Computational and Stochastic Methods for Epidemic Modeling

A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Life Science, Biophysics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2491

Special Issue Editors


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Guest Editor
Department of Mathematics, Technische Universitat Chemnitz, 62, 09111 Chemnitz, Germany
Interests: computational biology; numerical analysis; non-linear dynamics; stochastic differential equations; stochastic methods
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Guest Editor
Department of Mathematics, Faculty of Sciences and Technology, University of Central Punjab, Lahore 54000, Pakistan
Interests: mathematical biology; infectious disease modeling; numerical analysis; differential equations; non-linear dynamics

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Guest Editor
1. Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan
2. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
Interests: neural networking of infectious diseases; predator–prey models in ecology; stochastic reaction–diffusion models; stochastic fractional-order models and chaos analysis in biological sciences; stochastic fractional delayed models
1. Department of Mathematics, Punjab Higher Education Department (PHED), Government of Punjab, Lahore, Pakistan
2. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
Interests: complex nonlinear stochastic dynamical systems; reduce-order models; machine learning; data assimilation; stochastic methods; numerical analysis of stochastic models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, stochastic calculus has witnessed tremendous progress in various areas of sciences and mathematics. Stochastic calculus provides a significant contribution to the scientific literature. Nowadays, highly nonlinear systems (real-world problems) are a hot issue in different types of modeling, such as engineering, biology, physics, chemistry, economics, and many more. Many complex, continuous, and discontinuous systems based on ordinary differential equations (ODEs), partial differential equations (PDEs), and stochastic differential equations, and their applications, still have no analytic solutions. This Special Issue mainly focuses on addressing a wide range of efficient models in different types of real-world problems. The various uses of computational methods in sciences have opened new challenging paths of research.

This Special Issue represents a platform for researchers and scientists for collaboration among mathematicians with other researchers.

We welcome and invite review and original research articles dealing with recent advances on the topics of stochastic calculus, as well as their applications in this particular issue.

This Special Issue will be focused on, but not limited to:

  • Stochastic differential equations and their applications;
  • New stochastic models and their properties;
  • Complex multiscale nonlinear stochastic dynamical systems;
  • Numerical methods for stochastic differential equations;
  • Uncertainty quantification;
  • Data assimilation;
  • Reduced-order models;
  • Parameterization;
  • Machine learning;
  • Chaos analysis in biological sciences;
  • Computer methods in economy-based problems;
  • Neural networking of infectious diseases;
  • Predator–prey models in ecology;
  • Stochastic reaction–diffusion models;
  • Stochastic fractional-order models and chaos analysis in biological sciences;
  • Stochastic fractional delayed models;
  • Advances in numerical techniques regarding stochastic aspects.

Dr. Muhammad Mohsin
Prof. Dr. Muhammad Rafiq
Dr. Nauman Ahmed
Dr. Ali Raza
Guest Editors

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Published Papers (1 paper)

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Research

14 pages, 483 KiB  
Article
Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process
by Ahmed Nafidi, Yassine Chakroune, Ramón Gutiérrez-Sánchez and Abdessamad Tridane
Fractal Fract. 2023, 7(9), 660; https://doi.org/10.3390/fractalfract7090660 - 31 Aug 2023
Cited by 1 | Viewed by 1257
Abstract
In this work, we study the possibility of using a new non-homogeneous stochastic diffusion process based on the Rayleigh density function to model the evolution of the active cases of COVID-19 in Morocco. First, the main probabilistic characteristics and analytic expression of the [...] Read more.
In this work, we study the possibility of using a new non-homogeneous stochastic diffusion process based on the Rayleigh density function to model the evolution of the active cases of COVID-19 in Morocco. First, the main probabilistic characteristics and analytic expression of the proposed process are obtained. Next, the parameters of the model are estimated by the maximum likelihood methodology. This estimation and the subsequent statistical inference are based on the discrete observation of the variable x(t) “number of active cases of COVID-19 in Morocco” by using the data for the period of 28 January to 4 March 2022. Then, we analyze the mean functions by using simulated data for fit and forecast purposes. Finally, we explore the illustration of using this new process to fit and forecast the active cases of COVID-19 data. Full article
(This article belongs to the Special Issue Computational and Stochastic Methods for Epidemic Modeling)
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