Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
PLoS One. 2024 May 20;19(5):e0302360. doi: 10.1371/journal.pone.0302360. eCollection 2024.
Attendance absences have a substantial impact on student's future physical and mental health as well as academic progress. Numerous personal, familial, and social issues are among the causes of student absences. Any kind of absence from school should be minimized. Extremely high rates of student absences may indicate the abrupt commencement of a serious school health crisis or public health crisis, such as the spread of tuberculosis or COVID-19, which provides school health professionals with an early warning. We take the extreme values in absence data as the object and attempt to apply the extreme value theory (EVT) to describe the distribution of extreme values. This study aims to predict extreme instances of student absences. School health professionals can take preventative measures to reduce future excessive absences, according to the predicted results. Five statistical distributions were applied to individually characterize the extreme values. Our findings suggest that EVT is a useful tool for predicting extreme student absences, thereby aiding preventative measures in public health.
缺勤对学生的身心健康和学业进步都有重大影响。学生缺勤的原因有很多，包括个人、家庭和社会问题。应尽量减少任何形式的缺课。学生缺勤率极高可能表明严重的学校卫生危机或公共卫生危机的突然爆发，例如结核病或 COVID-19 的传播，这为学校卫生专业人员提供了早期预警。我们以缺勤数据的极值为对象，尝试应用极值理论 (EVT) 来描述极值的分布。本研究旨在预测学生缺勤的极端情况。根据预测结果，学校卫生专业人员可以采取预防措施，减少未来的过度缺勤。我们应用了五种统计分布来分别刻画极值。我们的研究结果表明，EVT 是预测学生极端缺勤的有用工具，有助于公共卫生的预防措施。
