当前位置: X-MOL 学术J. Med. Internet Res. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Machine Learning–Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation
Journal of Medical Internet Research ( IF 7.4 ) Pub Date : 2021-04-23 , DOI: 10.2196/26628
Arnold Ys Yeung 1, 2 , Francois Roewer-Despres 1, 2 , Laura Rosella 3 , Frank Rudzicz 1, 2, 4
Affiliation  

Background: National governments worldwide have implemented nonpharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects. Objective: The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth—the percentage change in total cumulative cases—across 14 days for 114 countries using nonpharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national sociocultural norms. Methods: We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non–time series machine learning models in predicting confirmed infection growth. We used three validation methods—in-distribution, out-of-distribution, and country-based cross-validation—for the evaluation, each of which was applicable to a different use case of the models. Results: Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959) and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression. Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case. Conclusions: This work provides new considerations in using machine learning techniques with nonpharmaceutical interventions and cultural dimensions as metrics to predict the national growth of confirmed COVID-19 infections.

This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.


中文翻译:

使用非药物干预措施和文化维度指标,基于机器学习预测 114 个国家确诊的 COVID-19 感染病例的增长:模型开发和验证

背景:世界各国政府已实施非药物干预措施来控制 COVID-19 大流行并减轻其影响。目的:本研究的目的是使用非药物干预指标和文化维度指标,调查 114 个国家未来 14 天内每日全国确诊的 COVID-19 感染增长的预测(总累计病例的百分比变化),这些指标表明具体的国家社会文化规范。方法:我们结合牛津 COVID-19 政府响应跟踪数据集、Hofstede 文化维度和每日报告的 COVID-19 感染病例数来训练和评估五个非时间序列机器学习模型,以预测确诊的感染增长。我们使用三种验证方法(分布内、分布外和基于国家/地区的交叉验证)进行评估,每种方法都适用于模型的不同用例。结果:我们的结果表明,分布内方法的标签和预测之间的 R2 值较高(0.959),而分布外和基于国家/地区的交叉验证方法(分别为 0.513 和 0.574)则具有中等 R2 值(分别为 0.513 和 0.574)。森林和自适应增强(AdaBoost)回归。尽管这些模型可用于预测已确认的感染增长,但从这三个任务获得的不同准确度表明用例的强大影响。结论:这项工作为使用机器学习技术与非药物干预措施和文化维度作为预测全国确诊的 COVID-19 感染增长的指标提供了新的考虑。

这只是摘要。在 JMIR 网站上阅读全文。JMIR 是互联网时代电子健康和医疗保健领域领先的开放获取期刊。
更新日期:2021-04-23
down
wechat
bug