scholarly journals Indo-Pak Comparison Of COVID-19 By ARIMA Model: A Secondary Analysis

2021 ◽  
Author(s):  
Osama Ajaz ◽  
Muhammad Irfan ◽  
Muhammad Amjad ◽  
Ayesha Siddiqa
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Secondary Analysis ◽  
Health Spending ◽  
Climatic Conditions ◽  
World Health ◽  
Data Set ◽  
Related Case ◽  
Time Period ◽  
The World

Abstract Background: Historically, the world has faced and recovered from many pandemics. The most recent global pandemic facing the entire world is Covid-19. India and Pakistan can be considered to be in the same phases of development and health spending relative to their GDP and also have similar climatic conditions. The main aim of the study is to forecast cumulative cases and deaths in Pakistan and India, which will be helpful for policy makers to plan accordingly.Methods: The data set was obtained from the World Health Organization (WHO) website (https://covid19.who.int). The time period we have considered since the first corona related case and death were observed in both countries. The dataset for Pakistan covered the time period from 28th February 2020 to 28th February 2021 and for India 30th January to 28th February 2021. The Auto-Regressive integrated moving average (ARIMA) model was applied for forecasting using R-package.Results: Our results forecasted that cumulative COVID-19 cases at the end of June 2021, at the end of September 2021 and at the end of December 2021 will be 13065792, 14704450, and 16481122 respectively in India while for Pakistan, we forecasted that at the end of June 2021, at the end of September 2021 and at the end of December 2021 will be 746963.5 873557.3and 999766.5 respectively. Cumulative deaths were also forecasted for Pakistan and India. We predicted cumulative deaths as at the end of June 2021 at the end of June 2021, at the end of September 2021 and at the end of December 2021 will be 170586.5, 181153.4 and 192017.5 respectively in India while for Pakistan, we forecasted that cumulative deaths at the end of June 2021, at the end of September 2021 and at the end of December 2021 will be 17890.98, 21825.26 and 25849.4 respectively.Conclusion: Corona related cumulative cases and deaths are on the rise in both countries. The pandemic situation in India is worse than in Pakistan nevertheless both countries are at high risk. There is a sudden increasing pattern in the number of corona related cases in both countries.

scholarly journals Indo-Pak Comparison and Forecasting of Covid-19 Epidemic: A Secondary Data Analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 184-188
Author(s):  
Osama Ajaz ◽  
Muhammad Irfan ◽  
Ayesha Siddiqa ◽  
Muhammad Amjad
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Current Trend ◽  
Secondary Data ◽  
World Health ◽  
Data Set ◽  
Related Case ◽  
The World ◽  
Novel Coronavirus ◽  
Health Organization

Background: The world has historically faced and recovered from many pandemics. The most recent global pandemic that the whole world is facing is Novel Coronavirus – Covid-19. The objective of current study is to compare and forecast COVID-19 trends for Pakistan and India. Methods: The data set for this research is obtained from the World Health Organization (WHO) online repository (https://covid19.who.int/). The time period we have considered since the first corona related case and death were observed in both countries. This research paper analyzes corona related cases and deaths in Pakistan and India till 28th February 2021, a total of 578,797 cases in Pakistan and 11,096,731 cases in India has been confirmed including 128,37 and 1,570,51 deaths respectively. The Auto-Regressive Integrated Moving Average (ARIMA) model is used to forecast the variables cumulative cases and deaths. It is simple to use and more predictive than any other regression model. Results: Based on the current trend, the forecast graph reveals that the number of cumulative corona cases could reach 999,767 in Pakistan and 16,481,122 in India up to 31st December 2021. Conclusion: This research found that corona related cumulative cases and deaths are on the rise in both countries. The pandemic situation in India is worse than in Pakistan nevertheless both countries are at high risk. There is a sudden increasing pattern in the number of corona related cases in both countries. Both governments must impose effective policies to control this pandemic.

scholarly journals Time-Series Forecasting and Analysis of COVID-19 Outbreak in Highly Populated Countries

2022 ◽  
Vol 13 (2) ◽  
pp. 1-17
Author(s):  
Arunkumar P. M. ◽  
Lakshmana Kumar Ramasamy ◽  
Amala Jayanthi M.
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Research Area ◽  
Time Series Forecasting ◽  
Disease Spread ◽  
Time Period ◽  
The World ◽  
Auto Regressive ◽  
Important Research Area

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.

