Department of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States.
Department of Statistics and Data Science, Cornell University, Ithaca, NY, United States.
JMIR Public Health Surveill. 2024 Jun 27;10:e54551. doi: 10.2196/54551.
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.
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.
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.
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).
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.
症状监测代表了一种潜在的低成本 COVID-19 监测补充手段。通过加强对 COVID-19 样疾病（CLI）的监测，可以在不主要依赖检测的情况下，促进有针对性和快速的干预，从而防止 COVID-19 爆发。
本研究旨在分别评估大学和县城环境中确诊的 SARS-CoV-2 感染与自我报告和卫生保健提供者报告的 CLI 之间的时间关系。
我们从康奈尔大学（2020-2021 年）和汤普金斯县卫生部门（2020-2022 年）收集了聚合的 COVID-19 检测和症状报告监测数据。我们使用负二项式和线性回归模型将确诊的 COVID-19 病例数和阳性检测率与 CLI 率时间序列、滞后的 COVID-19 病例或率以及星期几作为自变量进行相关分析。使用格兰杰因果关系和似然比检验确定最佳滞后期。
在对本科学生病例建模时，CLI 率（P=.003）和接触 CLI 的比率（P<.001）与线性模型中 COVID-19 检测阳性率无滞后相关。在县级水平，卫生保健提供者报告的 CLI 率与 SARS-CoV-2 检测阳性率显著相关，线性模型（P<.001）和负二项式模型（P=.005）均存在 3 天的滞后。
大学校园症状监测与 COVID-19 病例之间的实时相关性表明，症状报告是 COVID-19 监测检测的可行替代或补充手段。在县级水平，症状监测也是 COVID-19 病例的主要指标，可以快速采取行动减少传播。进一步的研究应该在其他环境中使用症状监测来调查 COVID-19 风险，例如低收入和中等收入国家等资源有限的环境。
