Modeling the dynamics of coronavirus with super-spreader class: A fractal-fractional approach

Xiao-ping, LI; Ullah, Saif; Zahir, Hina; Alshehri, Ahmed; Riaz, Muhammad Bilal; Alwan, Basem Al

doi:10.1016/j.rinp.2022.105179

Cited by 12 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this way, high degree nodes represents the most consistent individuals who acted as hubs or super-spreaders. This is motivated by recent work which studies the role of super-spreaders in highly infectious disease such as COVID-19 43 , 44 . The DegMST graph is completed when the average degree k is the same as the dynamic graph .…”

Section: Methodsmentioning

confidence: 99%

Static graph approximations of dynamic contact networks for epidemic forecasting

Shirzadkhani,

Huang,

Leung

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Epidemic modeling is essential in understanding the spread of infectious diseases like COVID-19 and devising effective intervention strategies to control them. Recently, network-based disease models have integrated traditional compartment-based modeling with real-world contact graphs and shown promising results. However, in an ongoing epidemic, future contact network patterns are not observed yet. To address this, we use aggregated static networks to approximate future contacts for disease modeling. The standard method in the literature concatenates all edges from a dynamic graph into one collapsed graph, called the full static graph. However, the full static graph often leads to severe overestimation of key epidemic characteristics. Therefore, we propose two novel static network approximation methods, DegMST and EdgeMST, designed to preserve the sparsity of real world contact network while remaining connected. DegMST and EdgeMST use the frequency of temporal edges and the node degrees respectively to preserve sparsity. Our analysis show that our models more closely resemble the network characteristics of the dynamic graph compared to the full static ones. Moreover, our analysis on seven real-world contact networks suggests EdgeMST yield more accurate estimations of disease dynamics for epidemic forecasting when compared to the standard full static method.

show abstract

Section: Methodsmentioning

confidence: 99%

Static graph approximations of dynamic contact networks for epidemic forecasting

Shirzadkhani,

Huang,

Leung

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Further the authors provided the existence and uniqueness of the problem under consideration. A similar study on the application of CF fractional modeling approach to explore the transmission and control of pine wilt infection is presented in Shah et al 23 The application of a generalized fractional operator in CF case to electrical circuits is demonstrated in Alshabanat et al 24 Li et al 25 studied the dynamics of COVID-19 with a case study of Saudi Arabia in the presence of various interventions and super-spreaders with the help of a new CF fractional-order model. Recently, Salah et al 26 studied the dynamics of dengue fever with the help of fractional modeling approach based on CF derivative.…”

Section: Introductionmentioning

confidence: 98%

Mathematical assessment of a fractional‐order vector–host disease model with the Caputo–Fabrizio derivative

Chu

Khan

Ullah

et al. 2022

Math Methods in App Sciences

Self Cite

View full text Add to dashboard Cite

Vector-host diseases outbreak is a major public health concern, and it has greatly affected human health and economy in various regions around the globe. Different approaches have been adopted to investigate the dynamical behavior and possible control of these diseases. In this study, we present a compartmental transmission model in order to explore the dynamics of vector-host infectious diseases. The saturated incidence rate instead of bilinear (or standard) and saturated treatment function is considered in model formulation which enhance the biological suitability of the proposed model. We first formulate the model based on nonlinear classical integer-order differential equations. Then, the proposed integer-order model is reformulated using the fractional-order operator in Caputo-Fabrizio sense with nonsingular kernel. We investigate the model equilibria and evaluate the expression for the most important threshold parameter known as the basic reproduction number. Furthermore, the existence and uniqueness are presented via the fixed point approach. Additionally, using an efficient numerical scheme, the iterative solution of the model is obtained.Finally, we present the model simulations to illustrate the impact of arbitrary fractional order and some of other important parameters involved in the model on the disease dynamics and minimization.

show abstract

“…Motivated by the above literature, the present study develops a mathematical model that analyzes the role of super-spreaders on COVID-19 incidence and prevention with spatial and temporal impact. The present work is actually a spatial extension of the fractional order model 26 . To achieve our goals, initially a compartmental-based reaction-diffusion epidemic model is formulated.…”

Section: Introductionmentioning

confidence: 99%

A rigorous theoretical and numerical analysis of a nonlinear reaction-diffusion epidemic model pertaining dynamics of COVID-19

Wang,

Khan,

Ullah

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

The spatial movement of the human population from one region to another and the existence of super-spreaders are the main factors that enhanced the disease incidence. Super-spreaders refer to the individuals having transmitting ability to multiple pathogens. In this article, an epidemic model with spatial and temporal effects is formulated to analyze the impact of some preventing measures of COVID-19. The model is developed using six nonlinear partial differential equations. The infectious individuals are sub-divided into symptomatic, asymptomatic and super-spreader classes. In this study, we focused on the rigorous qualitative analysis of the reaction-diffusion model. The fundamental mathematical properties of the proposed COVID-19 epidemic model such as boundedness, positivity, and invariant region of the problem solution are derived, which ensure the validity of the proposed model. The model equilibria and its stability analysis for both local and global cases have been presented. The normalized sensitivity analysis of the model is carried out in order to observe the crucial factors in the transmission of infection. Furthermore, an efficient numerical scheme is applied to solve the proposed model and detailed simulation are performed. Based on the graphical observation, diffusion in the context of confined public gatherings is observed to significantly inhibit the spread of infection when compared to the absence of diffusion. This is especially important in scenarios where super-spreaders may play a major role in transmission. The impact of some non-pharmaceutical interventions are illustrated graphically with and without diffusion. We believe that the present investigation will be beneficial in understanding the complex dynamics and control of COVID-19 under various non-pharmaceutical interventions.

show abstract

Modeling the dynamics of coronavirus with super-spreader class: A fractal-fractional approach

Cited by 12 publications

References 23 publications

Static graph approximations of dynamic contact networks for epidemic forecasting

Static graph approximations of dynamic contact networks for epidemic forecasting

Mathematical assessment of a fractional‐order vector–host disease model with the Caputo–Fabrizio derivative

A rigorous theoretical and numerical analysis of a nonlinear reaction-diffusion epidemic model pertaining dynamics of COVID-19

Contact Info

Product

Resources

About