Epidemiological Characteristics of COVID-19 Ongoing Epidemic in Iraq

Al-Hussein, Abdul-Basset A.; Tahir, F.

doi:10.2471/blt.20.257907

Cited by 12 publications

(8 citation statements)

References 26 publications

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“…In [13], the authors developed an exponentiated M family of continuous distributions to provide new statistical models. Several researchers have studied COVID-19 in Iraq (see, e.g., [14][15][16]). Using several regression models, Amar et al [17] studied the actual COVID-19 database for Egypt from 15 February to 15 June 2020 to predict the number of COVID-19-infected patients and measured the final epidemic scale.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling, Control, and Prediction of the Spread of COVID-19 Using Compartmental, Logistic, and Gauss Models: A Case Study in Iraq and Egypt

Ibrahim

AL-Najafi

2020

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In this paper, we study and investigate the spread of the coronavirus disease 2019 (COVID-19) in Iraq and Egypt by using compartmental, logistic regression, and Gaussian models. We developed a generalized SEIR model for the spread of COVID-19, taking into account mildly and symptomatically infected individuals. The logistic and Gaussian models were utilized to forecast and predict the numbers of confirmed cases in both countries. We estimated the parameters that best fit the incidence data. The results provide discouraging forecasts for Iraq from 22 February to 8 October 2020 and for Egypt from 15 February to 8 October 2020. To provide a forecast of the spread of COVID-19 in Iraq, we present various simulation scenarios for the expected peak and its timing using Gaussian and logistic regression models, where the predicted cases showed a reasonable agreement with the officially reported cases. We apply our compartmental model with a time-periodic transmission rate to predict the possible start of the second wave of the COVID-19 epidemic in Egypt and the possible control measures. Our sensitivity analyses of the basic reproduction number allow us to conclude that the most effective way to prevent COVID-19 cases is by decreasing the transmission rate. The findings of this study could therefore assist Iraqi and Egyptian officials to intervene with the appropriate safety measures to cope with the increase of COVID-19 cases.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling, Control, and Prediction of the Spread of COVID-19 Using Compartmental, Logistic, and Gauss Models: A Case Study in Iraq and Egypt

Ibrahim

AL-Najafi

2020

Processes

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“…The SIR model of epidemic, Kermack and McKendrick's seminal compartment model [1] has been widely used to analyse various epidemics, and the ongoing COVID-19 pandemic is not an exception (See, for example, [2][3][4][5]). This paper explores how well one of its variants, the SEIR(D) model, fares with the current COVID-19 epidemic data for South Korea ('Korea' hereafter).…”

Section: Introductionmentioning

confidence: 99%

A multi-stage SEIR(D) model of the COVID-19 epidemic in Korea

Shin

2021

Annals of Medicine

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Background: This paper uses a SEIR(D) model to analyse the time-varying transmission dynamics of the COVID-19 epidemic in Korea throughout its multiple stages of development. This multi-stage estimation of the model parameters offers a better model fit compared to the whole period analysis and shows how the COVID-19's infection patterns change over time, primarily depending on the effectiveness of the public health authority's non-pharmaceutical interventions (NPIs). Methods: This paper uses the SEIR(D) compartment model to simulate and estimate the parameters for three distinctive stages of the COVID-19 epidemic in Korea, using a manually compiled COVID-19 epidemic dataset for the period between 18 February 2020 and 08 February 2021. The paper identifies three major stages of the COVID-19 epidemic, conducts multi-stage estimations of the SEIR(D) model parameters, and carefully infers context-dependent meaning of the estimation results to help better understand the unique patterns of the transmission of the novel coronavirus (SARS-CoV-2) in each stage. Results:The original SIR compartment model may produce a poor and even misleading estimation result if it is used to cover the entire period of the epidemic. However, if we use the model carefully in distinctive stages of the COVID-19 epidemic, we can find useful insights into the nature of the transmission of the novel coronavirus and the relative effectiveness of the government's non-pharmaceutical interventions over time. KEY MESSAGESIdentifies three distinctive waves of the COVID-19 epidemic in Korea. Conducts multi-stage estimations of the COVID-19 transmission dynamics using SEIR(D) epidemic models. The transmission dynamics of the COVID-19 vary over time, primarily depending on the relative effectiveness of the government's non-pharmaceutical interventions (NPIs). The SEIR(D) epidemic model is useful and informative, but only when it is used carefully to account for the presence of multiple waves and context-dependent infection patterns in each wave.

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“…Based on published outbreak data, reference [4] investigates the role of various methods of contact mitigation and suppression, such as self-quarantining, social distancing, and closure of schools and universities. Detailed reports and mathematical analyses of COVID-19 outbreaks in various countries and regions have been published in scientific media, such as [2,3,[5][6][7][8][9] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and optimal intervention of COVID‐19 outbreak

Biswas¹,

Ahmed²

2021

Quant. Biol.

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Background:The COVID-19 pandemic has become a formidable threat to global health and economy. The coronavirus SARS-CoV-2 that causes COVID-19 is known to spread by human-to-human transmission, and in about 40% cases, the exposed individuals are asymptomatic which makes it difficult to contain the virus.Methods: This paper presents a modified SEIR epidemiological model and uses concepts of optimal control theory for analysis of the effects of intervention methods of the COVID19. Fundamentally the pandemic intervention problem can be viewed as a mathematical optimization problem as there are contradictory outcomes in terms of reduced infection and fatalities but with serious economic downturns.Results: Concepts of optimal control theory have been used to determine the optimal control (intervention) levels of i) social contact mitigation and suppression, and ii) pharmaceutical intervention modalities, with minimum impacts on the economy. Numerical results show that with optimal intervention policies, there is a significant reduction in the number of infections and fatalities. The computed optimum intervention policy also provides a timeline of systematic enforcement and relaxation of stay-at-home regulations, and an estimate of the peak time and number of hospitalized critical care patients. Conclusions:The proposed method could be used by local and state governments in planning effective strategies in combating the pandemic. The optimum intervention policy provides the necessary lead time to establish necessary field hospitals before getting overwhelmed by new patient arrivals. Our results also allow the local and state governments to relax social contact suppression guidelines in an orderly manner without triggering a second wave.

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Epidemiological Characteristics of COVID-19 Ongoing Epidemic in Iraq

Cited by 12 publications

References 26 publications

Modeling, Control, and Prediction of the Spread of COVID-19 Using Compartmental, Logistic, and Gauss Models: A Case Study in Iraq and Egypt

Modeling, Control, and Prediction of the Spread of COVID-19 Using Compartmental, Logistic, and Gauss Models: A Case Study in Iraq and Egypt

A multi-stage SEIR(D) model of the COVID-19 epidemic in Korea

Mathematical modeling and optimal intervention of COVID‐19 outbreak

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