Age-specific contribution of contacts to transmission of SARS-CoV-2 in Germany

Abstract

Current estimates of pandemic SARS-CoV-2 spread in Germany using infectious disease models often do not use age-specific infection parameters and are not always based on age-specific contact matrices of the population. They also do usually not include setting- or pandemic phase-based information from epidemiological studies 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 different phases of the COVID-19 pandemic in Germany. We developed a deterministic SEIRS model using a pre-pandemic contact matrix. The model was optimized to fit age-specific SARS-CoV-2 incidences reported by the German National Public Health Institute (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 studies. Taking age-specific underreporting into account, younger adults and teenagers were identified in the modeling study as relevant contributors to infections during the first three pandemic waves in Germany. For the fifth wave, the Delta to Omicron transition, only age-specific parametrization reproduces the observed relative and absolute increase in pediatric hospitalizations in Germany. Taking into account age-specific underdetection did not change considerably how much contacts in schools contributed to the total burden of infection in the population (up to 12% with open schools under hygiene measures in the third wave). Accounting for the pandemic phase and age-specific underreporting is important to correctly identify those groups of the population in which quarantine, testing, vaccination, and contact-reduction measures are likely to be most effective and efficient. Age-specific parametrization is also highly relevant to generate informative age-specific output for decision makers and resource planers.

Keywords: Age-specific viral spread; COVID-19; Compartment models; Epidemiological modeling; Infection severity; SARS-CoV-2; Underdetection.

Fig. 1

Schematic illustration of the extended SEIRS model

Fig. 2

Estimated weekly scaling parameters (CI: 95%) per contact by age group without accounting for underdetection

Fig. 3

The effective reproduction number is calculated by fitting parameters for case a without and b with underdetection In the first few weeks of the first wave, there were artifacts due to a low number of cases, increased testing, etc. Therefore, the results of weekly calibration are unrealizable during the first few weeks

Fig. 4

Estimated absolute contribution of contacts to the transmission

Fig. 5

Estimated force of infection in contribution of contacts by age group in the first wave

Fig. 6

Estimated contribution of contact by age group to transmission in the first wave

Fig. 7

Estimated force of infection in contribution of contacts by age group in the second wave

Fig. 8

Estimated contribution of contact by age group to transmission in the second wave

Fig. 9

Estimated force of infection in contribution of contacts by age group in the third wave

Fig. 10

Estimated contribution of contact by age group to transmission in the third wave

Fig. 11

Estimated force of infection by age group for case a without and b with underdetection

Fig. 12

Estimated absolute contribution of contacts to the transmission accounting for underdetection

Fig. 13

Estimated force of infection in schools by age group in the third wave for case a without and b with underdetection

Fig. 14

Estimated proportion of infection (CI: 95%) due to contact with infected people in schools in the third wave for case a without and b with underdetection

Fig. 15

Modeling estimates the simulated hypothetical waves of new hospitalizations in adults (red) and children (blue) (a & b) and hospitalization incidences as hospitalizations/100.000 persons in adults and children (c & d) for the simulated Delta (a & c) and Omicron (b & d) waves