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 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.

scholarly journals Modelling New Zealand Road Deaths

2021 ◽  
Vol 32 (2) ◽  
pp. 4-15
Author(s):  
Colin Morrison ◽  
Ernest Albuquerque
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Time Data ◽  
Baseline Model ◽  
Explanatory Variables ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag

New Zealand is developing an integrated road safety intervention logic model. This paper describes a core component of this wider strategic research carried out in 2018: a baseline model that extrapolates New Zealand road deaths to 2025. The baseline will provide context to what Waka Kotahi NZ Transport Agency is trying to achieve. It offers a way of understanding what impact interventions have in acting with and against external influences affecting road deaths and serious trauma. The baseline model considers autonomous change at a macro level given social and economic factors that influence road deaths. Identifying and testing relationships and modelling these explanatory variables clarifies the effect of interventions. Time-series forecasting begins by carefully collecting and rigorously analysing sequences of discrete-time data, then developing an appropriate model to describe the inherent structure of the series. Successful time-series forecasting depends on fitting an appropriate model to the underlying time-series. Several time-series models were investigated in understanding road deaths in the New Zealand context. In the final modelling an autoregressive integrated moving average (ARIMA) model and two differing autoregressive distributed lag (ARDL) models were developed. A preferred model was identified. This ARDL model was used to project road deaths to 2025.

scholarly journals Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Debasis Mithiya ◽  
Lakshmikanta Datta ◽  
Kumarjit Mandal
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Group Method ◽  
Data Handling ◽  
Agricultural Economy ◽  
Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

scholarly journals Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

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.

COVID-19 ARIMA Model Forecast: Confirmed, Mortality & Recovered Cases in 13 Caribbean Countries (Preprint)

2020 ◽  
Author(s):  
Debjyoti Talukdar ◽  
Dr. Vrijesh Tripathi
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Local Health ◽  
Health Administration ◽  
Model Forecast ◽  
Time Period ◽  
The Impact

BACKGROUND Rapid spread of SARS nCoV-2 virus in Caribbean region has prompted heightened surveillance with more than 350,000 COVID-19 confirmed cases in 13 Caribbean countries namely Antigua and Barbados, Bahamas, Barbados, Cuba, Dominica, Dominican Republic, Grenada, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago. OBJECTIVE The aim of our study is to analyze the impact of coronavirus (SARS nCoV-2) in 13 Caribbean countries in terms of confirmed cases, number of deaths and recovered cases. Current and projected forecasts using advanced autoregressive integrated moving average (ARIMA) models will enable local health organisations to plan future courses of action in terms of lockdown and managing essential public services. METHODS The study uses the auto regressive integrated moving average (ARIMA) model based upon time series pattern as per data retrieved from John Hopkins University, freely accessible on public domain and used for research and academic purposes. The data was analyzed using STATA 14 SE software between the time period - Jan 22, 2020 till May 27, 2020 using ARIMA time series analysis. It involves generalizing an autoregressive moving average model to better understand the data and predict future points in the time series until June 15, 2020. RESULTS The results show the predicted trend in terms of COVID-19 confirmed, mortality and recovered cases for 13 Caribbean countries. The projected ARIMA model forecast for the time period - May 25, 2020 to May 31, 2020 show 20278 (95% CI 19433.21 - 21123.08) confirmed cases, 631 (95% CI 615.90 - 646.51) deaths and 11501 (95% CI 10912.45 - 12089) recovered cases related to SARS nCoV-2 virus. The final ARIMA model chosen for confirmed COVID-19 cases, number of deaths and recovered cases are ARIMA (4,2,2), ARIMA (2,1,2) and ARIMA (4,1,2) respectively. All chosen models were compared with other models in terms of various factors like AIC/BIC (Akaike Information Criterion/Bayesian Information Criterion), log likelihood, p-value significance, coefficient < 1 and 5% significance. The autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs were plotted to reduce bias and select the best fitting model. CONCLUSIONS As per the results of the forecasted COVID-19 models, there is a steady rise in terms of confirmed, recovered and mortality cases during the time period March 1, 2020 until May 27, 2020. It shows an increasing trend for confirmed and recovered COVID-19 cases and slowing of the number of mortality cases over a period of time. The predicted model will help the local health administration to devise public policies in terms of awareness measures, lockdown and essential health services accordingly.

scholarly journals A hybrid of artificial fneural network, exponential smoothing, and ARIMA models for COVID-19 time series forecasting

2021 ◽  
Vol 16 (1) ◽  
pp. 25-35
Author(s):  
Samir K. Safi ◽  
Olajide Idris Sanusi
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Time Series Forecasting ◽  
Arima Models ◽  
Artificial Neural Network Ann ◽  
Novel Coronavirus ◽  
Two Stages ◽  
Second Wave

The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.

scholarly journals Impact of COVID-19 Pandemic on Population-Level Interest in Skincare: Evidence from a Google Trends

2021 ◽  
pp. 147-160
Keyword(s):  
Time Series ◽  
Public Interest ◽  
Moving Average ◽  
Arima Model ◽  
Population Level ◽  
Interrupted Time Series ◽  
Limited Information ◽  
Interrupted Time Series Analysis ◽  
Search Volume ◽  
The World

COVID-19 pandemic created an unprecedented disruption of daily life including the pattern of skincare in healthcare settings by issuing stay-at-home orders around the world. There has been limited information about trends of skincare-related public interest during CVOID-19 and whether any substantial disruption in population-level behavior. The objective of this study is to evaluate the change in skincare-related population interest around the world during the COVID-19 pandemic time. Weekly RSV data were extracted worldwide and in 25 counties between August 1, 2016, and August 31, 2020. Interrupted time-series analysis was conducted as the quasi-experimental approach to evaluate the longitudinal effects of COVID-19 skincare-related search queries. For each country, autoregressive integrated moving average (ARIMA) model relative search volume (RSV) time series and then testing multiple periods simultaneously to examine the magnitude of the interruption. Multivariate linear regression was used to estimate the correlation between the relative changes in RSV with COVID-19 confirmed cases/ per million population and lockdown measures. Of 25 included countries in our study, 17 showed significantly increased (p<0.01) RSVs during the lockdown period compared with ARIMA forecasted data. The highest percentage of increments occurs in May and June in most countries. There was also a significant correlation between lockdown measures and the number of COVID-19 cases with relative changes in population interests for skincare. Understanding the trend and changes in skincare public interest during COVID-19 may assist health authorities to promote accessible educational information and preventive initiatives regarding skin problems.

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.

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