COVID-19 - Novel Short Term Prediction Methods

Author:

Raju Sanjay1,B.S. Rishiikeshwer1,T. Aswin Shriram1,G.R. Brindha1,B. Santhi1,N. Bharathi2

Affiliation:

1. SASTRA Deemed to be University,Thanjavur,India,

2. SRM Institute of Science and Technology,Chennai,India,

Abstract

The recent outbreak of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2), also called COVID-19, is a major global health problem due to an increase in mortality and morbidity. The virus disturbs the respirational process of a human being and is highly spreadable. The current distressing COVID-19 pandemic has caused heavy financial crashing and the assets and standards of the highly impacted countries being compromised. Therefore, prediction methods should be devised, supporting the development of recovery strategies. To make accurate predictions, understanding the natural progression of the disease is very important.<br><br>The developed novel mathematical models may help the policymakers and government control the infection and protect society from this pandemic infection. Due to the nature of the data, the uncertainty may lead to an error in the estimation. In this scenario, the uncertainty arises due to the dynamic rate of change based on time in the infectious count because of the different stages of lockdowns, population density, social distancing, and many other reasons concerning demography. The period between exposure to the virus and the first symptom of infection is large compared to other viruses. It is mandatory to follow the infected persons.<br><br>The exposure needs to be controlled to prevent the spreading in the long term, and the infected people must be in isolation for the above-mentioned period to avoid short-term infections. Officials need to know about the long-term scenario as well as the shortterm for policymaking. Many studies are focusing on long-term forecasting using mathematical modelling. For the short-term prediction, this paper proposed two algorithms: 1) to predict next-day count from the past 2 days data irrespective of population size with less error rate and 2) to predict the next M days based on the deviation of the rate of change in previous N-days active cases.<br><br>The proposed methods can be adopted by government officials, researchers, and medical professionals by developing a mobile application. So that they can use it whenever and wherever necessary. The mobile health (M-Health) App. helps the user to know the status of the pandemic state and act accordingly.<br>

Publisher

BENTHAM SCIENCE PUBLISHERS

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