Narayana Darapaneni, Shyamal Dhua, Nikita Khare, K. Ayush, K. N., Supriya Ghodke, Abhishek Rajput, Saswat P Beurik, A. Paduri
{"title":"利用SIR和先知模型预测印度群体免疫接种活动","authors":"Narayana Darapaneni, Shyamal Dhua, Nikita Khare, K. Ayush, K. N., Supriya Ghodke, Abhishek Rajput, Saswat P Beurik, A. Paduri","doi":"10.1109/AIIoT52608.2021.9454186","DOIUrl":null,"url":null,"abstract":"This paper aims to study the COVID-19 vaccination drive in India to forecast the time, it will take vaccinate the minimum number of population for achieving herd immunity. As per the government data on 25th March, 2021, a total of 5,55,04,440 doses have been administered as first dose and 85,02,968 as the second dose, which is just a mere fraction of the total population of India which stands at 1.3 billion. As the number of cases are rising, considering the situation, it is important to expedite the drive and follow strict restrictions to achieve herd immunity. A simulation of the SIR model has been created to identify the effective reproduction number (Re), and then through time series analysis using Prophet model, the conclusion has been drawn for the number of days it will take to vaccinate enough population to achieve herd immunity. As an initial step, we will be fitting the data available for COVID-19 for India in the SIR model which is a set of three Ordinary Differential Equations (ODE). The results from the ODEs will be used to determining the initial Re which will be matched with the data set. Once confirming the Re value present in data set, the same will be passed to the data-driven forecasting time series model to get insights and draw conclusions which will help authorities to help in planning the drive and implement necessary actions to avoid further growth of COVID-19 cases.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Forecasting Vaccination Drive In India for Herd Immunity using SIR and Prophet Model\",\"authors\":\"Narayana Darapaneni, Shyamal Dhua, Nikita Khare, K. Ayush, K. N., Supriya Ghodke, Abhishek Rajput, Saswat P Beurik, A. Paduri\",\"doi\":\"10.1109/AIIoT52608.2021.9454186\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to study the COVID-19 vaccination drive in India to forecast the time, it will take vaccinate the minimum number of population for achieving herd immunity. As per the government data on 25th March, 2021, a total of 5,55,04,440 doses have been administered as first dose and 85,02,968 as the second dose, which is just a mere fraction of the total population of India which stands at 1.3 billion. As the number of cases are rising, considering the situation, it is important to expedite the drive and follow strict restrictions to achieve herd immunity. A simulation of the SIR model has been created to identify the effective reproduction number (Re), and then through time series analysis using Prophet model, the conclusion has been drawn for the number of days it will take to vaccinate enough population to achieve herd immunity. As an initial step, we will be fitting the data available for COVID-19 for India in the SIR model which is a set of three Ordinary Differential Equations (ODE). The results from the ODEs will be used to determining the initial Re which will be matched with the data set. Once confirming the Re value present in data set, the same will be passed to the data-driven forecasting time series model to get insights and draw conclusions which will help authorities to help in planning the drive and implement necessary actions to avoid further growth of COVID-19 cases.\",\"PeriodicalId\":443405,\"journal\":{\"name\":\"2021 IEEE World AI IoT Congress (AIIoT)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE World AI IoT Congress (AIIoT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIIoT52608.2021.9454186\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE World AI IoT Congress (AIIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIIoT52608.2021.9454186","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Forecasting Vaccination Drive In India for Herd Immunity using SIR and Prophet Model
This paper aims to study the COVID-19 vaccination drive in India to forecast the time, it will take vaccinate the minimum number of population for achieving herd immunity. As per the government data on 25th March, 2021, a total of 5,55,04,440 doses have been administered as first dose and 85,02,968 as the second dose, which is just a mere fraction of the total population of India which stands at 1.3 billion. As the number of cases are rising, considering the situation, it is important to expedite the drive and follow strict restrictions to achieve herd immunity. A simulation of the SIR model has been created to identify the effective reproduction number (Re), and then through time series analysis using Prophet model, the conclusion has been drawn for the number of days it will take to vaccinate enough population to achieve herd immunity. As an initial step, we will be fitting the data available for COVID-19 for India in the SIR model which is a set of three Ordinary Differential Equations (ODE). The results from the ODEs will be used to determining the initial Re which will be matched with the data set. Once confirming the Re value present in data set, the same will be passed to the data-driven forecasting time series model to get insights and draw conclusions which will help authorities to help in planning the drive and implement necessary actions to avoid further growth of COVID-19 cases.