1
|
Zrieq R, Kamel S, Boubaker S, Algahtani FD, Alzain MA, Alshammari F, Alshammari FS, Aldhmadi BK, Atique S, Al-Najjar MAA, Villareal SC. Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia. Healthcare (Basel) 2022; 10:healthcare10101874. [PMID: 36292321 PMCID: PMC9601417 DOI: 10.3390/healthcare10101874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/21/2022] [Accepted: 09/21/2022] [Indexed: 11/24/2022] Open
Abstract
The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the spread of new COVID-19 cases is critical to develop a framework for universal pandemic preparedness as well as mitigating the disease’s spread. To this end, the main aim of this paper is first to analyze the historical data of the disease gathered from 2 March 2020 to 20 June 2022 and second to use the collected data for forecasting the trajectory of COVID-19 in order to construct robust and accurate models. To the best of our knowledge, this study is the first that analyzes the outbreak of COVID-19 in Saudi Arabia for a long period (more than two years). To achieve this study aim, two techniques from the data analytics field, namely the auto-regressive integrated moving average (ARIMA) statistical technique and Prophet Facebook machine learning technique were investigated for predicting daily new infections, recoveries and deaths. Based on forecasting performance metrics, both models were found to be accurate and robust in forecasting the time series of COVID-19 in Saudi Arabia for the considered period (the coefficient of determination for example was in all cases more than 0.96) with a small superiority of the ARIMA model in terms of the forecasting ability and of Prophet in terms of simplicity and a few hyper-parameters. The findings of this study have yielded a realistic picture of the disease direction and provide useful insights for decision makers so as to be prepared for the future evolution of the pandemic. In addition, the results of this study have shown positive healthcare implications of the Saudi experience in fighting the disease and the relative efficiency of the taken measures.
Collapse
Affiliation(s)
- Rafat Zrieq
- Department of Public Health, College of Public Health and Health Informatics, University of Ha’il, Ha’il 55476, Saudi Arabia
| | - Souad Kamel
- Department of Computer & Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
| | - Sahbi Boubaker
- Department of Computer & Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
- Correspondence:
| | - Fahad D. Algahtani
- Department of Public Health, College of Public Health and Health Informatics, University of Ha’il, Ha’il 55476, Saudi Arabia
| | - Mohamed Ali Alzain
- Department of Public Health, College of Public Health and Health Informatics, University of Ha’il, Ha’il 55476, Saudi Arabia
| | - Fares Alshammari
- Department of Health Informatics, College of Public Health and Health Informatics, University of Ha’il, Ha’il 55476, Saudi Arabia
| | - Fahad Saud Alshammari
- Department of Health Informatics, College of Public Health and Health Informatics, University of Ha’il, Ha’il 55476, Saudi Arabia
| | - Badr Khalaf Aldhmadi
- Department of Health Management, College of Public Health and Health Informatics, University of Ha’il, Ha’il 55476, Saudi Arabia
| | - Suleman Atique
- Department of Health Informatics, College of Public Health and Health Informatics, University of Ha’il, Ha’il 55476, Saudi Arabia
- Department of Public Health Science, Faculty of Landscape and Society, Norwegian University of Life Sciences,1430 Ås, Norway
| | - Mohammad A. A. Al-Najjar
- Department of Pharmaceutical Science and Pharmaceutics, Faculty of Pharmacy, Applied Science Provate University, Al Arab St 21, Amman 11118, Jordan
| | - Sandro C. Villareal
- Medical-Surgical and Pediatric Nursing Department, College of Nursing, University of Ha’il, Ha’il 55476, Saudi Arabia
| |
Collapse
|
2
|
Chakraborty A, Das D, Mitra S, De D, Pal AJ. Forecasting adversities of COVID-19 waves in India using intelligent computing. INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2022:1-17. [PMID: 36186271 PMCID: PMC9512957 DOI: 10.1007/s11334-022-00486-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days' intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.
Collapse
Affiliation(s)
- Arijit Chakraborty
- Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India
| | - Dipankar Das
- Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India
| | - Sajal Mitra
- Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India
| | - Debashis De
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India
| | | |
Collapse
|
3
|
Perone G. Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy. THE EUROPEAN JOURNAL OF HEALTH ECONOMICS : HEPAC : HEALTH ECONOMICS IN PREVENTION AND CARE 2022; 23:917-940. [PMID: 34347175 PMCID: PMC8332000 DOI: 10.1007/s10198-021-01347-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 07/01/2021] [Indexed: 05/13/2023]
Abstract
The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic's second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box-Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020-October 13, 2020 were extracted from the website of the Italian Ministry of Health ( www.salute.gov.it ). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities' decision-making, especially in the short-term.
