Global Forecasting Confirmed and Fatal Cases of COVID-19 Outbreak Using Autoregressive Integrated Moving Average Model

Dansana, Debabrata; Kumar, Raghvendra; Adhikari, Janmejoy Das; Mohapatra, Mans; Sharma, Rohit; Priyadarshini, Ishaani; Le, Dac‐Nhuong

doi:10.3389/fpubh.2020.580327

Cited by 52 publications

(21 citation statements)

References 26 publications

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“…Nemes and Kiss [ 21 ] performed a sentiment analysis of social media based on COVID 19 (comments, hashtags, posts, tweets). While COVID-19 outbreak had an effect over the world [ 22 , 23 ], the study considered recurrent neural networks (RNN) for the analysis. The study concluded that there are more positive tweets over social media.…”

Section: Methodsmentioning

confidence: 99%

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis

2021

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Section: Methodsmentioning

confidence: 99%

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis

2021

Self Cite

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“…If we combine autoregressive and moving average models, we will get a non-seasonal ARIMA model ( 35 ). In the autoregressive model, the linear combination of past values of variables is used to predict interesting variables in the future.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

Wang

et al. 2021

Front. Public Health

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Objective: Given the ever-changing flow of obstetric patients in the hospital, how the government and hospital management plan and allocate medical resources has become an important problem that needs to be urgently solved. In this study a prediction method for calculating the monthly and daily flow of patients based on time series is proposed to provide decision support for government and hospital management.Methods: The historical patient flow data from the Department of Obstetrics and Gynecology of the First Hospital of Jilin University, China, from January 1, 2018, to February 29, 2020, were used as the training set. Seven models such as XGBoost, SVM, RF, and NNAR were used to predict the daily patient flow in the next 14 days. The HoltWinters model is then used to predict the monthly flow of patients over the next year.Results: The results of this analysis and prediction model showed that the obstetric inpatient flow was not a purely random process, and that patient flow was not only accompanied by the random patient flow but also showed a trend change and seasonal change rule. ACF,PACF,Ljung_box, and residual histogram were then used to verify the accuracy of the prediction model, and the results show that the Holtwiners model was optimal. R2, MAPE, and other indicators were used to measure the accuracy of the 14 day prediction model, and the results showed that HoltWinters and STL prediction models achieved high accuracy.Conclusion: In this paper, the time series model was used to analyze the trend and seasonal changes of obstetric patient flow and predict the patient flow in the next 14 days and 12 months. On this basis, combined with the trend and seasonal changes of obstetric patient flow, a more reasonable and fair horizontal allocation scheme of medical resources is proposed, combined with the prediction of patient flow.

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“…Furthermore, Singh et al [56] used a moving area model to predict the COVID-19 pandemic for the top 15 affected nations, where cumulative cases, deaths, and recoveries from COVID-19 were compared among the United States, the United Kingdom, Turkey, China, Russia, Switzerland, Germany, Iran, Brazil, the Netherlands, Italy, France, Germany, and Spain. On the other hand, Dansana et al [57] explored the fatal cases of the pandemic outbreak using a moving average (MA) model and forecasted the cases of COVID-19 in the future by successfully calculating the total confirmed cases and fatalities over the studied dates. As reported by Pal et al [58], the MA model provides a sparing description of a stationary stochastic process in terms of a polynomial.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of the COVID-19 on Economic Sustainability—A Case Study of Fluctuation in Stock Prices for China and South Korea

Jiang

Park

2021

Sustainability

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The coronavirus disease (COVID-19) pandemic has had a global impact on lives, livelihoods, and economies. This study investigates whether a contagious infectious disease can affect the prices of the Chinese and Korean stock markets. Specifically, we aim to discover discrepancies in the impact of COVID-19 on the stock prices of China and South Korea through panel data. To test these discrepancies, we first regressed the stock indices on confirmed cases and deaths. We then validated the stability of coefficients over the past days. The empirical results show that (1) responses of stock indices are stable and impulsive and (2) response patterns toward COVID-19 events considerably vary across nations, especially in the counties such as China and South Korea.

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Global Forecasting Confirmed and Fatal Cases of COVID-19 Outbreak Using Autoregressive Integrated Moving Average Model

Cited by 52 publications

References 26 publications

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

The Impact of the COVID-19 on Economic Sustainability—A Case Study of Fluctuation in Stock Prices for China and South Korea

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