Pandemic preparedness: Individual-based modeling of day care centers predicts optimal surveillance strategies againts pandemic viral diseases

IntroductionDuring the COVID-19 pandemic, interventions were implemented to prevent the viral spread, such as the closure of day care centers (DCC). This resulted in negative effects on children's well-being [1]. ObjectivesWe identify optimal surveillance strategies that prevent the closure of DCC while minimizing the viral spread during a pandemic. Materials & MethodsWe developed a stochastic individual-based SIR-type model that simulates the infection spread in a virtual DCC [2]. We could show that the infection spread is governed by testing regimes and infection surveillance policies such as test frequency, quarantine policy and specific test days. We now extend our model to other viruses such as Influenza A/H1N1 (pdm 09) and showcase a graphical user interface (GUI). Infection spread is modeled by calculating infection rates based on longitudinal viral load [3] derived from experimental data found in literature [4]. ResultsOur framework predicts an optimal surveillance strategy that consists of testing 50% of children twice per week using rapid antigen tests while only isolating a positively tested individual without closing the DCC. ConclusionWith this framework we are able to predict optimal surveillance strategies required to allow DCC to remain open during pandemic situations. The GUI aims at aiding policy makers without coding experience during future viral pandemics. [1] Marchetti, D. et al. The Effect of Parent Psychological Distress on Child Hyperactivity/Inattention During the COVID-19 Lockdown: Testing the Mediation of Parent Verbal Hostillity and Child Emotional Symptoms, Front Psychol 11, 567052 (2020). [2] Forster, J. et al. Feasibility of SARS-CoV-2 Surveillance Testing among Children and Childcare Workers at German Day Care Centers: A Nonrandomized Controlled Trial. JAMA Netw Open 5, (2022). [3] Jones, T.C. et al. Estimating infectiousness throughout SARS-CoV-2 infection course. Science (1979) 373, (2021). [4] Puhach, O., Meyer, B. & Eckerle, I. SARS-CoV-2 viral load and shedding kinetics. Nature Reviews Microbiology, 21(3), 147-161, (2023).