Collapse
Affiliation(s)
- Gaetano Perone
- Department of Management, Economics and Quantitative Methods, University of Bergamo, via dei Caniana 2, 24127, Bergamo, Italy.
| |
Collapse
|
4
|
SARIMA Model Forecasting Performance of the COVID-19 Daily Statistics in Thailand during the Omicron Variant Epidemic. Healthcare (Basel) 2022; 10:healthcare10071310. [PMID: 35885836 PMCID: PMC9324558 DOI: 10.3390/healthcare10071310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 11/17/2022] Open
Abstract
This study aims to identify and evaluate a robust and replicable public health predictive model that can be applied to the COVID-19 time-series dataset, and to compare the model performance after performing the 7-day, 14-day, and 28-day forecast interval. The seasonal autoregressive integrated moving average (SARIMA) model was developed and validated using a Thailand COVID-19 open dataset from 1 December 2021 to 30 April 2022, during the Omicron variant outbreak. The SARIMA model with a non-statistically significant p-value of the Ljung–Box test, the lowest AIC, and the lowest RMSE was selected from the top five candidates for model validation. The selected models were validated using the 7-day, 14-day, and 28-day forward-chaining cross validation method. The model performance matrix for each forecast interval was evaluated and compared. The case fatality rate and mortality rate of the COVID-19 Omicron variant were estimated from the best performance model. The study points out the importance of different time interval forecasting that affects the model performance.
Collapse
|
5
|
Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031504. [PMID: 35162523 PMCID: PMC8835281 DOI: 10.3390/ijerph19031504] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 12/09/2021] [Accepted: 12/14/2021] [Indexed: 12/29/2022]
Abstract
With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia’s official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.
Collapse
|
6
|
Abraham J, Turville C, Dowling K, Florentine S. Does Climate Play Any Role in COVID-19 Spreading?-An Australian Perspective. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18179086. [PMID: 34501673 PMCID: PMC8431748 DOI: 10.3390/ijerph18179086] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/23/2021] [Accepted: 08/25/2021] [Indexed: 12/21/2022]
Abstract
Compared to other countries, the COVID-19 pandemic did not severely affect Australia as measured by total deaths until mid-2021. Though a substantial number of daily confirmed cases (up to 698) were reported during the second wave, most of them were from the southern state of Victoria. This study examined the possible correlations between climate variables and the number of daily confirmed COVID-19 cases in Victoria, Australia, from 25 January to 31 October 2020. Appropriate regression models and cross-correlation diagnostics were used to examine the effect of temperature, rainfall, solar exposure, and ultraviolet index (UVI) with the number of daily confirmed cases. Significant positive associations were identified for solar exposure and maximum and average UVI for confirmed cases one and 19 days later. Negative associations for these variables were found for confirmed cases five days later. Minimum temperature had a significant negative correlation one day later and a positive effect 21 days later. No significant correlation was found for maximum temperature and rainfall. The most significant relationships were found for confirmed cases 19 days after changes in the meteorological variables. A 1% increase in solar exposure, maximum UVI, and average UVI was associated with a 0.31% (95% CI: 0.13 to 0.51), 0.71% (95% CI: 0.43 to 0.98), and 0.63% (95%CI: 0.20 to 1.61) increase 19 days later in the number of confirmed cases, respectively. The implications of these results can be used in the public health management of any possible future events in Australia. It also highlights the significance of considering the climatic variables and seasonality in all kinds of epidemics and pandemics.
Collapse
Affiliation(s)
- Joji Abraham
- School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Mt Helen Campus, Ballarat, VIC 3353, Australia; (C.T.); (K.D.)
- Correspondence: ; Tel.: +61-412-751-134
| | - Christopher Turville
- School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Mt Helen Campus, Ballarat, VIC 3353, Australia; (C.T.); (K.D.)
| | - Kim Dowling
- School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Mt Helen Campus, Ballarat, VIC 3353, Australia; (C.T.); (K.D.)
