论文标题
现在的区域和预测报告的数据延迟：迈向19 covid-19感染的监视
Regional now- and forecasting for data reported with delay: Towards surveillance of COVID-19 infections
论文作者
论文摘要
世界各地的政府继续采取行动遏制和减轻19日的传播。迅速发展的情况迫使官员和高管根据疾病传播的当前状态不断适应政策和社会疏远措施。在这种情况下，对于决策者来说，坚定地了解大流行状况以及对接下来几天的感染情况如何发展至关重要。但是，就像在许多其他强制性疾病及其他情况下一样，案件被延迟延迟到中央登记册，此延迟推迟了对事物状态的最新视野。我们提供了一个稳定的工具，用于监测当前感染水平，并在区域级别预测未来的感染数量。我们通过对尚未报告的病例以及未来感染的预测来实现这一目标。我们将模型应用于德国数据，我们的重点在于预测和解释地区传染性行为。
Governments around the world continue to act to contain and mitigate the spread of COVID-19. The rapidly evolving situation compels officials and executives to continuously adapt policies and social distancing measures depending on the current state of the spread of the disease. In this context, it is crucial for policymakers to have a firm grasp on what the current state of the pandemic is as well as to have an idea of how the infective situation is going to unfold in the next days. However, as in many other situations of compulsorily-notifiable diseases and beyond, cases are reported with delay to a central register, with this delay deferring an up-to-date view of the state of things. We provide a stable tool for monitoring current infection levels as well as predicting infection numbers in the immediate future at the regional level. We accomplish this through nowcasting of cases that have not yet been reported as well as through predictions of future infections. We apply our model to German data, for which our focus lies in predicting and explain infectious behavior by district.
