COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
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DOI：
10.3390/app10113880
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发表时间：
2020-06-01
影响因子：
2.7
通讯作者：
Kotsiantis, Sotiris
中科院分区：
文献类型：
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作者：
Papastefanopoulos, Vasilis;Linardatos, Pantelis;Kotsiantis, Sotiris
持续不断的新冠肺炎疫情已在全球范围内引发社会经济动荡，迫使各国政府采取极端措施来减少疫情的蔓延。能够准确预测疫情何时达到高峰将大大减少疾病的影响，因为这将使各国政府能够相应地改变其政策，并提前计划所需的预防步骤，如公共卫生宣传、提高公民认识和提高卫生系统的能力。这项研究调查了冠状病毒暴发检测的各种时间序列建模方法在截至2020年5月4日确诊病例数量最多的10个不同国家的准确性。对于这些国家中的每一个，制定了六种不同的时间序列方法，并分别使用两个公开提供的关于病毒在每个国家的发展和每个国家的人口的数据集进行了比较。结果表明，给定使用对一小部分人口的实际测试产生的数据，机器学习时间序列方法可以学习和扩展，以准确估计未来将受到影响的总人口的百分比。
The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future.