Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid‐19 Pandemic

This study presents forecasting methods using time series analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an artificial intelligence method and different statistical methodologies that can help predict the future of Covid‐19 cases. Various forecasting methods in time series analysis such as ARIMA, Holt's trend, naive, simple exponential smoothing, TBATS, and MAPE are extended for the study. It also involved the case fatality rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid‐19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.Alternate :Este estudio presenta métodos de pronóstico que utilizan el análisis de series temporales para los casos confirmados, el número de muertes y casos recuperados, y el estado de vacunación individual en diferentes estados de la India. Su objetivo es pronosticar los casos confirmados y la tasa de mortalidad y desarrollar un método de inteligencia artificial y diferentes metodologías estadísticas que puedan ayudar a predecir el futuro de los casos de Covid‐19. Para el estudio se adaptaron varios métodos de pronóstico para el análisis de series temporales como ARIMA, la tendencia de Holt, el ingenuo, el suavizado exponencial simple, TBATS y MAPE. También se incluyó la tasa de fatalidades para el número de muertes y casos confirmados para los respectivos estados de la India. Este estudio incluye los valores de pronóstico para el número de casos positivos, los pacientes curados, la tasa de mortalidad y la tasa de fatalidades para los casos de Covid‐19. Entre todos los métodos de pronóstico utilizados en este estudio, el método ingenuo y el de suavización exponencial simple muestran un mayor número de casos positivos y de pacientes curados.Alternate :抄録本研究は、インドの州における確定症例、死亡数及び回復例、および個人のワクチン接種状況に関する時系列分析を用いた予測方法を提示する。確定症例と死亡率を予測し、人工知能を用いた方法とCOVID‐19の症例の将来を予測するのに役立ついくつかの統計学的方法論を開発することを目指す。ARIMA、Holtのトレンド、単純法、単純指数平滑化法、TBATS、MAPEなどの時系列解析における各種予測法を拡張した。また、インドの各州の死亡者数と確定症例数の致死率も含んだ。本研究は、COVID‐19症例に対する、陽性症例数、治癒患者数、死亡率、および致死率に対する予測値を含む。この研究に含まれるすべての予測法の中で、単純法と単純指数平滑法は、陽性者数と治癒患者数の増加を予測した。

Analyzing and Predicting the Deaths and Cases of Covid-19 in Different Countries Using Fuzzy K Means Algorithm and Exponential Method

Covid-19 disease, since it first appearance in the Chinese city of Wuhan, has led to many infections and deaths, not only in China, but also in most countries of the world. The most prominent symptoms of this disease are headache, fever, strong cough, and perhaps the strongest of it is difficulty breathing in the event that the virus reaches the lung, which leads to death in many cases if the patient's condition is late, or he does not have strong immunity. The purpose of this study is to use Fuzzy k Means (FKM) and predictive algorithm representing in Simple Exponential Smoothing Method (SESM) to evaluate confirmed cases and deaths in different countries. This study's findings show that the FKM approach can evaluate data and produce reliable results, in addition to the SESM can give good prediction. According to this study, machine learning technologies and predicting methodologies achieved good results when used together. © 2021 IEEE.

Time series analysis using different forecast methods and case fatality rate for COVID-19 pandemic

This study presented forecasting methods using Time Series Analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an Artificial Intelligence method and different statistical methodologies that can help predict the future of Covid-19 cases. Various Forecasting methods in Time Series Analysis like ARIMA, Holt?s Trend, Naive, Simple Exponential Smoothing, TBATS, and MAPE are extended for the study. It also involved the Case Fatality Rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid-19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.

Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia.

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic's path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.