Tracking the COVID-19 epidemic using high-volume non-traditional data
- Funded by Yale University
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19start year
-99Known Financial Commitments (USD)
$0Funder
Yale UniversityPrincipal Investigator
Dan Weinberger, Ted Cohen, Virginia Pitzer, Josh Warren, Marcus Russi, Alyssa Amick, Forrest Crawford, Kelsie Cassell, Ernest Asare, Yu-Han Kao…Research Location
United States of AmericaLead Research Institution
N/AResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Disease transmission dynamics
Special Interest Tags
N/A
Study Type
Unspecified
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
Due to the lack of consistent and widespread testing for coronavirus, there is a need to use non-traditional data sources to track the progression of the epidemic. We are using high-volume electronic data from emergency departments, hospitals, and doctors visits to track the rate of people seeking care for syndromes that would be consistent with COVID-19 (e.g., fever, cough). We use statistical models to separate out increases related to COVID-19 from seasonal variations and increases due to influenza.