Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

Awwad, Fuad A.; Mohamoud, Moataz A.; Abonazel, Mohamed R.

doi:10.1371/journal.pone.0250149

Cited by 32 publications

(27 citation statements)

References 19 publications

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“…The spread of SARS-CoV-2 infection is detrimental, given its association with several parameters, including age and other underlying conditions [ 5 , 6 ]. A previous study found that patients with underlying health conditions carry a significantly higher risk of infection than healthy individuals [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Epidemiological Features of COVID-19 in Makkah City: A Retrospective Data Analysis

Asghar

Bamaga

Khogeer

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Coronavirus disease 2019 (COVID-19) is continuing to circulate and change, affecting billions of people worldwide and leading to increased mortality rates, especially in the Holy City of Makkah, Kingdom of Saudi Arabia (KSA). This study was aimed at investigating the epidemiological features of COVID-19 in Makkah City, KSA. Methods. A retrospective analysis was conducted to investigate the prevalence of COVID-19 and the association between the severity and mortality of COVID-19 with demographic factors and comorbidities. Results. Among 4,742 COVID-19 patients, the incidence rate observed in males was 66.7%, and 69.7% were from Al-Noor Specialized Hospital. The highest incidence rate (25.2%) was found in the age group > 60 years old , followed by the age group 51-60 years (21.8%). Furthermore, the highest frequency was observed in patients from Saudi Arabia (36.8%), followed by patients from Myanmar (14.7%) and Bangladesh (9.4%). The overall frequency of COVID-19 severity and death was 20.3% and 11.6%, respectively. Body mass index analysis showed that 1% of the patients were underweight, while 9.2% were overweight and 4.4% were obese. In addition, 9.6% had diabetes, 6.9% had hypertension, 1.1% had heart disease, and 2.2% had other chronic diseases. Conclusion. The study concluded that the overall percentages of severe COVID-19 (intensive care unit) cases and deaths in Makkah City, KSA, were 20.3% and 11.6%, respectively, and there was a higher incidence in male patients. The severity of infection was shown to have a strong significant correlation with different chronic diseases, nationality, body mass index, death rate (mortality), heart disease, and length of hospital stay ( P < 0.05 ). Despite these findings, more studies are needed to explain the underlying mechanisms that influence the overall health status of patients with specific characteristics and comorbidities.

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Section: Introductionmentioning

confidence: 99%

Epidemiological Features of COVID-19 in Makkah City: A Retrospective Data Analysis

Asghar

Bamaga

Khogeer

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Awwad et al [84] used the statistical STARMA model to predict confirmed and death cases in Saudi Arabian cities. This study covered most Saudi Arabian cities in the time interval from 23 March 2020 to 28 May 2020.…”

Section: Discussionmentioning

confidence: 99%

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Aldhyani

Alkahtani

2021

Life

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Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries.

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“…Based on the parametric estimates of saturated Engel curves of non-food expenditures categories, we found that the incidence of housing poverty is lower, while poverty incidence in the health and education expenditure categories is the highest, clarifying the importance of designing policies for unsatisfied needs that will ultimately contribute to improving households' living standards and reducing overall poverty rates in general. In future work, we plan to study the impact of the COVID-19 pandemic on non-food consumption in Egypt [42].…”

Section: Discussionmentioning

confidence: 99%

Estimating equivalence scales and non-food needs in Egypt: Parametric and semiparametric regression modeling

2021

Self Cite

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This paper investigated the appropriate specifications of Engel curves for non-food expenditure categories and estimated the deprivation indices of non-food needs in rural areas using a semi parametric examination of the presence of saturation points. The study used the extended partial linear model (EPLM) and adopted two estimation methods—the double residual estimator and differencing estimator—to obtain flexible shapes across different expenditure categories and estimate equivalence scales. We drew on data of the Egyptian Household Income, Expenditure, and Consumption Survey (HIEC). Our paper provides empirical evidence that the rankings of most non-food expenditure categories is of rank three at most. Rural households showed high economies of scale in non-food consumption, with child’s needs accounting for only 10% of adult’s non-food needs. Based on semi-parametrically estimated consumption behavior, the tendency of non-food expenditure categories to saturate did not emerge. While based on parametrically estimated consumption behavior, rural areas exhibited higher deprivation indices in terms of health and education expenditure categories, which indicates the need to design specific programs economically targeting such vulnerable households.

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Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

Cited by 32 publications

References 19 publications

Epidemiological Features of COVID-19 in Makkah City: A Retrospective Data Analysis

Epidemiological Features of COVID-19 in Makkah City: A Retrospective Data Analysis

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Estimating equivalence scales and non-food needs in Egypt: Parametric and semiparametric regression modeling

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