Statistical distribution of novel coronavirus in Iran

Author:

Gholami Elham1ORCID,Mansori Kamyar2ORCID,Soltani-Kermanshahi Mojtaba3ORCID

Affiliation:

1. Treatment Deputy, Tehran University of Medical Sciences, Tehran, Iran.

2. Department of Biostatistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.

3. Social Determinants of Health Research Center, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran.

Abstract

Background and Aim: The coronavirus disease-2019 (COVID-19) pandemic – novel coronavirus (nCoV) spread worldwide in 2019, and by March 27, 2020, 199 countries, including Iran, were affected. Prevention and control of the infection is the most important public health priority today. The behavior prediction of COVID-19 is a significant problem. Therefore, in the present research, we compared the different distribution of COVID-19 cases based on the daily reported data in Iran. Materials and Methods: In this research, we compared the different distribution of COVID-19 cases based on the daily reported data in Iran. We focused on 36 initial data on deaths and new cases with confirmed 2019-nCoV infection in Iran based on official reports from governmental institutes. We used the three types of continuous distribution known as Normal, Lognormal, and Weibull. Results: Our study showed that the Weibull distribution was the best fit to the data. However, the parameters of distribution were different between data on new cases and daily deaths. Conclusion: According to the mean and median of the best-fitted distribution, we can expect to pass the peak of the disease. In other words, the death rate is decreasing. Similar behaviors of COVID-19 in both Iran and China, in the long run, can be seen.

Publisher

Veterinary World

Subject

Infectious Diseases,Public Health, Environmental and Occupational Health,Health Policy,General Veterinary

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces;Spatial and Spatio-temporal Epidemiology;2024-02

2. Markovian Analysis of Covid-19 Dynamics;African Journal of Mathematics and Statistics Studies;2021-04-19

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3