Doornik Jurgen A, Castle Jennifer L, Hendry David F
Nuffield College, Oxford, UK.
Climate Econometrics and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford, UK.
Soc Sci Q. 2021 Sep;102(5):2070-2087. doi: 10.1111/ssqu.13008. Epub 2021 Aug 7.
We analyze the number of recorded cases and deaths of COVID-19 in many parts of the world, with the aim to understand the complexities of the data, and produce regular forecasts.
The SARS-CoV-2 virus that causes COVID-19 has affected societies in all corners of the globe but with vastly differing experiences across countries. Health-care and economic systems vary significantly across countries, as do policy responses, including testing, intermittent lockdowns, quarantine, contact tracing, mask wearing, and social distancing. Despite these challenges, the reported data can be used in many ways to help inform policy. We describe how to decompose the reported time series of confirmed cases and deaths into a trend, seasonal, and irregular component using machine learning methods.
This decomposition enables statistical computation of measures of the mortality ratio and reproduction number for any country, and we conduct a counterfactual exercise assuming that the United States had a summer outcome in 2020 similar to that of the European Union. The decomposition is also used to produce forecasts of cases and deaths, and we undertake a forecast comparison which highlights the importance of seasonality in the data and the difficulties of forecasting too far into the future.
Our adaptive data-based methods and purely statistical forecasts provide a useful complement to the output from epidemiological models.
我们分析了世界许多地区新冠病毒病（COVID-19）的记录病例数和死亡数，旨在了解数据的复杂性，并进行定期预测。
导致COVID-19的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）病毒已影响到全球各个角落的社会，但各国的经历差异巨大。各国的医疗保健和经济系统差异显著，政策应对措施也各不相同，包括检测、间歇性封锁、隔离、接触者追踪、戴口罩和保持社交距离。尽管存在这些挑战，但报告的数据可用于多种方式以帮助为政策提供信息。我们描述了如何使用机器学习方法将报告的确诊病例和死亡时间序列分解为趋势、季节性和不规则成分。
这种分解能够对任何国家的死亡率和繁殖数进行统计计算，并且我们进行了一项反事实分析，假设美国在2020年夏季的情况与欧盟类似。该分解还用于预测病例和死亡情况，并且我们进行了预测比较，突出了数据中季节性的重要性以及预测未来太远的困难。
我们基于数据的自适应方法和纯统计预测为流行病学模型的输出提供了有益的补充。
