Mitigation of infectious disease at school: targeted class closure vs school closure

Abstract: School environments are thought to play an important role in the community spread of airborne infections (e.g., influenza) because of the high mixing rates of school children. The closure of schools has therefore been proposed as efficient mitigation strategy, with however high social and economic costs: alternative, less disruptive interventions are highly desirable. The recent availability of high-resolution contact networks in school environments provides an opportunity to design micro-interventions and compare the outcomes of alternative mitigation measures. We consider mitigation measures that involve the targeted closure of school classes or grades based on readily available information such as the number of symptomatic infectious children in a class. We focus on the case of a primary school for which we have high-resolution data on the close-range interactions of children and teachers. We simulate the spread of an influenza-like illness in this population by using an SEIR model with asymptomatics and compare the outcomes of different mitigation strategies. We find that targeted class closure affords strong mitigation effects: closing a class for a fixed period of time -equal to the sum of the average infectious and latent durations- whenever two infectious individuals are detected in that class decreases the attack rate by almost 70% and strongly decreases the probability of a severe outbreak. The closure of all classes of the same grade mitigates the spread almost as much as closing the whole school. Targeted class closure strategies based on readily available information on symptomatic subjects and on limited information on mixing patterns, such as the grade structure of the school, can be almost as effective as whole-school closure, at a much lower cost. This may inform public health policies for the management and mitigation of influenza-like outbreaks in the community.

• The paper demonstrates that targeted class closures can reduce influenza outbreak attack rates by around 70% in schools.
• It employs an SEIR model with high-resolution sensor data to simulate disease spread among students and teachers.
• The study shows that class or grade closures nearly match full school shutdowns in effectiveness while minimizing socio-economic impact.

Mitigation of Infectious Disease at School: Targeted Class Closure vs School Closure

The paper at hand investigates targeted class closures as a nuanced intervention strategy for mitigating the spread of infectious diseases within school settings, contrasting it with the more conventional method of entire school closures. This study leverages the availability of high-resolution contact networks within a school environment, utilizing data from face-to-face interactions to assess the efficacy of more targeted approaches.

Methods and Findings

The authors employ an SEIR (Susceptible, Exposed, Infectious, Recovered) model to simulate the spread of an influenza-like illness among primary school children and teachers. The model incorporates data obtained from wearable proximity sensors to map interactions among students and between students and teachers. The study primarily compares three strategies: targeted class closure, targeted grade closure, and whole-school closure. The triggering of interventions is based on the number of symptomatic infectious individuals detected.

Significantly, the findings indicate that targeted class closures can reduce the attack rate by approximately 70%, with the likelihood of a severe outbreak diminishing considerably. When two or more symptomatic cases are identified, closing a class for a duration equal to the sum of the average infectious and latent periods demonstrates substantial mitigation effectiveness. Furthermore, closing all classes of the same grade almost parallels the impact of closing the entire school, but with considerably reduced socio-economic costs.

Implications

The implications of this research are twofold: practical and theoretical. Practically, the study suggests that targeted class closures, based on symptomatic detection and limited data, offer a viable alternative to whole-school closures. This approach could guide public health policies aiming to manage and curb influenza outbreaks more cost-effectively, minimizing disruptions in educational activities and mitigating the socio-economic burden on families and the broader community. Theoretically, the research underscores the importance of detailed interaction data in developing effective epidemic models and targeted intervention strategies.

Future Directions

Future developments could include exploring the scalability of these targeted strategies across different school sizes and types. Given the reliance on high-resolution interaction data, more extensive longitudinal datasets could enhance model robustness. Additionally, integrating more complex variables, such as varying contact patterns outside school or during weekends and holidays, could refine predictions further. Another prospective avenue is extending these strategies to inter-school networks, evaluating cross-school transmission dynamics and the effectiveness of interventions at larger scales. Such advancements would potentiate refined, region-specific approaches to epidemic management in educational settings.

In conclusion, this paper advances the dialog on epidemic response within schools, presenting targeted class closures as a compelling alternative to whole-school closures, and charts a path for future research that could further hone these intervention models.