Analysis and Forecasting Covid-19 Spread in India using Logistic Regression and Prophet Time Series

被引：0
|
作者：
Verghese, Ashok [1 ]
Sudalaimuthu, T. [2 ]
Visalaxi, S. [2 ]
机构：
[1] Hindustan Inst Technol & Sci, Chennai, Tamil Nadu, India
[2] Hindustan Inst Technol & Sci, Sch Comp Sci, Chennai, Tamil Nadu, India
来源：
2021 INTERNATIONAL CONFERENCE ON COMPUTATIONAL PERFORMANCE EVALUATION (COMPE-2021) | 2021年
关键词：
Artificial Intelligence (AI); Covid19; Logistic Regression (LR); Prophet; MODEL;
D O I：
10.1109/ComPE53109.2021.9752218
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Covid19 pandemic is infecting a large community across the globe. Nearly 29 million got affected due to covid in India. The cases are increasing day by day. There are confirmed cases along with recovery and fatality rate. Prediction of the growth /fatality rate is a challengeable one. This paper implements an Artificial Intelligence (AI) strategy for analyzing and predicting the covid cases across the nation on daily basis at various rates. It includes (a) Analysis of growth rate, (b) Prediction of Confirmed rate, (c) Prediction of Deceased Rate, (d) Analysis of Recovery rate, etc. Logistic regression (LR) is a classification problem that performs well on medical data. The proposed work implements logistic regression along with prophet methods for analyzing the time-based covid cases across India. This proposed work analyzes the active cases and perform them effectively with an accuracy of 0.96.
引用
收藏
页码：928 / 932
页数：5
相关论文
共 50 条
  • [1] Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models
    Almeshal, Abdullah M.
    Almazrouee, Abdulla I.
    Alenizi, Mohammad R.
    Alhajeri, Saleh N.
    [J]. APPLIED SCIENCES-BASEL, 2020, 10 (10):
  • [2] Forecasting of Covid-19 Using Time Series Regression Models
    Radwan, Akram M.
    [J]. 2021 PALESTINIAN INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (PICICT 2021), 2021, : 7 - 12
  • [3] Analysis and Prediction of COVID-19 using Regression Models and Time Series Forecasting
    Shaikh, Saud
    Gala, Jaini
    Jain, Aishita
    Advani, Sunny
    Jaidhar, Sagar
    Edinburgh, Mani Roja
    [J]. 2021 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2021), 2021, : 989 - 995
  • [4] Modelling intermittent time series and forecasting COVID-19 spread in the USA
    Sbrana, Giacomo
    [J]. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2023, 74 (02) : 465 - 475
  • [5] Time Series Analysis for Predicting Covid-19 Infection using Facebook Prophet Model
    Bazila Banu, A.
    Thirumalaikolundusubramanian, P.
    [J]. International Journal of COMADEM, 2021, 24 (03): : 23 - 26
  • [6] Impact of mobility on COVID-19 spread - A time series analysis
    Zargari, Faraz
    Aminpour, Nima
    Ahmadian, Mohammad Amir
    Samimi, Amir
    Saidi, Saeid
    [J]. TRANSPORTATION RESEARCH INTERDISCIPLINARY PERSPECTIVES, 2022, 13
  • [7] Forecasting of COVID-19 Cases in INDIA Using AReIMA and AR Time-Series Algorithm
    Prajapati, Dilip
    Kanojia, Mahendra
    [J]. PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR 2021), 2022, 417 : 361 - 370
  • [8] Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models
    Maleki, Mohsen
    Mahmoudi, Mohammad Reza
    Heydari, Mohammad Hossein
    Pho, Kim-Hung
    [J]. CHAOS SOLITONS & FRACTALS, 2020, 140 (140)
  • [9] Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models
    Gomez-Cravioto, Daniela A.
    Diaz-Ramos, Ramon E.
    Cantu-Ortiz, Francisco J.
    Ceballos, Hector G.
    [J]. COGNITIVE COMPUTATION, 2024, 16 (04) : 1794 - 1805
  • [10] Forecasting COVID-19 Cases in Algeria using Logistic Growth and Polynomial Regression Models
    Mohamed Lounis
    Malavika Babu
    [J]. Dr. Sulaiman Al Habib Medical Journal, 2021, 3 (3): : 83 - 87
12345