Inference under Superspreading: Determinants of SARS-CoV-2 Transmission in Germany

TL;DR

This study models SARS-CoV-2 transmission in Germany considering superspreading, identifying key factors like public awareness, testing, and weather that influence transmission rates and behavioral responses.

Contribution

A novel Bayesian model that accounts for superspreading in aggregated case data, providing insights into transmission determinants and behavioral effects during the pandemic.

Findings

Public awareness and testing significantly reduce transmission.

Weather increases transmission during colder seasons.

Behavioral responses to local risk impact transmission dynamics.

Abstract

Superspreading complicates the study of SARS-CoV-2 transmission. I propose a model for aggregated case data that accounts for superspreading and improves statistical inference. In a Bayesian framework, the model is estimated on German data featuring over 60,000 cases with date of symptom onset and age group. Several factors were associated with a strong reduction in transmission: public awareness rising, testing and tracing, information on local incidence, and high temperature. Immunity after infection, school and restaurant closures, stay-at-home orders, and mandatory face covering were associated with a smaller reduction in transmission. The data suggests that public distancing rules increased transmission in young adults. Information on local incidence was associated with a reduction in transmission of up to 44% (95%-CI: [40%, 48%]), which suggests a prominent role of behavioral adaptations to local risk of infection. Testing and tracing reduced transmission by 15% (95%-CI: [9%,20%]), where the effect was strongest among the elderly. Extrapolating weather effects, I estimate that transmission increases by 53% (95%-CI: [43%, 64%]) in colder seasons.