论文标题
COVID-19的Sikjalpha模型的变化，预测和场景预测
The Variations of SIkJalpha Model for COVID-19 Forecasting and Scenario Projections
论文作者
论文摘要
我们提出了在Covid-19大流行（2020年初）开头的Sikjalpha模型。从那时起，随着大流行的发展，添加了更多的复杂性来捕获可以帮助预测未来情况的关键因素和变量。在整个大流行中，已经组织了多模型的合作努力，以预测Covid-19和长期情景预测的短期结果（病例，死亡和住院）。我们已经参加了五项这样的努力。本文介绍了Sikjalpha模型及其许多版本的演变，这些版本自大流行以来一直用于提交这些协作努力。具体而言，我们表明Sikjalpha模型是一类流行病学模型的近似值。我们演示了该模型如何用于合并各种复杂性，包括报告不足，多种变体，免疫力减弱和接触率以及产生概率输出。
We proposed the SIkJalpha model at the beginning of the COVID-19 pandemic (early 2020). Since then, as the pandemic evolved, more complexities were added to capture crucial factors and variables that can assist with projecting desired future scenarios. Throughout the pandemic, multi-model collaborative efforts have been organized to predict short-term outcomes (cases, deaths, and hospitalizations) of COVID-19 and long-term scenario projections. We have been participating in five such efforts. This paper presents the evolution of the SIkJalpha model and its many versions that have been used to submit to these collaborative efforts since the beginning of the pandemic. Specifically, we show that the SIkJalpha model is an approximation of a class of epidemiological models. We demonstrate how the model can be used to incorporate various complexities, including under-reporting, multiple variants, waning of immunity, and contact rates, and to generate probabilistic outputs.
