine , pediatrics , environmental health , disease , population , computer science , infectious disease (medical specialty) , telecommunications , nursing , pathology , sociology
Significance Forecasting COVID-19 healthcare demand has been hindered by poor data throughout the pandemic. We introduce a robust model for predicting COVID-19 transmission and hospitalizations based on COVID-19 hospital admissions and cell phone mobility data. This approach was developed by a municipal COVID-19 task force in Austin, TX, which includes civic leaders, public health officials, healthcare executives, and scientists. The model was incorporated into a dashboard providing daily healthcare forecasts that have raised public awareness, guided the city’s staged alert system to prevent unmanageable ICU surges, and triggered the launch of an alternative care site to accommodate hospital overflow.