摘要翻译：
超传播使新型冠状病毒传播的研究复杂化。我提出了一个聚合病例数据的模型，该模型解释了超级传播并改进了统计推断。在贝叶斯框架中，该模型是根据德国60,000多例病例的数据进行估计的，这些病例有症状发作日期和年龄组。几个因素与传播的大幅减少有关：公众意识的提高、检测和追踪、关于当地发病率的信息以及高温。感染后的免疫力、学校和餐馆关闭、居家订单和强制面部遮盖与传播的较小减少有关。数据表明，公共距离规则增加了年轻人的传播。关于本地发病率的信息与传播减少高达44%(95%-CI:[40%,48%])有关，这表明行为适应对本地感染风险的显著作用。检测和追踪减少了15%的传播(95%-CI:[9%,20%])，其中老年人的影响最大。推断天气影响，我估计在较冷的季节传播增加53%(95%-CI:[43%，64%])。
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英文标题：
《Inference under Superspreading: Determinants of SARS-CoV-2 Transmission
in Germany》
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作者：
Patrick W. Schmidt
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最新提交年份：
2020
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分类信息：

一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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.
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