Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics

2021 ◽  
Author(s):  
Kayode Oshinubi ◽  
◽  
Fahimah Al-Awadhi ◽  
Mustapha Rachdi ◽  
Jacques Demongeot ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Moving Average ◽  
Arima Model ◽  
Exponential Model ◽  
Spreading Rate ◽  
Support Vector ◽  
Global Threat ◽  
Log Linear ◽  
Second Wave

Coronavirus (COVID-19) has continued to be a global threat to public health. When the coronavirus pandemic began early in 2020, experts wondered if there would be waves of cases, a pattern seen in other virus pandemics. The overall pattern so far has been one of increasing cases of COVID-19 followed by a decline, and we observed a second wave of increased cases and yet we are still exploring this pandemic. Hence, updating the prediction model for the new cases of COVID-19 for different waves is essential to monitor the spreading of the virus and control the disease. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modelling approach for predicting new cases of coronavirus (COVID-19). We propose a deterministic method to predict the basic reproduction number Ro of first and second wave transition of COVID-19 cases in Kuwait and also to forecast the daily new cases and deaths of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holt’s method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait. The results show that support vector regression has achieved the best performance for prediction while a simple exponential model without trend gives good optimal results for forecasting of Kuwait COVID-19 data.

scholarly journals Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait

2021 ◽  
Author(s):  
Kayode Oshinubi ◽  
Fahimah Al-Awadhi ◽  
Mustapha Rachdi ◽  
Jacques Demongeot
Keyword(s):  
Reproduction Number ◽  
Arima Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Deterministic Method ◽  
Linear Polynomial ◽  
Increasing Trend ◽  
Log Linear ◽  
Second Wave ◽  
Health Experts

The first COVID 19 case of Kuwait was announced on 24th February, 2020 and the daily new cases increases exponentially since then until May, 2020 when the first wave started to decline. The same exponential dynamics has been observed between January and March, 2021. The forecast of new cases and death recorded daily is crucial so that health experts and citizens can be guided in order to avoid escalation of the pandemic. We propose a deterministic method to predict the basic reproduction number Ro of first and second wave of COVID-19 cases in Kuwait and also to forecast the daily new cases and death of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holts method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Vol 1 (1) ◽  
pp. 52-65
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals Modelling monthly influenza cases in Malaysia

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254137
Author(s):  
Muhammad Adam Norrulashikin ◽  
Fadhilah Yusof ◽  
Nur Hanani Mohd Hanafiah ◽  
Siti Mariam Norrulashikin
Keyword(s):  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
World Health ◽  
Healthcare Sector ◽  
Support Vector ◽  
Test Dataset ◽  
Autoregressive Integrated Moving Average ◽  
Health Organization ◽  
Nonlinear Components

The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model.

scholarly journals Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

Gold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

scholarly journals Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

2020 ◽  
Vol 59 (7) ◽  
pp. 1239-1259
Author(s):  
Dehe Xu ◽  
Qi Zhang ◽  
Yan Ding ◽  
Huiping Huang
Keyword(s):  
Time Scale ◽  
Interpolation Method ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Kriging Interpolation ◽  
Support Vector ◽  
Kriging Interpolation Method

AbstractDrought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA–SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash–Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA–SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA–SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1–2 months ahead show reasonably good agreement with the actual data.

Hybrid Prediction Method of Flight Accident Based on ARIMA and SVM

2013 ◽  
Vol 339 ◽  
pp. 756-761
Author(s):  
Jing Shun Duanmu ◽  
Xu Sheng Gan ◽  
Jian Guo Gao
Keyword(s):  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Prediction Method ◽  
Support Vector ◽  
Good Prediction ◽  
Hybrid Prediction ◽  
Accident Prediction ◽  
Svm Model ◽  
Auto Regressive

The main target of flight accident prediction is to prevent the accident. To improve the pertinence and efficiency, the prediction must be strengthened to raise initiative of accident prevention. A hybrid prediction method, based on Auto-Regressive Integrating Moving Average (ARIMA) and Support Vector Machine (SVM), is proposed. First, ARIMA model is established to describe the linear relation in historical data, and then SVM model is built with regard to the residual error of ARIMA model in order to simulate the nonlinear law in historical data. The sum of prediction value of two models is to final hybrid prediction result. An application example show that the model based on this hybrid method has a good prediction to flight accident, and also is better than ARIMA or SVM model.

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 Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression

2021 ◽  
Vol 3 (1) ◽  
pp. 52-65
Author(s):  
Drajat Indra Purnama
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Testing Data ◽  
Linearity Test

ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

2021 ◽  
Vol 8 (1) ◽  
pp. 1111-1126
Author(s):  
Aba Diop ◽  
Abdourahmane Ndao ◽  
Cheikh Tidiane Seck ◽  
Ibrahima Faye
Keyword(s):  
Prediction Model ◽  
Moving Average ◽  
Arima Model ◽  
Short Term ◽  
Ministry Of Health ◽  
Term Prediction ◽  
Daily Data ◽  
Auto Regressive ◽  
Short Term Prediction ◽  
Second Wave

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

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