Age-specific Contribution of Contacts to Transmission of SARS-CoV-2 in Germany

Abstract

Introduction: Current estimates of pandemic spread using infectious disease models in Germany for SARS-CoV-2 often do not use age-specific infection parameters and are not always based on known contact matrices of the population. They also do not usually include setting-based information of reported cases and do not account for age-specific underdetection of reported cases. Here, we report likely pandemic spread using an age-structured model to understand the age- and setting-specific contribution of contacts to transmission during all phases of the COVID-19 pandemic in Germany.Methods: We developed a deterministic SEIRS model using a pre-pandemic contact matrix. The model is optimized to fit reported age-specific SARS-CoV-2 incidences from the Robert Koch Institute, includes information on setting-specific reported cases in schools and integrates age and pandemic period-specific parameters for underdetection of reported cases deduced from a large population-based seroprevalence study. Results: We showed that taking underreporting into account, younger adults and teenagers are the main contributors to infections during the first three pandemic waves in Germany. Overall, the contribution of contacts in schools to the total cases in the population was below 10% during the third wave. Discussion: Accounting for the pandemic phase and age-specific underreporting seems important to correctly identify those parts of the population where quarantine, testing, vaccination, and contact-reduction measures are likely to be most effective and efficient. In the future, we will aim to compare current model estimates with currently emerging during-pandemic age-specific contact survey data.

Infection and transmission risks of COVID-19 in schools and their contribution to population infections in Germany: A retrospective observational study using nationwide and regional health and education agency notification data

“…We (d) estimated infection dynamics in schools and their contribution to population infections using a SEIRS (Susceptible-Exposed-Infectious-Recovered-Susceptible) model previously described [ 28 ]. This distinguishes between healthy but susceptible individuals, those infected but not yet infectious (exposed), and symptomatic and asymptomatic patients.…”

“…This way, we estimated the effect of the NPIs on cases in schools relative to cases in the population, thus estimating whether the NPIs in schools have a significantly different effect on the overall population as on the school population. We (d) estimated infection dynamics in schools and their contribution to population infections using a SEIRS (Susceptible-Exposed-Infectious-Recovered-Susceptible) model previously described [28]. This distinguishes between healthy but susceptible individuals, those infected but not yet infectious (exposed), and symptomatic and asymptomatic patients.…”

Bridging the gap – estimation of 2022/2023 SARS-CoV-2 healthcare burden in Germany based on multidimensional data from a rapid epidemic panel

“…We applied a deterministic SEIR (Susceptible-Exposed-Infectious-Recovered) model with a realistic age structure and contact behavior based on a social contact matrix 16 . The model has been previously described 22 . We included compartments for hospitalizations, patients in the intensive care unit (ICU), and deaths.…”

“…For the description of the population-based cohort, we followed STROBE 19 and adapted the WHO protocol for SARS-CoV-2 seroprevalence studies 20 . We used EPIFORGE 21,22 to describe the ODE model.…”

“…However, the actual second BA.5 wave had rather short but steep dynamics and a relevantly larger relative number of cases from the older age groups, resulting in an overall higher hospitalization risk per case during the second BA.5 wave. More accurate use of estimates of age-specific underdetection during the BA.5 summer wave might have helped to predict the larger contribution of elderly during the second BA.5 wave 22 .…”