- Department of Geology, University of Johannesburg, Johannesburg 2006, South Africa
| | - Singarayer Florentine
- Future Regions Research Centre, School of Science, Psychology and Sport, Federation University Australia, Mt Helen Campus, Ballarat, VIC 3353, Australia;
| |
Collapse
|
7
|
Shaharudin SM, Ismail S, Hassan NA, Tan ML, Sulaiman NAF. Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model. Front Public Health 2021; 9:604093. [PMID: 34195166 PMCID: PMC8236644 DOI: 10.3389/fpubh.2021.604093] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 04/28/2021] [Indexed: 12/23/2022] Open
Abstract
Novel coronavirus (COVID-19) was discovered in Wuhan, China in December 2019, and has affected millions of lives worldwide. On 29th April 2020, Malaysia reported more than 5,000 COVID-19 cases; the second highest in the Southeast Asian region after Singapore. Recently, a forecasting model was developed to measure and predict COVID-19 cases in Malaysia on daily basis for the next 10 days using previously-confirmed cases. A Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) is proposed by establishing L and ET parameters via several tests. The advantage of using this forecasting model is it would discriminate noise in a time series trend and produce significant forecasting results. The RF-SSA model assessment was based on the official COVID-19 data released by the World Health Organization (WHO) to predict daily confirmed cases between 30th April and 31st May, 2020. These results revealed that parameter L = 5 (T/20) for the RF-SSA model was indeed suitable for short-time series outbreak data, while the appropriate number of eigentriples was integral as it influenced the forecasting results. Evidently, the RF-SSA had over-forecasted the cases by 0.36%. This signifies the competence of RF-SSA in predicting the impending number of COVID-19 cases. Nonetheless, an enhanced RF-SSA algorithm should be developed for higher effectivity of capturing any extreme data changes.
Collapse
Affiliation(s)
- Shazlyn Milleana Shaharudin
- Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia
| | - Shuhaida Ismail
- Data Analytics, Sciences & Modelling (DASM), Department of Mathematics & Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia
| | - Noor Artika Hassan
- Department of Community Medicine, Kulliyyah of Medicine, International Islamic University Malaysia, Kuantan, Malaysia
| | - Mou Leong Tan
- Geoinformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Gelugor, Malaysia
| | - Nurul Ainina Filza Sulaiman
- Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia
| |
Collapse
|
8
|
Lee H, Noh E, Jeon H, Nam EW. Association between traffic inflow and COVID-19 prevalence at the provincial level in South Korea. Int J Infect Dis 2021; 108:435-442. [PMID: 34044141 PMCID: PMC8142818 DOI: 10.1016/j.ijid.2021.05.054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/12/2021] [Accepted: 05/21/2021] [Indexed: 11/23/2022] Open
Abstract
Objectives To analyze the relationship between traffic inflow and COVID-19 prevalence in South Korea for formulating prevention policies for novel infections. Methods We evaluated traffic inflow and new COVID-19 cases in 8 regions of Korea from January 1, 2020, to January 31, 2021. The toll collection system (TCS) traffic volume for 2019–2020 and traffic inflow trends were analyzed using independent samples t-test and nonlinear regression, respectively. The association between TCS traffic volume and new COVID-19 cases by city was analyzed using correlation analysis. Results Traffic inflow volume in 2020 decreased 3.7% from 2019. The TCS traffic inflow trend in the 8 provinces decreased during the first COVID-19 wave, gradually increased until the second wave, decreased after the second wave, and showed a sharp decrease in the third wave. There was a positive correlation between the traffic inflow volume and new cases in Busan-Gyeongnam and Jeonbuk, but not in Daegu-Gyeongbuk or Gangwon. Conclusions A decrease in new COVID-19 cases in the regions was associated with increased traffic inflow volume. Therefore, the Korean government can establish preventive social distancing policies by identifying increases or decreases in traffic volume. These policies will also need to consider the distribution of vaccines in each area.
Collapse
Affiliation(s)
- Hocheol Lee
- Department of Health Administration, Yonsei University Graduate School, Wonju, Gangwon-do, Republic of Korea; Yonsei Global Health Center, Yonsei University, Wonju, Republic of Korea
| | - Eunbi Noh
- Department of Health Administration, Yonsei University Graduate School, Wonju, Gangwon-do, Republic of Korea; Yonsei Global Health Center, Yonsei University, Wonju, Republic of Korea
| | - Huiwon Jeon
- Department of Health Administration, Yonsei University Graduate School, Wonju, Gangwon-do, Republic of Korea
| | - Eun Woo Nam
- Yonsei Global Health Center, Yonsei University, Wonju, Republic of Korea; Department of Health Administration, College of Health Science, Yonsei University, Wonju, Gangwon-do, Republic of Korea.
| |
Collapse
|