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.

中文翻译：

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超级传播下的推论：SARS-CoV-2 在德国传播的决定因素

超级传播、漏报、报告延迟和混淆使疾病传播决定因素的统计推断变得复杂。使用德国 Covid-19 监测数据估计了在贝叶斯框架内解释这些因素的模型。根据症状出现的日期、地点和年龄组进行划分，可以识别特定年龄的传播变化、根据天气进行调整、报告的患病率以及检测和追踪。有几个因素与传播减少有关：公众意识的提高、有关当地流行情况的信息、检测和追踪、高温、居家令和餐馆关闭。然而，其他干预措施（包括关闭学校和强制戴口罩）仍然存在很大的不确定性。通过模拟研究讨论和检验了理清不同决定因素影响的挑战。从更广泛的角度来看，该研究说明了监测数据与人口统计信息和症状发作日期的潜力，可以在存在漏报和报告延迟的情况下改善推断。