Utilizing early-prediction of respiratory infections in Larissa, Thessaly, Greece: Incorporating daily school absenteeism as an early-warning indicator

Background The outbreak of COVID19 in Greece prompted extensive public health measures, including the first national lockdown and the suspension of in-person schooling. Recognizing the significant role of children in community transmission due to their contacts in schools, school absenteeism data began to be systematically recorded as a potential indicator of outbreak patterns. Objectives This study aims to explore the utility of incorporating school absenteeism data in an early warning surveillance system for respiratory infections, particularly in predicting the onset and spread of diseases such as COVID19 and influenza. Methods We utilized school absenteeism data from primary schools and kindergartens in the Municipality of Larissa for the 2022 2024 school years, alongside health data from the University Hospital of Larissa (UHL). These included incidence rates of respiratory infections, COVID-19, and flu cases, which were cross-referenced with absenteeism patterns. Results The analysis showed that peaks in absenteeism often preceded increases in cases of respiratory infections, COVID19, and flu, suggesting absenteeism as a potential early warning indicator. Notable divergences in patterns were observed during school closures for holidays, which posed challenges in data continuity and surveillance effectiveness. Conclusions School absenteeism data significantly enhances the capability for early detection and monitoring of respiratory disease outbreaks. To improve future surveillance and outbreak prediction, integrating more comprehensive data sources and refining predictive models to accommodate educational calendar variations is recommended.