the KITE framework: a KIndergarten Test scenario Evaluator for optimizing viral infection surveillance by Individual-based Modeling

During the recent SARS-CoV-2 pandemic, governments worldwide have implemented several interven-tions to prevent the viral spread, such as the closure of day care centers for children. This resulted in negativeeffects on children’s health and well-being [6]. Therefore, we aim to identify optimal surveillance strategiesthat prevent the closure of day care centers while minimizing the viral spread. In previous studies, we developed a stochastic individual-based SIR-type model that simulates the infectionspread in a day care center [1, 2]. We could show that the infection spread is governed by various testingregimes 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 and consider vaccination of individ-uals. Infection spread is modelled by a susceptible individual becoming infected with a specific infectionrate based on the current viral load of already infected individuals [5]. Thus, for each individual, the cor-responding temporal viral load kinetic needs to be simulated, which is done using a piecewise linear model[4]. Herefore, the underlying parameters are sampled from distributions established by using a linear mixedeffect model that was fitted to experimental data [3]. With this model we are able to predict optimal surveillance strategies that are required to allow child carecenters to remain open during pandemic situations. It also allows the identification of common viral charac-teristics and how they affect the spread of infection, making it a valuable tool for other infectious diseases. Additionally, we designed a graphical user interface with the goal to aid policy makers to predict optimal surveillance strategies without the need to have programming skills during future pandemic scenarios.