Dynamics of SARS-CoV-2 outbreak in the Kingdom of Saudi Arabia: A predictive model

Aletreby, Waleed Tharwat; Alharthy, Abdulrahman; Faqihi, Fahad; Mady, Ahmed; Ramadan, Omar; Huwait, Bassim Mohammad; Alodat, Mohammed; Lahmar, Abdullah Ba; Mahmood, Nasir; Mumtaz, Shahzad; Alzayer, Waseem; Karakitsos, Dimitrios

doi:10.4103/sccj.sccj_19_20

Cited by 18 publications

(33 citation statements)

References 20 publications

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“…Their results predicted that the total number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Aletreby et al [ 22 ] predicted that the pandemic would peak by the end of July 2020. Further, the work in [ 23 ] used the SIR model to predict future trends and compare the impact of control measures taken by Saudi Arabia and the United Kingdom on the outcomes of COVID-19 pandemic.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis

Alrasheed

Althnian

Kurdi

et al. 2020

IJERPH

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The novel coronavirus Severe Acute Respiratory Syndrome (SARS)-Coronavirus-2 (CoV-2) has resulted in an ongoing pandemic and has affected over 200 countries around the world. Mathematical epidemic models can be used to predict the course of an epidemic and develop methods for controlling it. As social contact is a key factor in disease spreading, modeling epidemics on contact networks has been increasingly used. In this work, we propose a simulation model for the spread of Coronavirus Disease 2019 (COVID-19) in Saudi Arabia using a network-based epidemic model. We generated a contact network that captures realistic social behaviors and dynamics of individuals in Saudi Arabia. The proposed model was used to evaluate the effectiveness of the control measures employed by the Saudi government, to predict the future dynamics of the disease in Saudi Arabia according to different scenarios, and to investigate multiple vaccination strategies. Our results suggest that Saudi Arabia would have faced a nationwide peak of the outbreak on 21 April 2020 with a total of approximately 26 million infections had it not imposed strict control measures. The results also indicate that social distancing plays a crucial role in determining the future local dynamics of the epidemic. Our results also show that the closure of schools and mosques had the maximum impact on delaying the epidemic peak and slowing down the infection rate. If a vaccine does not become available and no social distancing is practiced from 10 June 2020, our predictions suggest that the epidemic will end in Saudi Arabia at the beginning of November with over 13 million infected individuals, and it may take only 15 days to end the epidemic after 70% of the population receive a vaccine.

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Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis

Alrasheed

Althnian

Kurdi

et al. 2020

IJERPH

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“…Finally, the recovered compartment represents Individuals that have either recovered or died. Mathematically different compartments are governed by following differential equations (1)(2)(3)(4). It is assumed that the total population ( ) remains fixed and recovered individuals remain immune to the disease during the study period.…”

Section: Base Seir Modelmentioning

confidence: 99%

“…It mainly causes the common cold and could cause severe acute respiratory syndrome and may develop into fatal pneumonia [1]. It transmits from person to person through direct and indirect contact, the fecal-oral route, and respiratory aerosolized droplets [2][3]. It was first reported in Wuhan in late December 2019, and the first case in Saudi Arabia was reported on March 2, 2020 [1].…”

Section: Introductionmentioning

confidence: 99%

“…The base compartment epidemiology model is Susceptible-Infectious-Recovered (SIR) [5] and the extension of it is Susceptible-Exposed-Infectious-Recovered (SEIR). To study the dynamics of the COVID-19 epidemic, several modified SEIR models have been used to incorporate various measures, especially social distancing, testing, public responses, and mobility restrictions [2,[6][7][8][9]. The modified models have a large number of parameters that increase the complexity.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 Modeling in Saudi Arabia Using the Modified Susceptible-Exposed-Infectious-Recovered (SEIR) Model

