Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experience

Background: Large-scale diagnostic testing has been proven ineffective for prompt monitoring of the spread of COVID-19. Electronic resources may facilitate enhanced early detection of epidemics. Here, we aimed to retrospectively explore whether examining trends in the use of emergency and healthcare services and the Google search engine is useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared with the currently used swab-based surveillance system. Methods: Healthcare Utilization databases of the Italian region of Lombardy and the Google Trends website were used to measure the weekly utilization of emergency and healthcare services and determining the volume of Google searches from 2020 to 2022. Improved Farrington algorithm (IMPF) and Exponentially Weighted Moving Average (EWMA) control chart were both fitted to detect outliers in weekly searches of nine syndromic tracers. AND/OR Boolean operators were tested aimed for joint using tracers and models. Signals that occurred during periods labelled as free from epidemics were used to measure positive predictive values (PPV) and false negative values (FNV) in anticipating the epidemic wave. Results: Out of the 156 weeks of interest, 70 (45 %) were affected by epidemic waves. Overall, 54 syndromic signals were obtained from any one of the 7 healthcare or Google tracers, generating an outlier from both the EWMA and IMPF models. PPV values of 0.95, 1.00, 0.96 admitting a delay of 0, 1, and 2 weeks, respectively, between signal and epidemic wave. The values of FNP ranged from 0.19 to 0.21. Conclusions: High predictive power for anticipating COVID-19 epidemic waves, even two weeks ahead of the official reports, was obtained from electronic syndromic tracers of healthcare-seeking trends and Google search engine use. Following verification via a prospective approach, public health organizations are encouraged to take advantage of this free forecasting system to anticipate and effectively manage respiratory outbreaks.