JMIR Publications

ABSTRACT

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 reporting COVID-19 in the post-pandemic era.

Methods:

Data from four surveillance systems (hospital, wastewater, climatological, and Internet search engine) in a Southern city of China were collected over a one-year period following the easing of the COVID-19 pandemic (from April 1, 2023, to June 30, 2024). Variables were integrated into daily time series and analyzed using Spearman correlation with COVID-19 reported cases. Median and interquartile ranges of correlation coefficients were calculated with a 60-day moving window and 7-day lag to assess association variability. Distributed lag non-linear model (DLNM) was used to capture the non-linear effects of meteorological factors on cases reported at different lag periods.

Results:

Spearman correlation analyses indicated significant correlations between each surveillance systems and reported cases. Among four surveillance systems, 16 variables correlated with reported cases. Notably, the nucleic acid test positivity rate showed a strong correlation, with a coefficient of 0.834 (95%CI: 0.803 to 0.860). Following this, the positivity rate and concentration of viral genes within the wastewater surveillance system were moderately correlated. The time lags ranging from –7 to +7 days did not significantly enhance the statistical measures.

Conclusions:

Each surveillance system plays an indispensable role in infectious disease monitoring. Viral testing and wastewater data are accurate and timely for tracking epidemics. Climatological and Internet search engine surveillance can also effectively reflect disease trends.