{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T06:02:20Z","timestamp":1783749740339,"version":"3.55.0"},"reference-count":44,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Public health surveillance systems are critical for decision-making and have been advanced by monitoring infectious diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to assess the effectiveness and timeliness of multiple surveillance systems in tracking COVID-19 cases in the postpandemic era.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data of COVID-19\u2013reported 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\u20107 d) of each surveillance indicator, with and without meteorological adjustment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u20100.860). Wastewater surveillance system demonstrated a moderate correlation, with the correlation coefficient of 0.776 (95% CI 0.737\u20100.810) for the N gene positivity rate and 0.698 (95% CI 0.648\u20100.743) for the N gene concentration. Moving-window analyses confirmed a stable correlation between NAAT positivity and reported cases (median 0.534, IQR 0.394\u20100.724; 58% of windows \u03c1&gt;0.5), while wastewater indicators exhibited greater temporal fluctuation, with the N gene concentration (median 0.585, IQR 0.214\u20100.766; 60.8% of windows \u03c1&gt;0.5) exceeding the N gene positivity rate (median 0.530, IQR 0.222\u20100.742; 53.5% of windows \u03c1&gt;0.5). Time-series analysis identified same-day associations (lag 0) for both NAAT positivity (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      =.819, 95% CI 0.768\u20100.870) and wastewater signals (maximum effect:\n                      <jats:italic>\u03b2<\/jats:italic>\n                      =1.023, 95% CI 0.931\u20101.115). Meteorological factors significantly modified the effect of internet surveillance indicators (\n                      <jats:italic>P<\/jats:italic>\n                      &lt;.05), particularly temperature and absolute humidity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/70232","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T10:57:20Z","timestamp":1762513040000},"page":"e70232-e70232","source":"Crossref","is-referenced-by-count":2,"title":["Comparative Performance of Wastewater, Clinical, and Digital 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