{"title":"估计COVID-19感染病例的隐藏模型和趋势","authors":"R. Arunachalam, T. Pakkirisamy","doi":"10.37896/sr8.5/012","DOIUrl":null,"url":null,"abstract":"\n The main aim of the present investigation is to estimate the hidden models and trends in COVID-19 infected cases in all the thirty seven district of from the period from 1st August,2020 to 31st December, 2020. Different statistical curve fitting tools like, Linear, Quadratic, S-Curve, Simple Exponential Smoothing, Holt’s Linear Exponential, Brown’s Linear Exponential Smoothing and Auto Regressive Integrated Moving Average models were employed to study the COVID-19 infected trends and it’s future predictions.","PeriodicalId":422413,"journal":{"name":"Strad Research","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Estimating hidden models and trends in COVID-19 Infected cases\",\"authors\":\"R. Arunachalam, T. Pakkirisamy\",\"doi\":\"10.37896/sr8.5/012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The main aim of the present investigation is to estimate the hidden models and trends in COVID-19 infected cases in all the thirty seven district of from the period from 1st August,2020 to 31st December, 2020. Different statistical curve fitting tools like, Linear, Quadratic, S-Curve, Simple Exponential Smoothing, Holt’s Linear Exponential, Brown’s Linear Exponential Smoothing and Auto Regressive Integrated Moving Average models were employed to study the COVID-19 infected trends and it’s future predictions.\",\"PeriodicalId\":422413,\"journal\":{\"name\":\"Strad Research\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Strad Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37896/sr8.5/012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Strad Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37896/sr8.5/012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimating hidden models and trends in COVID-19 Infected cases
The main aim of the present investigation is to estimate the hidden models and trends in COVID-19 infected cases in all the thirty seven district of from the period from 1st August,2020 to 31st December, 2020. Different statistical curve fitting tools like, Linear, Quadratic, S-Curve, Simple Exponential Smoothing, Holt’s Linear Exponential, Brown’s Linear Exponential Smoothing and Auto Regressive Integrated Moving Average models were employed to study the COVID-19 infected trends and it’s future predictions.
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