Forecasting of COVID19 per regions using ARIMA models and polynomial functions
Forecasting of COVID19 per regions using ARIMA models and polynomial functions
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DOI：
10.1016/j.asoc.2020.106610
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发表时间：
2020-11-01
影响因子：
8.7
通讯作者：
Perez-Meana, Hector
中科院分区：
文献类型：
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作者：
Hernandez-Matamoros, Andres;Fujita, Hamido;Perez-Meana, Hector
2019冠状病毒病是一种全球性威胁，因此，世界各地的研究一直集中在诸如检测、预防、治疗和预测等主题上。不同的分析提出了预测这种流行病演变的模型。这些分析为特定的地理区域、特定的国家提出模型，或者创建一个全球模型。这些模型使我们有可能预测病毒的行为，它可以用来制定未来的应对计划。这项工作通过6个地理区域（大洲），从不同的角度分析了COVID-19在全球的传播情况。我们建议在同一地理区域的国家之间建立一种关系，以预测病毒的发展。同一地理区域的国家具有具有相似值（可量化和不可量化）的变量，这些变量影响病毒的传播。我们提出了一种算法来执行和评估分布在6个地区的145个国家的ARIMA模型。然后，我们使用ARIMA参数、每100万人的人口、病例数和多项式函数构建了这些地区的模型。该方案能够预测新冠肺炎病例，RMSE平均值为144.81。本文的主要成果是展示了COVID-19行为与一个地区人口之间的关系，这些结果为我们提供了创建更多模型的机会，可以使用湿度、气候、文化等变量来预测COVID-19行为。(C) 2020 Elsevier B.V.版权所有
COVID-2019 is a global threat, for this reason around the world, researches have been focused on topics such as to detect it, prevent it, cure it, and predict it. Different analyses propose models to predict the evolution of this epidemic. These analyses propose models for specific geographical areas, specific countries, or create a global model. The models give us the possibility to predict the virus behavior, it could be used to make future response plans. This work presents an analysis of COVID-19 spread that shows a different angle for the whole world, through 6 geographic regions (continents). We propose to create a relationship between the countries, which are in the same geographical area to predict the advance of the virus. The countries in the same geographic region have variables with similar values (quantifiable and non-quantifiable), which affect the spread of the virus. We propose an algorithm to performed and evaluated the ARIMA model for 145 countries, which are distributed into 6 regions. Then, we construct a model for these regions using the ARIMA parameters, the population per 1M people, the number of cases, and polynomial functions. The proposal is able to predict the COVID-19 cases with a RMSE average of 144.81. The main outcome of this paper is showing a relation between COVID-19 behavior and population in a region, these results show us the opportunity to create more models to predict the COVID-19 behavior using variables as humidity, climate, culture, among others. (C) 2020 Elsevier B.V. All rights reserved.