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Abstract BackgroundSyndromic 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. ObjectiveThis 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. MethodsWe 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 counts and positive test rates with CLI rate time series, lagged COVID-19 cases or rates, and day of the week as independent variables. Optimal lag periods were identified using Granger causality and likelihood ratio tests. ResultsIn modeling undergraduate student cases, the CLI rate (PPPP ConclusionsThe real-time correlation between syndromic surveillance and COVID-19 cases on a university campus suggests symptom reporting is a viable alternative or supplement to COVID-19 surveillance testing. At the county level, syndromic surveillance is also a leading indicator of COVID-19 cases, enabling quick action to reduce transmission. Further research should investigate COVID-19 risk using syndromic surveillance in other settings, such as low-resource settings like low- and middle-income countries.