  The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to
accurately evaluate the effects of implementing new or altering existing
nonpharmaceutical interventions. Since these interventions applied at the
societal level cannot be evaluated through traditional experimental means,
public health officials and other decision makers must rely on statistical and
mathematical epidemiological models. Nonpharmaceutical interventions are
typically focused on contacts between members of a population, and yet most
epidemiological models rely on homogeneous mixing which has repeatedly been
shown to be an unrealistic representation of contact patterns. An alternative
approach is individual based models (IBMs), but these are often time intensive
and computationally expensive to implement, requiring a high degree of
expertise and computational resources. More often, decision makers need to know
the effects of potential public policy decisions in a very short time window
using limited resources. This paper presents an estimation algorithm for an IBM
designed to evaluate nonpharmaceutical interventions. By utilizing recursive
relationships, our method can quickly compute the expected epidemiological
outcomes even for large populations based on any arbitrary contact network. We
utilize our methods to evaluate the effects of relaxing current social
distancing measures in Iowa, USA, at various times and to various degrees.
\verb!R! code for our method is provided in the supplementary material, thereby
allowing others to utilize our approach for other regions.
