Pilot Influenza Syndromic Surveillance System Based on Absenteeism and Temperature in China: Development and Usability Study

Abstract

Background Shortcomings of the current school-based infectious disease syndromic surveillance system (SSS) in China include relying on school physicians to collect data manually and ignoring the health information of students in attendance. Objective This study aimed to design and implement an influenza SSS based on the absenteeism (collected by face recognition) and temperature of attending students (measured by thermal imaging). Methods An SSS was implemented by extending the functionality of an existing application. The system was implemented in 2 primary schools and 1 junior high school in the Yangtze River Delta, with a total of 3535 students. The examination period was from March 1, 2021, to January 14, 2022, with 174 effective days. The daily and weekly absenteeism and fever rates reported by the system (DAR1 and DFR; WAR1 and WFR) were calculated. The daily and weekly absenteeism rates reported by school physicians (DAR2 and WAR2) and the weekly positive rate of influenza virus (WPRIV, released by the Chinese National Influenza Center) were used as standards to evaluate the quality of the data reported by the system. Results Absenteeism reported by school physicians (completeness 86.7%) was 36.5% of that reported by this system (completeness 100%), and a significant positive correlation between them was detected (r=0.372, P=.002). When the influenza activity level was moderate, DAR1s were significantly positively correlated among schools (rab=0.508, P=.004; rbc=0.427, P=.02; rac=0.447, P=.01). During the influenza breakout, the gap of DAR1s widened. WAR1 peaked 2 weeks earlier in schools A and B than in school C. Variables significantly positively correlated with the WPRIV were the WAR1 and WAR2 of school A, WAR1 of school C, and WFR of school B. The correlation between the WAR1 and WPRIV was greater than that between the WAR2 and WPRIV in school A. Addition of the WFR to the WAR1 of school B increased the correlation between the WAR1 and WPRIV. Conclusions Data demonstrated that absenteeism calculation based on face recognition was reliable, but the accuracy of the temperature recorded by the infrared thermometer should be enhanced. Compared with similar SSSs, this system has superior simplicity, cost-effectiveness, data quality, sensitivity, and timeliness.