Mitigation of Disease in Primary Schools

Implications for School Policies during the COVID-19 Pandemic

Abstract

Children in America spend an average of 1,000 hours in school each year. This accounts for one-sixth of their total waking hours (Department of Education 2008). Given this extensive time and exposure, school environments play a vital role in community health. This is especially true when it comes to the spread of infectious disease. Integral characteristics of traditional school environments, such as the high mixing rates of school children, general architectural environment, and the culture and hygiene of school-aged children, can lead to large outbreaks of viral diseases such as influenza (Gemmetto et al. 2014). This is particularly concerning because school-age children are an at-risk group to viral disease. Behavioral determinants affect children who are unaware of health risks around them and are typically unable to take actions to reduce their risk. Physiological determinants such as less developed immune systems and less capacity to resist vector-borne diseases and developmental determinants like immature organs make children more vulnerable to disease and damage in their early years (Gemmetto et al. 2014).

Past proposals for disease mitigation in schools include the closure of schools when an outbreak occurs. However, such measures come with high associated social and economic costs, making alternative, less disruptive interventions highly desirable (Gemmetto et al. 2014). Recently, disease modeling has allowed for an opportunity to design models of micro-interventions (Stehlé et al.) This is made possible through school-based viral surveillance, in particular, using high-resolution contact network data from school environments to model disease spread. Such surveillance can be an essential part of managing community health, as it provides early warnings for outbreaks (Gemmetto et al. 2014) and supports early action disease mitigation.

The aim of our project is to use high-resolution contact network data from a primary school and apply social network analysis techniques to understand the spread and mitigation of disease within it. In doing so, we hope that such methods will inform the way policymakers and healthcare officials model, understand, and address disease spread in schools to ensure that neither the education nor the health of students is compromised. In the sections to follow, we will introduce our dataset and share our findings using network visualizations and descriptors, community detection methods, and centrality measures. Finally, we will discuss how our findings can inform our understanding of school policy development during a pandemic and what implications that has for the COVID-19 crisis.