tetano
Editor, Senior Moderator
JMIR Public Health Surveill
. 2024 Jun 27:10:e54551.
doi: 10.2196/54551. Syndromic Surveillance Tracks COVID-19 Cases in University and County Settings: Retrospective Observational Study
Lily Minh Wass[SUP] 1 [/SUP], Derek O'Keeffe Hoare[SUP] 2 [/SUP], Georgia Elena Smits[SUP] 2 [/SUP], Marwan Osman[SUP] 1 3 [/SUP], Ning Zhang[SUP] 4 [/SUP], William Klepack[SUP] 5 [/SUP], Lara Parrilla[SUP] 1 [/SUP], Jefferson M Busche[SUP] 6 [/SUP], Marin E Clarkberg[SUP] 6 [/SUP], Sumanta Basu[SUP] 2 [/SUP], Casey L Cazer[SUP] 1 7 [/SUP]
Affiliations
Background: 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.
Objective: 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.
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 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.
Results: In modeling undergraduate student cases, the CLI rate (P=.003) and rate of exposure to CLI (P<.001) were significantly correlated with the COVID-19 test positivity rate with no lag in the linear models. At the county level, the health care provider-reported CLI rate was significantly correlated with SARS-CoV-2 test positivity with a 3-day lag in both the linear (P<.001) and negative binomial model (P=.005).
Conclusions: The 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.
Keywords: COVID-19; SARS-CoV-2; coronavirus; epidemic; epidemiological; epidemiology; infectious; pandemic; predict; prediction; predictions; predictive; respiratory; surveillance; surveillance system; syndromic; syndromic surveillance.
. 2024 Jun 27:10:e54551.
doi: 10.2196/54551. Syndromic Surveillance Tracks COVID-19 Cases in University and County Settings: Retrospective Observational Study
Lily Minh Wass[SUP] 1 [/SUP], Derek O'Keeffe Hoare[SUP] 2 [/SUP], Georgia Elena Smits[SUP] 2 [/SUP], Marwan Osman[SUP] 1 3 [/SUP], Ning Zhang[SUP] 4 [/SUP], William Klepack[SUP] 5 [/SUP], Lara Parrilla[SUP] 1 [/SUP], Jefferson M Busche[SUP] 6 [/SUP], Marin E Clarkberg[SUP] 6 [/SUP], Sumanta Basu[SUP] 2 [/SUP], Casey L Cazer[SUP] 1 7 [/SUP]
Affiliations
- PMID: 38952000
- PMCID: PMC11220726
- DOI: 10.2196/54551
Background: 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.
Objective: 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.
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 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.
Results: In modeling undergraduate student cases, the CLI rate (P=.003) and rate of exposure to CLI (P<.001) were significantly correlated with the COVID-19 test positivity rate with no lag in the linear models. At the county level, the health care provider-reported CLI rate was significantly correlated with SARS-CoV-2 test positivity with a 3-day lag in both the linear (P<.001) and negative binomial model (P=.005).
Conclusions: The 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.
Keywords: COVID-19; SARS-CoV-2; coronavirus; epidemic; epidemiological; epidemiology; infectious; pandemic; predict; prediction; predictions; predictive; respiratory; surveillance; surveillance system; syndromic; syndromic surveillance.