The variations of SIkJalpha model for COVID-19 forecasting and scenario projections
The variations of SIkJalpha model for COVID-19 forecasting and scenario projections
复制标题
用于 COVID-19 预测和情景预测的 SIkJalpha 模型的变化
DOI：
10.1016/j.epidem.2023.100729
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发表时间：
2023
期刊：
影响因子：
3.8
通讯作者：
Srivastava, Ajitesh
中科院分区：
文献类型：
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作者：
Srivastava, Ajitesh
我们在新冠肺炎大流行之初(2020年初)提出了SIkJAlpha模型。从那时起，随着大流行的演变，增加了更多的复杂性，以捕捉关键因素和变量，这些因素和变量可以帮助预测预期的未来情景。在整个大流行期间，组织了多模型协作努力来预测新冠肺炎的短期结果(病例、死亡和住院)和长期情景预测。我们已经参加了五次这样的努力。本文介绍了SIkJpha模型的演变及其自大流行开始以来用于提交这些合作努力的许多版本。具体地说，我们证明了SIkJpha模型是一类流行病模型的近似。我们演示了如何使用该模型来合并各种复杂性，包括漏报、多变种、免疫力减弱和接触率，并生成概率输出。
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.