Optimizing Masks and Random Screening Test Usage within K-12 Schools - Yiwei Zhang; Maria E. Mayorga; Julie S. Ivy; Julie L. Swann (MDM Policy & Practice)
Background. COVID-19 tremendously disrupted the global health system. People of all ages were at risk of becoming infected. Frequent school closures raised concerns about both the physical and mental health of school-age children. Many studies discussed the effectiveness of various interventions, while few focused on optimizing such interventions. Methods. This study aimed to optimize the usage of random screening tests and masking requirements within K-12 schools. We simulated the disease transmission within a school setting and sought to find the most efficient schedules for schools to arrange their weekly screening tests and mask mandates. The goal was to minimize the number of the end-of-semester infections as well as to use the minimum number of resources. We applied the nondominated sorting genetic algorithm, NSGA-II, to solve this multiobjective optimization problem. We also compared results when polymerase chain reaction (PCR) versus rapid antigen tests were used. Results. The NSGA successfully found Pareto solutions when optimizing the end-of-semester infections, the total number of tests, and the total number of weeks masking. The screening tests and masks can serve as alternatives to one another when prioritizing minimizing the number of infections. In addition, due to the faster return of testing results and lower accuracy, the rapid antigen tests had a similar effect as PCR tests. Conclusion. Our study provides policy makers in K-12 schools with valuable insights. The conclusions derived from this research can serve as a solid foundation for making informative decisions regarding random screening tests and universal masking policies. Highlights Our simulation optimization framework was used to design weekly schedules for random screening tests and masking within K-12 schools to mitigate COVID-19 infections. We considered multiple objectives and applied the NSGA-II algorithm to find a Pareto solution set. Based on local context and preferences, decision makers can trade off testing and masking to achieve a similar number of end-of-semester infections. When a few weeks of masks are mandated, it is best to use them at the beginning of a semester.