Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia

Alboaneen, Dabiah; Pranggono, Bernardi; Alshammari, Dhahi; Alqahtani, Nourah; Alyaffer, Raja H.

doi:10.3390/ijerph17124568

Cited by 37 publications

(49 citation statements)

References 30 publications

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“…Beside the statistical methods as mentioned above, the mathematical modeling and simulation including Logistic Growth and Susceptible-Infected-Recovered (SIR-model) have been utilized to predict the new COVID-19 cases in China [ 10 ] and Saudi Arabia [ 11 ]. Moreover, Papastefanopoulos et al [ 12 ] have investigated and compared the accuracy of six time-series forecasting approaches, namely, ARIMA, Holt–Winters additive model (HWAAS), TBAT, Facebook’s Prophet, DeepAR, and N-Beats, in predicting the progression of COVID-19.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As for statistical methods, they have been applied to predict the COVID-19 cases in different countries like Italy [ 5 , 6 ], Iran [ 7 , 8 ], Spain and France [ 9 ]. Beside the statistical methods, the mathematical modeling and simulation have been utilized to predict the new COVID-19 cases in China [ 10 ] and Saudi Arabia [ 11 ]. Moreover, Papastefanopoulos et al [ 12 ] have compared the accuracy of six time-series forecasting approaches, namely, ARIMA, Holt–Winters additive model (HWAAS), TBAT, Facebook’s Prophet, DeepAR, and N-Beats, in predicting the progression of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Eltoukhy

Shaban

Chan

et al. 2020

IJERPH

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The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Eltoukhy

Shaban

Chan

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…Several researches have been published aiming to predict the number of infected cases, the number of deaths and more particularly a probable end date in different countries and sometimes in different cities (provinces) inside the same country. As examples, we can cite references [3] for Saudi Arabia, [4] for Kuwait and [5] for India. The reported results have shown varying degrees of accuracy and reliability due to several causes including the quality and quantity of available information about the virus [6] .…”

Section: Introductionmentioning

confidence: 99%

Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm

et al. 2020

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COVID-19 pandemic is spreading around the world becoming thus a serious concern for health, economic and social systems worldwide. In such situation, predicting as accurately as possible the future dynamics of the virus is a challenging problem for scientists and decision-makers. In this paper, four phenomenological epidemic models as well as Suspected-Infected-Recovered (SIR) model are investigated for predicting the cumulative number of infected cases in Saudi Arabia in addition to the probable end-date of the outbreak. The prediction problem is formulated as an optimization framework and solved using a Particle Swarm Optimization (PSO) algorithm. The Generalized Richards Model (GRM) has been found to be the best one in achieving two objectives: first, fitting the collected data (covering 223 days between March 2 nd and October 10, 2020) with the lowest mean absolute percentage error (MAPE = 3.2889%), the highest coefficient of determination (R 2 = 0.9953) and the lowest root mean squared error (RMSE = 8827); and second, predicting a probable end date found to be around the end of December 2020 with a projected number of 378,299 at the end of the outbreak. The obtained results may help the decision-makers to take suitable decisions related to the pandemic mitigation and containment and provide clear understanding of the virus dynamics in Saudi Arabia.

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“…Several mathematical models have been proposed to forecast the course of COVID-19 in countries around the world, with a few studies discussing the use of the logistic model in countries like China [5,6] and Saudi Arabia [7]. This type of mathematical model has roots in the classic susceptible-infectious-removed (SIR) epidemiological model [8].…”

Section: Introductionmentioning

confidence: 99%

Novel uses of three-parameter logistic models and first-derivative models for the Coronavirus Disease (COVID-19) epidemic in the United States, in three distinct scenarios

Samuel

2020

Preprint

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Background: Forecasting the current coronavirus disease (COVID-19) epidemic in the United States necessitates novel mathematical models for accurate predictions. This paper examines novel uses of three-parameter logistic models and first-derivative models through three distinct scenarios that have not been examined in the literature as of July 14, 2020.Methods: Using publicly available data, statistical software was used to conduct a non-linear least-squares estimate to generate a three-parameter logistic model, with a subsequently generated first-derivative model. In the first scenario a logistic model was used to examine the natural log of COVID-19 cases as the dependent variable (versus day number), on July 11 and May 1. Independent t-test analyses were used to test comparative coefficient differences across models. In the second scenario, a first-derivative model was derived from a base three-parameter logistic model for April 27, examining time to peak mortality and decrease in case fatality rate. In the third scenario, a first-derivative model of mortality through July 11 as the dependent variable, versus confirmed cases, was generated to look at case fatality rate relative to increasing cases.Results: All models generated were statistically significant with R2 > 99%. The logistic models in the first scenario best predicted time to growth deceleration in the natural log of cases in the U.S. (slowing of exponential growth), estimated at March 11, 2020. For the May 1 data, independent t-test analyses of comparative coefficients across models were useful to track improvements from implemented public health measures. The first-derivative model in the second scenario on April 27, when the epidemic was more controlled, showed peak mortality around April 12-13, with a case fatality rate of < 1,000 deaths and trending down. The first-derivative model in the third scenario estimated a near-zero case fatality rate to occur at 4 million confirmed cases. It has not been affected by fluctuations in mortality from June 29 through July 11.Conclusion: Three-parameter logistic models and first-derivative models have utility in predicting time to growth deceleration, and case fatality rates relative to cases. They can objectively assess improvements of implemented epidemiologic measures and have applicable public health safety implications.

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Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia

Cited by 37 publications

References 30 publications

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm

Novel uses of three-parameter logistic models and first-derivative models for the Coronavirus Disease (COVID-19) epidemic in the United States, in three distinct scenarios

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