Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics
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 followe...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-12-01
|
| Series: | Kuwait Journal of Science |
| Online Access: | https://journalskuwait.org/kjs/index.php/KJS/article/view/14501 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849727699760709632 |
|---|---|
| author | Kayode Oshinubi Fahima Al-Awadhi Mustapha Rachdi Jacques Demongeot |
| author_facet | Kayode Oshinubi Fahima Al-Awadhi Mustapha Rachdi Jacques Demongeot |
| author_sort | Kayode Oshinubi |
| collection | DOAJ |
| description |
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.
|
| format | Article |
| id | doaj-art-7a9f6ff2ae2a4e2ebacc0b977cfcff37 |
| institution | DOAJ |
| issn | 2307-4108 2307-4116 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kuwait Journal of Science |
| spelling | doaj-art-7a9f6ff2ae2a4e2ebacc0b977cfcff372025-08-20T03:09:47ZengElsevierKuwait Journal of Science2307-41082307-41162021-12-0149splcov10.48129/kjs.splcov.14501Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number DynamicsKayode Oshinubi0Fahima Al-Awadhi 1Mustapha Rachdi2Jacques Demongeot3Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical and Labcom CNRS/UGA/OrangeLabs Telecom4Health, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France; Kayode.Oshinubi@univ-grenoble-alpes.frFaculty of Science, Kuwait UniversityLaboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical and Labcom CNRS/UGA/OrangeLabs Telecom4Health, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France; Kayode.Oshinubi@univ-grenoble-alpes.frUniversity Grenoble Alpes 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. https://journalskuwait.org/kjs/index.php/KJS/article/view/14501 |
| spellingShingle | Kayode Oshinubi Fahima Al-Awadhi Mustapha Rachdi Jacques Demongeot Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics Kuwait Journal of Science |
| title | Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics |
| title_full | Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics |
| title_fullStr | Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics |
| title_full_unstemmed | Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics |
| title_short | Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics |
| title_sort | data analysis and forecasting of covid 19 pandemic in kuwait based on daily observation and basic reproduction number dynamics |
| url | https://journalskuwait.org/kjs/index.php/KJS/article/view/14501 |
| work_keys_str_mv | AT kayodeoshinubi dataanalysisandforecastingofcovid19pandemicinkuwaitbasedondailyobservationandbasicreproductionnumberdynamics AT fahimaalawadhi dataanalysisandforecastingofcovid19pandemicinkuwaitbasedondailyobservationandbasicreproductionnumberdynamics AT mustapharachdi dataanalysisandforecastingofcovid19pandemicinkuwaitbasedondailyobservationandbasicreproductionnumberdynamics AT jacquesdemongeot dataanalysisandforecastingofcovid19pandemicinkuwaitbasedondailyobservationandbasicreproductionnumberdynamics |