Ahmad¹

2020

Cureus

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The coronavirus disease 2019 (COVID-19) pandemic has created unprecedented healthcare emergencies across the globe. The World Health Organization (WHO) has proposed social distancing (SD) as a prudent measure to contain the pandemic and, hence, governments have been enacting lockdowns of varied nature. These lockdowns, causing economic and social strain, warrant the development of quantitative models to optimally manage the pandemic. Similarly, extensive testing aids in early detection and isolation, hence containing the spread of the pandemic. Compartment epidemiology models have been used extensively in modeling such infectious diseases. This paper attempts to utilize the modified Susceptible-Exposed-Infectious-Recovered (SEIR) model incorporating the SD, testing, and infectiousness of exposed and infectious compartments to study the COVID-19 pandemic in Saudi Arabia. Saudi Arabia has put restrictions on the movement of people in different phases to ascertain SD. Timedependent parameters based on the timeline of restrictions and testing in Saudi Arabia have been introduced to capture SD and testing. The arrived model has been validated through statistical tests. The (R naught), basic reproduction number, value has ranged between 0.6014 and 2.7860 with an average of 1.4904 and currently holds at 0.8952. In the absence of SD and testing measures, the model predicts the threshold herd immunity to be 69.31% and value as 3.26. Further, scenario analysis has been conducted for alleviating the SD measure. The results show that early lifting of all restrictions may undo all efforts in the containment of the COVID-19 pandemic. The outcome of results will help policymakers and medical practitioners prepare better to manage the pandemic and lockdown.

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“…Since the first case of severe acute respiratory syndrome coronavirus 2 (COVID-19) infection was identified at the end of 2019, COVID-19 has become a huge threat to global health [1,2] . The full spectrum of clinical manifestations of COVID-19 ranges from asymptomatic carriage and mild acute respiratory disease, to severe pneumonia and even acute respiratory distress syndrome (ARDS) [3] .…”

Section: Introductionmentioning

confidence: 99%

Tocilizumab Effect in COVID-19 Hospitalized Patients: A Systematic Review and Meta-Analysis of Randomized Control Trials

Abdulrahman

Aletreby

Mady

et al. 2021

Preprint

Self Cite

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Since the emergence of the first cases of COVID 19 viral pneumonia late 2019 several studies evaluated the benefits of different treatment modalities. Early in the pandemic, the interleukin 6 (IL 6) receptor antibody Tocilizumab was considered in view of the cytokine release syndrome associated with COVID 19 infection. Several early observational studies showed beneficial effect of treatment with Tocilizumab on mortality, however, results from well designed randomized clinical trials (RCT) were contradicting. Objectives: To perform a systematic literature review and meta-analysis of RCTs utilizing Tocilizumab in the treatment of COVID 19 pneumonia, with in hospital mortality as a primary objective, while secondary objectives included composite outcome of mortality, intubation, or ICU admission, another secondary outcome was super added infection. Method: This was a random effects model (DerSimonian and Laird) model of relative risk (RR), along with corresponding 95% confidence intervals, p values, and forest plots of both primary and secondary outcomes. A fixed effect sensitivity test was performed for the primary outcome, in addition to subgroup and meta regression analyses with predefined criteria. Results: The primary outcome of mortality showed statistically insignificant reduction of mortality with Tocilizumab (RR = 0.9, 95% CI: 0.8 to 1.01; p = 0.09) although with an unmistakable apparent clinical benefit. There was a significant reduction in the RR of the secondary composite outcome (RR = 0.83, 95% CI: 0.76 to 0.9; p < 0.001), and no difference between groups in super added infection (RR = 0.77, 95% CI: 0.51 to 1.19; p = 0.24). Treatment protocol allowing a second dose was the only significant predictor of improved mortality in the meta regression analysis. Certainty of evidence was reduced to moderate for the primary outcome and the secondary outcome of clinical deterioration, while it was reduced to low for the secondary outcome of super added infection. Conclusion: Moderate certainty of evidence suggest no statistically significant improvement of 28-30 day all cause mortality of hospitalized COVID-19 patients treated with TCZ, although there may be clinically important value. Moderate certainty of evidence suggest lowered relative risk of a composite outcome of death or clinical deterioration, while, low grade evidence indicate no increase in the risk of super added infection associated with TCZ treatment. A protocol allowing two doses of TCZ shows evidence of improved mortality as compared to a strictly single dose protocol.

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Dynamics of SARS-CoV-2 outbreak in the Kingdom of Saudi Arabia: A predictive model

Cited by 18 publications

References 20 publications

COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis

COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis

COVID-19 Modeling in Saudi Arabia Using the Modified Susceptible-Exposed-Infectious-Recovered (SEIR) Model

Tocilizumab Effect in COVID-19 Hospitalized Patients: A Systematic Review and Meta-Analysis of Randomized Control Trials

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