Comparative Performance of Wastewater, Clinical, and Digital Surveillance Indicators for COVID-19 Monitoring in Routine Practice: Retrospective Observational Study.

BACKGROUND

Public health surveillance systems are critical for decision-making and have been advanced by monitoring infectious diseases.

OBJECTIVE

This study aims to assess the effectiveness and timeliness of multiple surveillance systems in tracking COVID-19 cases in the postpandemic era.

METHODS

Data of COVID-19-reported cases in a southern city of China were collected from the National Notifiable Disease Reporting Information System over a 1-year period, following the easing of the COVID-19 pandemic restrictions (from April 1, 2023, to June 30, 2024) as the operational benchmark. A total of 4 surveillance systems (hospital, wastewater, meteorological, and internet search engine) were integrated into a daily time series. Spearman correlation and 60-day moving window analyses with 7-day lags were used to assess associations. Distributed lag nonlinear models captured nonlinear meteorological effects. Time-series regression models assessed lead effects (0-7 d) of each surveillance indicator, with and without meteorological adjustment.

RESULTS

Among 4 surveillance systems, 16 variables correlated significantly with reported cases. The nucleic acid amplification test (NAAT) positivity rate showed the strongest correlation, with a coefficient of 0.834 (95% CI 0.803-0.860). Wastewater surveillance system demonstrated a moderate correlation, with the correlation coefficient of 0.776 (95% CI 0.737-0.810) for the N gene positivity rate and 0.698 (95% CI 0.648-0.743) for the N gene concentration. Moving-window analyses confirmed a stable correlation between NAAT positivity and reported cases (median 0.534, IQR 0.394-0.724; 58% of windows ρ>0.5), while wastewater indicators exhibited greater temporal fluctuation, with the N gene concentration (median 0.585, IQR 0.214-0.766; 60.8% of windows ρ>0.5) exceeding the N gene positivity rate (median 0.530, IQR 0.222-0.742; 53.5% of windows ρ>0.5). Time-series analysis identified same-day associations (lag 0) for both NAAT positivity (β=.819, 95% CI 0.768-0.870) and wastewater signals (maximum effect: β=1.023, 95% CI 0.931-1.115). Meteorological factors significantly modified the effect of internet surveillance indicators (P<.05), particularly temperature and absolute humidity.

CONCLUSIONS

An integrated, multichannel surveillance strategy of leveraging wastewater, clinical, and digital streams with meteorological contextualization can strengthen early warning and situational awareness for respiratory pathogen threats.