论文标题
如何最好地预测Covid-19的新感染的每日数量
How to Best Predict the Daily Number of New Infections of Covid-19
论文作者
论文摘要
关于Covid-19的新感染日常感染的知识很重要，因为它是导致封锁和紧急医疗保健措施的政治决定的基础。我们以德国为例来说明官方数字的缺点，至少在德国，仅延迟了几天的时间，并在周末被严重报道（超过40％）。这些缺点概述了急需替代数据源的需求。约翰·霍普金斯大学（JHU）系统科学与工程中心提供的另一项广泛引用的来源也平均使德国偏离官方数字79％。我们认为Google搜索和Twitter数据应该补充官方数字。他们的预测甚至比约翰·霍普金斯大学的原始价值更好，并在几天内做到这一点。这两个数据源也可以在不存在或认为不可靠的世界部分中使用。
Knowledge about the daily number of new infections of Covid-19 is important because it is the basis for political decisions resulting in lockdowns and urgent health care measures. We use Germany as an example to illustrate shortcomings of official numbers, which are, at least in Germany, disclosed only with several days of delay and severely underreported on weekends (more than 40%). These shortcomings outline an urgent need for alternative data sources. The other widely cited source provided by the Center for Systems Science and Engineering at Johns Hopkins University (JHU) also deviates for Germany on average by 79% from the official numbers. We argue that Google Search and Twitter data should complement official numbers. They predict even better than the original values from Johns Hopkins University and do so several days ahead. These two data sources could also be used in parts of the world where official numbers do not exist or are perceived to be unreliable.
