论文标题
超级推论：德国SARS-COV-2传播的决定因素
Inference under Superspreading: Determinants of SARS-CoV-2 Transmission in Germany
论文作者
论文摘要
超级扩展使对SARS-COV-2传播的研究变得复杂。我为汇总案例数据提出了一个模型，该模型说明了超级扩展并改善统计推断。在贝叶斯框架中，该模型是根据德国数据估算的，这些数据具有超过60,000例症状发作日期和年龄组的案例。几个因素与传播的强烈降低有关：公众意识上升，测试和追踪，局部发病率的信息和高温。感染后的免疫力，学校和餐厅关闭，全职订单以及强制性面部覆盖物与传输的减少相关。数据表明，公共距离规则增加了年轻人的传播。有关局部发病率的信息与多达44％的传播（95％-CI：[40％，48％]）有关，这表明行为适应局部感染风险的重要作用。测试和追踪将传播降低15％（95％-CI：[9％，20％]），其中的影响最强。推断天气影响，我估计传播在较冷的季节中增加了53％（95％-CI：[43％，64％]）。
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.
