Real-time pandemic surveillance using hospital admissions and mobility data-Reference-Cited by-同舟云学术

Real-time pandemic surveillance using hospital admissions and mobility data

Published:2022-02 Issue:7 Volume:119 Page:
ISSN:0027-8424
Container-title:Proceedings of the National Academy of Sciences
language:en
Short-container-title:Proc. Natl. Acad. Sci. U.S.A.

Author:

Fox Spencer J.¹^ORCID,Lachmann Michael²,Tec Mauricio³^ORCID,Pasco Remy⁴^ORCID,Woody Spencer¹^ORCID,Du Zhanwei⁵^ORCID,Wang Xutong¹,Ingle Tanvi A.¹,Javan Emily¹,Dahan Maytal⁶,Gaither Kelly⁶⁷^ORCID,Escott Mark E.⁸^ORCID,Adler Stephen I.⁹,Johnston S. Claiborne¹⁰,Scott James G.³¹¹,Meyers Lauren Ancel¹²³^ORCID

Affiliation:

1. Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712

2. Santa Fe Institute, Santa Fe, NM, 87501

3. Department of Statistics and Data Science, The University of Texas at Austin, Austin, TX 78712

4. Department of Operations Research and Industrial Engineering, The University of Texas at Austin, Austin, TX 78712

5. School of Public Health, The University of Hong Kong, Hong Kong, China

6. Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78712

7. Department of Women’s Health, Dell Medical School, Austin, TX 78712

8. Office of the Chief Medical Officer, City of Austin, Austin, TX 78721

9. Office of the Mayor, City of Austin, Austin, TX 78701

10. Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712

11. Department of Information, Risk, and Operations Management, The University of Texas at Austin, Austin, TX 78712

Abstract

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.

Funder

HHS | National Institutes of Health

HHS | Centers for Disease Control and Prevention

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

Link

https://pnas.org/doi/pdf/10.1073/pnas.2111870119

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