Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework
Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework
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DOI：
10.1111/tbed.14492
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发表时间：
2022-03-13
影响因子：
4.3
通讯作者：
Du, Xiangjun
中科院分区：
文献类型：
--
作者：
Cao, Zicheng;Qiu, Zekai;Du, Xiangjun
冠状病毒病2019年(新冠肺炎)已成为一场全球大流行，并在许多国家继续流行，出现多次反弹浪潮。新冠肺炎传播的驱动因素及其数量贡献，特别是对反弹浪的贡献，研究得还不够深入。收集了截至2021年6月30日的39个国家的政策、旅行、医疗、社会经济、环境、突变和疫苗相关的多维时间序列数据，并使用可解释的机器学习框架(XGBoost模型和Shapley加法解释解释)，以日有效再生产次数为指标，系统分析了多因素对新冠肺炎传播的影响。基于疫苗前时代的模型，政策相关因素被证明是新冠肺炎传播的主要驱动因素，贡献率为60.81%。在后疫苗时代，政策性因素的贡献率降至28.34%，同时国内航班等旅行相关因素的贡献率上升，突变相关因素(16.49%)和疫苗相关因素(7.06%)的贡献出现。对于单峰国家，在上升和下降阶段，占主导地位的是与政策有关的因素，总体贡献率分别为33.7%和37.7%。对于双峰期国家，反弹阶段的因素贡献了45.8%，政策相关因素在反弹阶段(32.6%)和衰退阶段(25.0%)的贡献最大。对于多高峰国家，Delta变种、国内航班(当月)和每日疫苗接种人口是三个最大的贡献者(分别为8.12%、7.59%和7.26%)。基于这些发现建立了预测反弹风险的预测模型，在疫苗接种前和接种后的精度分别为0.78和0.81。这些发现定量地展示了新冠肺炎传播的系统性驱动因素，本研究提出的框架将有助于有针对性地预防和控制正在进行的新冠肺炎大流行。
Coronavirus disease 2019 (COVID-19) has become a global pandemic and continues to prevail with multiple rebound waves in many countries. The driving factors for the spread of COVID-19 and their quantitative contributions, especially to rebound waves, are not well studied. Multidimensional time-series data, including policy, travel, medical, socioeconomic, environmental, mutant and vaccine-related data, were collected from 39 countries up to 30 June 2021, and an interpretable machine learning framework (XGBoost model with Shapley Additive explanation interpretation) was used to systematically analyze the effect of multiple factors on the spread of COVID-19, using the daily effective reproduction number as an indicator. Based on a model of the pre-vaccine era, policy-related factors were shown to be the main drivers of the spread of COVID-19, with a contribution of 60.81%. In the post-vaccine era, the contribution of policy-related factors decreased to 28.34%, accompanied by an increase in the contribution of travel-related factors, such as domestic flights, and contributions emerged for mutant-related (16.49%) and vaccine-related (7.06%) factors. For single-peak countries, the dominant ones were policy-related factors during both the rising and fading stages, with overall contributions of 33.7% and 37.7%, respectively. For double-peak countries, factors from the rebound stage contributed 45.8% and policy-related factors showed the greatest contribution in both the rebound (32.6%) and fading (25.0%) stages. For multiple-peak countries, the Delta variant, domestic flights (current month) and the daily vaccination population are the three greatest contributors (8.12%, 7.59% and 7.26%, respectively). Forecasting models to predict the rebound risk were built based on these findings, with accuracies of 0.78 and 0.81 for the pre- and post-vaccine eras, respectively. These findings quantitatively demonstrate the systematic drivers of the spread of COVID-19, and the framework proposed in this study will facilitate the targeted prevention and control of the ongoing COVID-19 pandemic.