论文标题
短期COVID-19的后文预报
Short-Term Covid-19 Forecast for Latecomers
论文作者
论文摘要
COVID-19案件的数量在全球范围内急剧增加。因此，在接下来的几天中，可靠的案件数量可靠的预测至关重要。我们提出了一种简单的统计方法，以实时预测199例COVID案件的数量和死亡人数的短期实时预测，即后来的国家 - 即疾病案件开始出现了一段时间的国家。特别是，我们提出了具有误差校正机制的受惩罚（LASSO）回归，以构建后来几天中大流行阶段的其他国家 /地区的模型。通过跟踪这些国家的案件和死亡人数，我们通过自适应滚动窗户计划预测后来的案件和死亡人数。我们将此方法应用于巴西，并表明（到目前为止）表现非常出色。这些预测旨在促进对卫生系统能力的更好短期管理。
The number of Covid-19 cases is increasing dramatically worldwide. Therefore, the availability of reliable forecasts for the number of cases in the coming days is of fundamental importance. We propose a simple statistical method for short-term real-time forecasting of the number of Covid-19 cases and fatalities in countries that are latecomers -- i.e., countries where cases of the disease started to appear some time after others. In particular, we propose a penalized (LASSO) regression with an error correction mechanism to construct a model of a latecomer in terms of the other countries that were at a similar stage of the pandemic some days before. By tracking the number of cases and deaths in those countries, we forecast through an adaptive rolling-window scheme the number of cases and deaths in the latecomer. We apply this methodology to Brazil, and show that (so far) it has been performing very well. These forecasts aim to foster a better short-run management of the health system capacity.
