Everett Jin

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.