System identification and mathematical modeling of the pandemic spread COVID-19 in Serbia

Šajić, Jasmina Lozanović; Langthaler, Sonja; Schröttner, Jörg; Baumgartner, Christian F.

doi:10.1016/j.ifacol.2022.06.003

Cited by 3 publications

(2 citation statements)

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“…In order to solve the problem, we use an algorithm based on finding the model parameters [12] that provide the best agreement between the actual data and the model function N ̃(𝑡) i.e. the best correlation of both according Solution (16) for model variants 𝑁(𝑡) (5)/( 6), ( 7)/(8), ( 10) is calculated using Excel and presented in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Statistical assessment of biogenic risk for the human population caused by COVID-19

et al. 2023

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In the paper an appreciable hierarchy of mathematical models is proposed. It describes the actual dynamics of COVID-19 thickness during 02/12/2020 – 09/22/2021. The incidence sub-model reflects reliably regular (aperiodic and periodic), as well as random components. It is established that the dynamics of the epidemy is essentially seasonal thrice a year. Model elaborated enables to clarify and explain weak weekly fluctuations in the death rate dynamics. It turned out that the maximum risk of death is at 15 and 22 days of disease duration. It means that this virus will presumably be a "satellite" of the human population with corresponded mortality at 1.75%. Calculations performed enable to estimate the level of stochastics in disease and death dynamics. It is near to the amplitude of periodic variation. Computer experiments with developed model predict the global dynamics of the incidence of COVID-19. New epidemic data can show the prospect of improving our model to regard the competitiveness between new sporadically emerging virus strains.

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Section: Resultsmentioning

confidence: 99%

Statistical assessment of biogenic risk for the human population caused by COVID-19

et al. 2023

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“…Various studies have explored the controllability and observability properties of mathematical models in different fields. Several researchers [3,6,9,10,19,24,25,26,28] have studied various aspects of controllability and observability in different types of dynamic systems, including time-fractional, heat equation, conformable fractional, robotic arms, fractional-order differential, and stochastic singular systems.…”

Section: Introductionmentioning

confidence: 99%

Fractional proportional linear control systems: A geometric perspective on controllability and observability

Bukhsh,

Younus,

Mukheimer

et al. 2024

Constructive Mathematical Analysis

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The paper presents a detailed analysis of control and observation of generalized Caputo proportional fractional time-invariant linear systems. The focus is on identifying controllable states and observable systems within the controllable subspace, null space, and unobservable subspace of the proposed system. The necessary conditions for the controllable subspace and the necessary and sufficient conditions for observability criteria are firmly established. The controllable subspace is treated geometrically as the set of controllable states, while the observable system is characterized by a zero unobservable subspace. The results are reinforced by examples and will immensely benefit future studies on fractional-order control systems.

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On the controllability and observability of fractional proportional linear systems

Bukhsh

Younus

2023

International Journal of Systems Science

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System identification and mathematical modeling of the pandemic spread COVID-19 in Serbia

Cited by 3 publications

References 1 publication

Statistical assessment of biogenic risk for the human population caused by COVID-19

Statistical assessment of biogenic risk for the human population caused by COVID-19

Fractional proportional linear control systems: A geometric perspective on controllability and observability

On the controllability and observability of fractional proportional linear systems

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