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Mathematical Methods and Models in Epidemiology, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 736

Special Issue Editors


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Guest Editor
Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460-8093, USA
Interests: mathematical biology; control theory
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460-8093, USA
Interests: systems and their applications; computational modeling; mathematics education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Mathematical Biology will focus on recent developments in epidemiological methods and models. We are seeking articles that compare existing models as well as newer versus older models, and those that provide an overview of the development of new models and numerical studies. We expect several of these articles to deal with general theory, while others will cover more specific epidemic and endemic diseases. To be considered for submission, articles should not be purely based on conjectural possibilities but should be related to a current situation or connect a current situation to previous ones.

We hope to hear from authors throughout the world, especially those who are carefully studying the dynamics in the most affected areas.

Dr. James P. Braselton
Prof. Dr. Martha L. Abell
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical biology
  • epidemiology
  • epidemic models
  • endemic models
  • COVID-19

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Published Papers (1 paper)

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Research

16 pages, 1424 KB  
Article
An Age-Structured Model for COVID-19 Hospitalization Rate
by Lingju Kong, Ryan Z. Shi and Min Wang
Mathematics 2026, 14(1), 58; https://doi.org/10.3390/math14010058 - 24 Dec 2025
Viewed by 310
Abstract
In this study, an age-structured framework is developed to model post-pandemic COVID-19 hospitalization rates. A partial differential equation (PDE) incorporating age-specific information is first formulated and analyzed. Based on this PDE, a physics-informed neural network (PINN) is constructed and calibrated using real-world data. [...] Read more.
In this study, an age-structured framework is developed to model post-pandemic COVID-19 hospitalization rates. A partial differential equation (PDE) incorporating age-specific information is first formulated and analyzed. Based on this PDE, a physics-informed neural network (PINN) is constructed and calibrated using real-world data. Through this integration, mathematical rigor is combined with the adaptability of machine learning (ML), resulting in an interpretable approach that leverages physical principles while reducing the opacity typically associated with ML-based models. Full article
(This article belongs to the Special Issue Mathematical Methods and Models in Epidemiology, 2nd Edition)
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