Global sensitivity analysis of COVID-19 mathematical model

Zhang, Zizhen; Gul, Raheem; Zeb, Anwar

doi:10.1016/j.aej.2020.09.035

Cited by 37 publications

(16 citation statements)

References 18 publications

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“…1 saturates to P. Such graph is composed of two parts: the first shows that there are always new infections, since but more important that their number is initially accelerating day after day. This is seen from the second derivative (6) showing that the curve is concave upwards until a point of inflection is reached, a point crucial in the development of the pandemic. To obtain it, we set P − N 0 − N 0 e ct = 0 , giving the timing at which this inflection point occurs; that is (around the 20th of June).…”

Section: Methodsmentioning

confidence: 97%

“…Some investigated the uncertainty of the parameters fed into the model [4,5]; e.g. health care management, the natural history of the disease, possible survey biases, the transmission behavioral factors, the sampling or measurement errors, etc.… Determining the sensitivities to changes in these parameter values will help identify those key parameters that can help the decision makers explore various policies [6]. In general, not that modeling needs a laborious effort on the part of the modeler, but demands also a substantial team of specialists, whether in virology, medicine, epidemiology, ecology, public health, policy makers, statistics, computer simulation experts, etc.… and sponsored by large institutions [7].…”

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confidence: 99%

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Modeling the COVID-19 spread, a case study of Egypt

Deif

El‐Naggar

2021

J Egypt Math Soc

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In this article, the authors applied a logistic growth model explaining the dynamics of the spread of COVID-19 in Egypt. The model which is simple follows well-known premises in population dynamics. Our aim is to calculate an approximate estimate of the total number of infected persons during the course of the disease. The model predicted—to a high degree of correctness—the timing of the pandemic peak$$t_{{\text{m}}}$$ t m and the final epidemic size$$P$$ P ; the latter was foreseen by the model long before it was announced by the Egyptian authorities. The estimated values from the model were also found to match significantly with the nation reported data during the course of the disease. The period in which we applied the model was from the first of April 2020 until the beginning of October of the same year. By the time the manuscript was returned for revision, the second wave swept through Egypt and the authors felt obliged to renew their study. Finally, a comparison is made with the SIR model showing that ours is much simpler; yet leading to the same results.

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Section: Methodsmentioning

confidence: 97%

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confidence: 99%

Modeling the COVID-19 spread, a case study of Egypt

Deif

El‐Naggar

2021

J Egypt Math Soc

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“…As proposed in [33] , Let D denote the domain:

Suppose that

satisfies the Lipschitz condition:

and whenever the pair of

and

belong to the domain D, where

represents a non-negative constant. Then, there exist a constant

such that there exists a unique (exactly one) continuous vector solution

of the system (9) in the interval

.…”

Section: Model Analysismentioning

confidence: 99%

“…If

has continuous partial derivative

on a bounded closed convex domain

. The interest is on the domain:

So we look for a bounded solution of the form

as proposed in the study [33] . In proving Lemma 1 we need to consider Theorem 4 .…”

Section: Model Analysismentioning

confidence: 99%

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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“…In this case, . In COVID-19, the asymptomatic infection has been regarded as an important transmission path (He et al, 2020 ) and the asymptomatic class has been incorporated into models (Kuniya & Inaba, 2020 ; Zhang et al, 2021 ).…”

Section: Basic Models and Conceptsmentioning

confidence: 99%

Structure of epidemic models: toward further applications in economics

Kuniya¹

2021

JER

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In this paper, we review the structure of various epidemic models in mathematical epidemiology for the future applications in economics. The heterogeneity of population and the generalization of nonlinear terms play important roles in making more elaborate and realistic models. The basic, effective, control and type reproduction numbers have been used to estimate the intensity of epidemic, to evaluate the effectiveness of interventions and to design appropriate interventions. The advanced epidemic models includes the age structure, seasonality, spatial diffusion, mutation and reinfection, and the theory of reproduction numbers has been generalized to them. In particular, the existence of sustained periodic solutions has attracted much interest because they can explain the recurrent waves of epidemic. Although the theory of epidemic models has been developed in decades and the development has been accelerated through COVID-19, it is still difficult to completely answer the uncertainty problem of epidemic models. We would have to mind that there is no single model that can solve all questions and build a scientific attitude to comprehensively understand the results obtained by various researchers from different backgrounds.

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Global sensitivity analysis of COVID-19 mathematical model

Cited by 37 publications

References 18 publications

Modeling the COVID-19 spread, a case study of Egypt

Modeling the COVID-19 spread, a case study of Egypt

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

Structure of epidemic models: toward further applications in economics

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