Predicting Optimal Surveillance Strategies against SARS-CoV-2 in Virtual Day Care Centers using Individual-based Modeling

During the recent SARS-CoV-2 pandemic, governments worldwide have implemented severalinterventions to prevent the viral spread, such as the closure of day care centers for children. Thisresulted in negative effects on children’s health and well-being. Therefore, we aim to identifyoptimal surveillance strategies that prevent the closure of day care centers while minimizing theviral spread. In previous studies, we developed a stochastic individual-based SIR-type model that simulates theinfection spread in a day care center. We could show that the infection spread is governed byvarious testing regimes and infection surveillance policies such as test frequency, quarantine policy,and specific test days. In the current study, we extend our model for other variants, test types andconsider vaccination of individuals. Infection spread is modelled by a susceptible individualbecoming infected with a specific infection rate based on the current viral load of already infectedindividuals. Thus, for each individual, the corresponding temporal viral load kinetic needs to besimulated, which is done using a piecewise linear model. Herefore, the underlying parameters aresampled from distributions established by using a linear mixed effect model that was fitted toexperimental data. With this model we are able to predict optimal surveillance strategies that are required to allowdaycare centers to remain open during pandemic situations. It also allows the identification ofcommon viral characteristics and how they affect the spread of infection, making it a valuable toolfor other infectious diseases.