Time-series forecasting and analysis of COVID-19 outbreak in highly populated countries- A data-driven approach

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Research Area ◽  
Time Series Forecasting ◽  
Disease Spread ◽  
Time Period ◽  
The World ◽  
Data Driven Approach ◽  
Important Research Area

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.

scholarly journals Predicting the Pandemic COVID-19 Using ARIMA Model

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Nguyen Quoc Duong ◽  
Le Phuong Thao ◽  
Dinh Thi Nhu Quynh ◽  
Le Thanh Binh ◽  
Cao Thi Ai Loan ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
World Health ◽  
Percentage Error ◽  
The World ◽  
Validation Set ◽  
Short Term Forecasting ◽  
Health Organization ◽  
Global Threat

Coronavirus disease 2019 (COVID-19) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. The main objective of this study is to apply AutoRegressive Integrated Moving Average (ARIMA) model with the objective of monitoring and short-term forecasting the total confirmed new cases per day all over the world. The data are extracted from daily report of World Health Organization from 21st January 2020 to 16th March 2020. Akaike’s Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. To assess the validity of the proposed model, the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) between the observed and fitted of COVID-19 total confirmed new cases was calculated. Finally, we applied “forecast” package in R software and the fitted ARIMA model to predict the infections of COVID-19. We found that the ARIMA (1, 2, 1) model was able to describe and predict the epidemiological trend of the disease of COVID-19. The MAPE and RMSE for the training set and validation set respectively, which we found was reasonable for use in the forecast. Furthermore, the model also provided forecast total confirmed new cases for the following days. ARIMA model applied to COVID-19 confirmed cases data are an important tool for COVID-19 surveillance all over the world. This study shows that accurate forecasting of the COVID-19 trend is possible using an ARIMA model. Unless strict infection management and control are taken, our findings indicate the potential of COVID-19 to cause greater outbreak all over the world.

scholarly journals Implication of ARIMA Time Series Model on COVID-19 Outbreaks in India

2020 ◽  
Vol 4 (11) ◽  
pp. 41-45
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Model ◽  
Data Set ◽  
Time Period ◽  
Fitting Procedures ◽  
Auto Regressive ◽  
Mathematical Techniques

This research paper focuses on a Time Series Model to predict COVID-19 Outbreaks in India. COVID-19 Corona virus disease has been recognized as a worldwide hazard, and most of the studies are being conducted using diverse mathematical techniques to forecast the probable evolution of this outbreak. These mathematical models based on various factors and analyses are subject to potential bias. Here, we put forward a natural Times Series (TS) model that could be very useful to predict the spread of COVID-19. Here, a popular method Auto Regressive Integrated Moving Average (ARIMA) TS model is performed on the real COVID-19 data set to predict the outbreak trend of the prevalence and incidence of COVID-19 in India. Every day data of fresh COVID-19 confirmed cases act as an exogenous factor in this frame. Our data envelops the time period from 12th March, 2020 to 27th June, 2020. The time series under study is a non-stationary. Results obtained in the study revealed that the ARIMA model has a strong potential for prediction. In ACF and PACF graphs. Lag 1 and Lag 40 was found to be significant. Regressed values imply Lag 1 and Lag 40 was significant in predicting the present trend. The model predicted maximum COVID-19 cases in India at around 14, 22,337 with an interval (12, 80,352 - 15, 69, 817) during 1st July to 30th July period cumulatively. As per the model, the number of new cases shall increases drastically in India only. The results will help governments to make necessary arrangements as per the estimated cases. This kind of investigation, implications of ARIMA models and fitting procedures are useful in forecasting COVID-19 Outbreaks in India.

