biostudies-literatureUnknown10Wass LM<h4>Background</h4>Syndromic surveillance represents a potentially inexpensive supplement to test-based COVID-19 surveillance. By strengthening surveillance of COVID-19-like illness (CLI), targeted and rapid interventions can be facilitated that prevent COVID-19 outbreaks without primary reliance on testing.<h4>Objective</h4>This study aims to assess the temporal relationship between confirmed SARS-CoV-2 infections and self-reported and health care provider-reported CLI in university and county settings, respectively.<h4>Methods</h4>We collected aggregated COVID-19 testing and symptom reporting surveillance data from Cornell University (2020-2021) and Tompkins County Health Department (2020-2022). We used negative binomial and linear regression models to correlate confirmed COVID-19 case cJMIR public health and surveillancee54551https://www.ebi.ac.uk/biostudies/studies/S-EPMC11220726biostudies-literatureSyndromic Surveillance Tracks COVID-19 Cases in University and County Settings: Retrospective Observational Study.PMC11220726Klepack WBusche JMO'Keeffe Hoare DOsman MBasu SZhang NClarkberg MEWass LMParrilla LSmits GECazer CLfalseSyndromic Surveillance Tracks COVID-19 Cases in University and County Settings: Retrospective Observational Study.<h4>Background</h4>Syndromic surveillance represents a potentially inexpensive supplement to test-based COVID-19 surveillance. By strengthening surveillance of COVID-19-like illness (CLI), targeted and rapid interventions can be facilitated that prevent COVID-19 outbreaks without primary reliance on testing.<h4>Objective</h4>This study aims to assess the temporal relationship between confirmed SARS-CoV-2 infections and self-reported and health care provider-reported CLI in university and county settings, respectively.<h4>Methods</h4>We collected aggregated COVID-19 testing and symptom reporting surveillance data from Cornell University (2020-2021) and Tompkins County Health Department (2020-2022). We used negative binomial and linear regression models to correlate confirmed COVID-19 case c2024-01-01T00:00:00Z2024 Jun2026-06-01T21:46:50.33Z2025-04-04T23:54:08.987ZS-EPMC112207263895200010.2196/54551