论文标题
从数据驱动的角度重新思考COVID-19的病例死亡比率
Rethinking Case Fatality Ratios for COVID-19 from a data-driven viewpoint
论文作者
论文摘要
COVID-19的病例死亡比率（CFR）很难估计。一个困难是由于忽略或高估报告与死亡之间的时间延迟。我们声称所有这些都会导致CFR的大错误和人为的时间依赖性。我们发现，对于每个国家 /地区，报告的病例与死亡与时间之间的时间滞后有一个独特的价值，这使它们之间的最佳相关性是一种特定的意义。我们发现，所得校正后的CFR（死亡在此时间滞后，除以案件的划分）实际上在许多国家中，而且对于全世界而言也是如此。可以通过简单的数据驱动算法找到每个国家的最佳时间滞后和恒定CFR。传统的CFR（忽略时间滞后）是微不足道的，其演变很难量化。我们校正后的CFR是恒定的，因此每个国家的大流行是一个重要的指数，可以从早期的数据中推断出，从而促进了对Covid-19死亡率的早期估计。
The case fatality ratio (CFR) for COVID-19 is difficult to estimate. One difficulty is due to ignoring or overestimating time delay between reporting and death. We claim that all of these cause large errors and artificial time dependence of the CFR. We find that for each country, there is a unique value of the time lag between reported cases and deaths versus time, that yields the optimal correlation between them is a specific sense. We find that the resulting corrected CFR (deaths shifted back by this time lag, divided by cases) is actually constant over many months, for many countries, but also for the entire world. This optimal time lag and constant CFR for each country can be found through a simple data driven algorithm. The traditional CFR (ignoring time lag) is spuriously time-dependent and its evolution is hard to quantify. Our corrected CFR is constant over time, therefore an important index of the pandemic in each country, and can be inferred from data earlier on, facilitating improved early estimates of COVID-19 mortality.
