A new mathematical model of COVID-19 using real data from Pakistan

Peter, Olumuyiwa James; Qureshi, Sania; Yusuf, Abdullahi; Al-Shomrani, M. M.; Abioye, Adesoye Idowu

doi:10.1016/j.rinp.2021.104098

Cited by 102 publications

(72 citation statements)

References 36 publications

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“…In [41] , the authors presented a mathematical model qualitatively using stability theory of differential equations and the basic reproduction number that represents an epidemic indicator obtained from the largest eigenvalue of the so-called next-generation matrix to find the likely outcome of an outbreak that is beneficial for public health initiatives. Other mathematical models with ordinary time derivatives can be found in literature [4] , [14] , [30] , [31] , [32] , [41] .…”

Section: Introductionmentioning

confidence: 99%

Modeling the dynamic of COVID-19 with different types of transmissions

Amouch

Karim

2021

Chaos, Solitons & Fractals

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In this paper, we propose a new epidemiological mathematical model for the spread of the COVID-19 disease with a special focus on the transmissibility of individuals with severe symptoms, mild symptoms, and asymptomatic symptoms. We compute the basic reproduction number and we study the local stability of the disease-free equilibrium in terms of the basic reproduction number. Numerical simulations were employed to illustrate our results. Furthermore, we study the present model in case we took into consideration the vaccination of a portion of susceptible individuals to predict the impact of the vaccination program.

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Section: Introductionmentioning

confidence: 99%

Modeling the dynamic of COVID-19 with different types of transmissions

Amouch

Karim

2021

Chaos, Solitons & Fractals

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“…Several mathematical models [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] have been developed to study the transmission of the COVID-19 virus caused by the virus species “severe acute respiratory syndrome- related coronavirus”, named SARS-CoV-2. The virus has caused over thirty million active cases and over 600 million deaths in the United States as of July, 2021.…”

Section: Introductionmentioning

confidence: 99%

Estimation of epidemiological parameters for COVID-19 cases using a stochastic SEIRS epidemic model with vital dynamics

Otunuga¹

2021

Results in Physics

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We estimate and analyze the time-dependent parameters: transmission rate, symptomatic recovery rate, immunity rate, infection noise intensities, and the effective reproduction number for the United States COVID-19 cases for the period 01/22/2020-02/25/2021 using an innovative generalized method of moments estimation scheme. We assume the disease-dynamics is described by a stochastic susceptible-exposed-infected-recovered-susceptible (SEIRS) epidemic model, where the infected class is divided into the asymptomatic infected, and symptomatic infectious classes. Stochasticity appears in the model due to fluctuations in the disease’s transmission and recovery rates. The disease eradication threshold is derived from the reproduction number. The estimated parameters are used to model the disease outbreak’s possible trajectories. Our analysis reveals that current interventions are having positive effects on the transmission and recovery rates. The analysis is demonstrated using the daily United States COVID-19 infection and recovered cases for the period: 01/22/2020-02/25/2021.

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“…COVID-19 can be spread from one individual to another through inhaling respiratory droplets released from the nose or mouth of an infectious individual when talking, sneezing, or coughing [9] . Also, an individual can acquire this virus through fomite transmission [10] .…”

Section: Introductionmentioning

confidence: 99%

“…Older people aged 65 and above are more likely to be hospitalized or die from this disease. There is also a high severity of the disease to the group of people with underlying medical conditions such as hypertension, diabetes, cardiovascular, chronic respiratory disease and cancer [9] . Common clinical symptoms of the disease include tiredness, dry cough and fever [11] while serious symptoms include blood pressure, loss of movement or speech, chest pain and difficulty in breathing.…”

Section: Introductionmentioning

confidence: 99%

“…Many models of epidemiology divide their population into compartments in which assumptions are made about the nature and time rate of transfer among the human population [17] . These models act as a mathematical framework for studying the complexity of the dynamics of epidemiological processes [9] . A mathematical model is a major tool used to present COVID-19 transmission and its simulation can be used for predictions [16] .…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling of COVID-19 transmission dynamics between healthcare workers and community

Masandawa¹,

Mirau²,

Mbalawata³

2021

Results in Physics

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Corona-virus disease 2019 (COVID-19) is an infectious disease that has affected different groups of humankind such as farmers, soldiers, drivers, educators, students, healthcare workers and many others. The transmission rate of the disease varies from one group to another depending on the contact rate. Healthcare workers are at a high risk of contracting the disease due to the high contact rate with patients. So far, there exists no mathematical model which combines both public control measures (as a parameter) and healthcare workers (as an independent compartment). Combining these two in a given mathematical model is very important because healthcare workers are protected through effective use of personal protective equipment, and control measures help to minimize the spread of COVID-19 in the community. This paper presents a mathematical model named SWE HR; susceptible individuals (S), healthcare workers (W), exposed (E), symptomatic infectious ( ), asymptomatic infectious ( ), hospitalized (H), recovered (R). The value of basic reproduction number for all parameters in this study is 2.8540. In the absence of personal protective equipment and control measure in the public , the value of which implies the presence of the disease. When and were introduced in the model, basic reproduction number is reduced to 0.4606, indicating the absence of disease in the community. Numerical solutions are simulated by using Runge–Kutta fourth-order method. Sensitivity analysis is performed to presents the most significant parameter. Furthermore, identifiability of model parameters is done using the least square method. The results indicated that protection of healthcare workers can be achieved through effective use of personal protective equipment by healthcare workers and minimization of transmission of COVID-19 in the general public by the implementation of control measures. Generally, this paper emphasizes the importance of using protective measures.

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A new mathematical model of COVID-19 using real data from Pakistan

Cited by 102 publications

References 36 publications

Modeling the dynamic of COVID-19 with different types of transmissions

Modeling the dynamic of COVID-19 with different types of transmissions

Estimation of epidemiological parameters for COVID-19 cases using a stochastic SEIRS epidemic model with vital dynamics

Mathematical modeling of COVID-19 transmission dynamics between healthcare workers and community

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