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The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need toaccurately evaluate the effects of implementing new or altering existingnonpharmaceutical interventions. Since these interventions applied at thesocietal level cannot be evaluated through traditional experimental means,public health officials and other decision makers must rely on statistical andmathematical epidemiological models. Nonpharmaceutical interventions aretypically focused on contacts between members of a population, and yet mostepidemiological models rely on homogeneous mixing which has repeatedly beenshown to be an unrealistic representation of contact patterns. An alternativeapproach is individual based models (IBMs), but these are often time intensiveand computationally expensive to implement, requiring a high degree ofexpertise and computational resources. More often, decision makers need to knowthe effects of potential public policy decisions in a very short time windowusing limited resources. This paper presents an estimation algorithm for an IBMdesigned to evaluate nonpharmaceutical interventions. By utilizing recursiverelationships, our method can quickly compute the expected epidemiologicaloutcomes even for large populations based on any arbitrary contact network. Weutilize our methods to evaluate the effects of relaxing current socialdistancing measures in Iowa, USA, at various times and to various degrees.\verb!R! code for our method is provided in the supplementary material, therebyallowing others to utilize our approach for other regions.

Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in Iowa