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受影响最严重的五个国家的新冠疫情自回归移动平均模型建模与预测

ARIMA modelling & forecasting of COVID-19 in top five affected countries.

作者信息

Sahai Alok Kumar, Rath Namita, Sood Vishal, Singh Manvendra Pratap

机构信息

Sri Sri University, Cuttack, India.

Sri Sri University, Cuttack, India.

出版信息

Diabetes Metab Syndr. 2020 Sep-Oct;14(5):1419-1427. doi: 10.1016/j.dsx.2020.07.042. Epub 2020 Jul 28.

DOI:10.1016/j.dsx.2020.07.042
PMID:32755845
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7386367/
Abstract

BACKGROUND AND AIMS

In a little over six months, the Corona virus epidemic has affected over ten million and killed over half a million people worldwide as on June 30, 2020. With no vaccine in sight, the spread of the virus is likely to continue unabated. This article aims to analyze the time series data for top five countries affected by the COVID-19 for forecasting the spread of the epidemic.

MATERIAL AND METHODS

Daily time series data from 15th February to June 30, 2020 of total infected cases from the top five countries namely US, Brazil, India, Russia and Spain were collected from the online database. ARIMA model specifications were estimated using Hannan and Rissanen algorithm. Out of sample forecast for the next 77 days was computed using the ARIMA models.

RESULTS

Forecast for the first 18 days of July was compared with the actual data and the forecast accuracy was using MAD and MAPE were found within acceptable agreement. The graphic plots of forecast data suggest that While Russia and Spain have reached the inflexion point in the spread of epidemic, the US, Brazil and India are still experiencing an exponential curve.

CONCLUSION

Our analysis shows that India and Brazil will hit 1.38 million and 2.47 million mark while the US will reach the 4.29 million mark by 31st July. With no effective cure available at the moment, this forecast will help the governments to be better prepared to combat the epidemic by ramping up their healthcare facilities.

摘要

背景与目的

截至2020年6月30日,在短短六个多月的时间里,新冠病毒疫情已在全球范围内感染了超过1000万人,并导致超过50万人死亡。由于尚无疫苗在望,病毒的传播可能会持续不减。本文旨在分析受新冠疫情影响最严重的五个国家的时间序列数据,以预测疫情的传播情况。

材料与方法

从在线数据库收集了2020年2月15日至6月30日美国、巴西、印度、俄罗斯和西班牙这五个国家的每日总感染病例时间序列数据。使用汉南和里桑南算法估计自回归积分移动平均(ARIMA)模型规格。使用ARIMA模型计算未来77天的样本外预测。

结果

将7月前18天的预测与实际数据进行比较,发现使用平均绝对偏差(MAD)和平均绝对百分比误差(MAPE)的预测准确性在可接受范围内。预测数据的图形显示,虽然俄罗斯和西班牙在疫情传播方面已达到拐点,但美国、巴西和印度仍处于指数曲线阶段。

结论

我们的分析表明,到7月31日,印度和巴西将分别达到138万和247万例的感染数,而美国将达到429万例。鉴于目前尚无有效的治疗方法,这一预测将有助于各国政府通过加强医疗设施来更好地应对疫情。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/b7ad83ed6d7a/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/64d9eee3de97/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/906d88b3d70a/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/0eef3fd7436a/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/482bbe656f33/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/b7ad83ed6d7a/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/64d9eee3de97/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/906d88b3d70a/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/0eef3fd7436a/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/482bbe656f33/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1006/7386367/b7ad83ed6d7a/gr5_lrg.jpg

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