Using computational models to demonstrate the impact of distributed school closure authority on the spread of infectious disease

Previous generations of infectious disease modeling have assumed that mitigation strategies – such as school closures – are implemented immediately and uniformly. Across the US however, state and local jurisdictions have highly differentiated legal authorities and thus, such interventions are not immediately and uniformly applied. The purpose of this research was to analyze and characterize the distribution of legal authority for school closure as a disease mitigation strategy, and to impose this heterogeneity across jurisdictions upon computational models of infectious disease outbreaks. The distribution of this authority in each state was classified on a scale of 1-7, ranging from “ 1” indicating single state level authority (enabling the most uniform school closure), to 6 denoting shared authority at the local level authority (allowing potential disagreement and marked heterogeneity across local jurisdictions) to “7” (denoting no express statutorily derived school closure authority and thus the greatest potential for delay and heterogeneity). Utilizing qualitative research methodologies to characterize the content of the school closure laws enabled the application of law and legal authority to computational modeling. Applying this scheme of distributed school closure authority across jurisdictions to computational models revealed marked differences in the spread of infectious disease as a result of delayed and heterogeneous school closures. Modeling the potential impact of the distribution of this authority can inform critical decisions such as when and how to most effectively implement school closure as a disease mitigation strategy.