Influenza Symptom Surveillance System Based on Absenteeism and Temperature: A Pilot Study in China (Preprint)

Abstract

BACKGROUND Absenteeism has been shown to be a valid indicator for influenza surveillance. In China, it is common for students to attend school while ill, so a surveillance system that focuses only on absentees may lead to error. Therefore, the development of a system for both absenteeism and attendance is necessary. OBJECTIVE The objective is to design and implement an influenza symptom surveillance system (SSS) based on absenteeism (collected by face recognition) and temperature of attending students (measured by infrared thermometer), and to evaluate the effectiveness of the system. METHODS An influenza SSS was deployed by extending the functionality of an existing application for student health management. The system used infrared thermometers for temperature screening while counting absent students by facial recognition. The operation of the system was investigated in participating schools in China, the weekly absenteeism and fever rates (WAR and WFR) were calculated and compared with the weekly positive rate of influenza virus (WPRIV) released by the China National Influenza Center to inspect the data reliability and operation feasibility of this system. The system was implemented in two primary schools and one junior high school in the Yangtze River Delta, with a total of approximately 3,500 students participating. The period was from March 1, 2021 to January 14, 2022, with 174 effective days. RESULTS A significant positive correlation between WAR and WPRIV (r=0.868, p<0.001) was detected. When the influenza activity level was low, the WAR was significantly positively correlated among schools. As the influenza activity level increased, the WAR gap among schools gradually increased, and the peak was reached about one week earlier in primary schools than in the junior high school. A significant positive correlation between WFR and WPRIV (r=0.532, p<0.05) was also detected. When WFR was included, the proposed system detected influenza outbreaks up to three weeks earlier than traditional surveillance systems. CONCLUSIONS The feasibility of the proposed system, which calculates absenteeism through face recognition and the temperature of attending students with infrared thermometers, was shown in the present paper. Compared with similar existing systems, this system has advantages of simplicity, acceptability, security, sensitivity and timeliness. CLINICALTRIAL The data used in this study were anonymized, so the Tongji University Review Board designated this study as non-human subject research.