A Missing Data on Covid-19 Forecasts

R. Isea
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Abstract

Mathematical and computational studies of Covid-19 have underestimated the influence that other countries have on their daily records. To visualize this, a Granger causality analysis was implemented in Python to determine if the cases registered in Brazil, Chile, Colombia, Ecuador, Panama, Paraguay, Peru and the USA have any effect on Venezuela, and between all of them. Finally, this paper highlights the need to incorporate causality analysis employing only the cases of Covid-19 to improve mid and long term forecasts.
Covid-19预测数据缺失
关于Covid-19的数学和计算研究低估了其他国家对其日常记录的影响。为了可视化这一点,在Python中实现了格兰杰因果分析,以确定在巴西、智利、哥伦比亚、厄瓜多尔、巴拿马、巴拉圭、秘鲁和美国登记的病例是否对委内瑞拉有任何影响,以及它们之间的影响。最后,本文强调需要纳入仅使用Covid-19病例的因果关系分析,以改进中长期预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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