scholarly journals ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries

Equilibrium ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 181-204
Author(s):  
Tadeusz Kufel
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Essential Element ◽  
Value Added ◽  
World Health ◽  
Global Pandemic ◽  
The World ◽  
Auto Regressive ◽  
Health Organization ◽  
Virus Distribution

Research background: On 11 March 2020, the Covid-19 epidemic was identified by the World Health Organization (WHO) as a global pandemic. The rapid increase in the scale of the epidemic has led to the introduction of non-pharmaceutical countermeasures. Forecast of the Covid-19 prevalence is an essential element in the actions undertaken by authorities. Purpose of the article: The article aims to assess the usefulness of the Auto-regressive Integrated Moving Average (ARIMA) model for predicting the dynamics of Covid-19 incidence at different stages of the epidemic, from the first phase of growth, to the maximum daily incidence, until the phase of the epidemic's extinction. Methods: ARIMA(p,d,q) models are used to predict the dynamics of virus distribution in many diseases. Model estimates, forecasts, and the accuracy of forecasts are presented in this paper. Findings & Value added: Using the ARIMA(1,2,0) model for forecasting the dynamics of Covid-19 cases in each stage of the epidemic is a way of evaluating the implemented non-pharmaceutical countermeasures on the dynamics of the epidemic.

ARIMA model for forecasting COVID-19 Death cases in India (Preprint)

2020 ◽  
Author(s):  
Jeya Sutha M
Keyword(s):  
Arima Model ◽  
Public Health Emergency ◽  
World Health ◽  
The People ◽  
The World ◽  
The Status ◽  
The Government ◽  
Health Organization ◽  
Spread Of Infection ◽  
Death Cases

UNSTRUCTURED COVID-19, the disease caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a highly contagious disease. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of July 25, 2020; 15,947,292 laboratory-confirmed and 642,814 deaths have been reported globally. India has reported 1,338,928 confirmed cases and 31,412 deaths till date. This paper presents different aspects of COVID-19, visualization of the spread of infection and presents the ARIMA model for forecasting the status of COVID-19 death cases in the next 50 days in order to take necessary precaution by the Government to save the people.

scholarly journals A statistical study of COVID-19 pandemic in Egypt

2021 ◽  
Vol 54 (1) ◽  
pp. 233-244
Author(s):  
Taha Radwan
Keyword(s):  
Linear Relationship ◽  
Statistical Study ◽  
Moving Average ◽  
Arima Model ◽  
Decision Makers ◽  
Accurate Data ◽  
Autoregressive Integrated Moving Average ◽  
The World ◽  
The Future

Abstract The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA) model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

Comparativa de modelos multivariados basados en aprendizaje automático para la predicción del riesgo de diabetes en etapas tempranas

2021 ◽  
Author(s):  
◽  
Zayra Ramírez Gaytán
Keyword(s):  
World Health ◽  
Degenerative Diseases ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
The World ◽  
Long Time ◽  
Life Threatening ◽  
Health Organization ◽  
Logistic Regression Algorithm ◽  
Asymptomatic Phase

Diabetes is one of the fastest-growing, life-threatening, chronic degenerative diseases. According to the World Health Organization (WHO), it has affected 422 million people worldwide in 2018. Approximately 50% of all people who suffer diabetes are not diagnosed due to the asymptomatic phase which usually lasts a long time. In this work, a data set of 520 instances has been used. The data set has been analyzed with the next three algorithms: logistic regression algorithm, decision trees and random forest. The results show that the decision tree algorithm had better performance with an AUC of 98%. Also, it was found the most common symptoms that a person with a risk of diabetes presents are polyuria, polydipsia and sudden weight loss.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

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