Inference under superspreading: Determinants of SARS‐CoV‐2 transmission in Germany

• Published in: Statistics in medicine Vol. 43; no. 10; pp. 1933 - 1954
• Main Author: Schmidt, Patrick W.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 10.05.2024; Wiley Subscription Services, Inc
• Subjects: COVID-19; Infectious diseases; instantaneous reproductive number; Intervention; nonpharmaceutical interventions; Covid‐19; instantaneous reproductive number; infectious diseases; nonpharmaceutical interventions
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.10046

Superspreading, under‐reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid‐19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age‐specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay‐at‐home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under‐reporting and reporting delay.