JMIR Publications

ABSTRACT

Background:

Syndromic surveillance represents a potentially inexpensive supplement to test-based COVID-19 surveillance. By strengthening surveillance of COVID-Like Illness (CLI), targeted and rapid interventions can be facilitated that prevent COVID-19 outbreaks without primary reliance on testing. We sought to predict trends in confirmed SARS-CoV-2 infections from self-reported and healthcare provider-reported CLI in university and county settings, respectively.

Objective:

To predict trends in confirmed SARS-CoV-2 infections from self-reported and healthcare provider-reported CLI in university and county settings, respectively.

Methods:

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 predict confirmed COVID-19 case counts and test-positive rates with CLI rate time series, lagged COVID-19 cases or rates, and day of the week as predictors. Optimal lag periods were identified using Granger causality and likelihood ratio tests.

Results:

In modeling undergraduate student cases, the CLI rate (P=.003) and rate of exposure to CLI (P<.001) predicted the COVID-19 test positivity rate with no lag in the linear models. At the county level, the healthcare provider-reported CLI rate predicted SARS-CoV-2 test positivity with a three-day lag in both the linear (P<.001) and negative binomial model (P=.005).

Conclusions:

Syndromic surveillance can predict COVID-19 cases on university campuses, making it 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.