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Mathematics
Special Issues
Dynamic Model and Analysis of Biology and Epidemiology
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Dynamic Model and Analysis of Biology and Epidemiology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 114

Special Issue Editors

Prof. Dr. Xinzhu Meng

E-Mail Website
Guest Editor
Department of Information and Computational Sciences, Shandong University of Science and Technology, Qingdao 266590, China
Interests: evolutionary dynamics; dynamic modeling and analysis of problems related to mathematical biology
Dr. Tongqian Zhang

E-Mail Website
Guest Editor
Department of Mathematics Teaching and Research, Shandong University of Science and Technology, Qingdao 266590, China
Interests: modeling and analysis of population dynamics and epidemic dynamics

Special Issue Information

Dear Colleagues,

Mathematical modeling and dynamic analysis methods in biological and epidemiological research have developed rapidly worldwide. This Special Issue, “Dynamic Model and Analysis of Biology and Epidemiology”, aims to highlight research in this field. Areas covered include general mathematical methods and their applications in biology and epidemiology, with an emphasis on work related to mathematical modeling and dynamic analysis. Topics appropriate for this Special Issue include, but are not limited to, all areas of mathematical biology and epidemiology that employ dynamic (differential equation) models to describe the dynamic phenomena of life sciences.

Therefore, this Special Issue will consider novel articles that explore the population dynamics and epidemic dynamics. Potential contributions should present mathematical studies on the following topics (among others):

  • Dynamic system;
  • Mathematical biology;
  • Mathematical epidemiology;
  • Ecology and evolution of infectious diseases;
  • Simulation;
  • Stability;
  • Bifurcation.

Prof. Dr. Xinzhu Meng
Dr. Tongqian Zhang
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

  • population dynamics
  • epidemic dynamics
  • evolutionary dynamics
  • spatial dynamics
  • mathematical and computational modeling

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Published Papers (1 paper)

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Research

27 pages, 4702 KB  
Article
Comparative Mathematical Evaluation of Models in the Meta-Analysis of Proportions: Evidence from Neck, Shoulder, and Back Pain in the Population of Computer Vision Syndrome
by Vanja Dimitrijević, Bojan Rašković, Miroslav Popović, Patrik Drid and Borislav Obradović
Mathematics 2026, 14(3), 556; https://doi.org/10.3390/math14030556 (registering DOI) - 3 Feb 2026
Abstract
Meta-analysis of proportions requires a rigorous transformation model due to the inherent mathematical constraints of proportional data (boundedness and non-constant variance). This study compared four proportions (Untransformed, Freeman–Tukey, Logit, and Arcsine) to determine the most reliable and numerically stable estimator for pooled prevalence. [...] Read more.
Meta-analysis of proportions requires a rigorous transformation model due to the inherent mathematical constraints of proportional data (boundedness and non-constant variance). This study compared four proportions (Untransformed, Freeman–Tukey, Logit, and Arcsine) to determine the most reliable and numerically stable estimator for pooled prevalence. A rigorous comparative evaluation was performed using 35 empirical studies on Computer Vision Syndrome (CVS)-related musculoskeletal pain prevalence. The analysis employed frequentist methods, Monte Carlo simulations (10,000 iterations) to test CI coverage, and Bayesian sensitivity analysis. Key findings were validated using the Generalized Linear Mixed Model (GLMM), representing the one-step methodological standard. Pooled prevalence estimates were highly consistent (0.467 to 0.483). Extreme heterogeneity (I2 ≈ 98–99%) persisted across all models, with τ2 values exceeding 1.0 specifically in Logit and GLMM frameworks. Mixed-effects meta-regression confirmed that this heterogeneity was independent of study size (p = 0.692 to 0.755), with the moderator explaining virtually none of the variance (R2) of 0% to 0.2%. This confirms that the high variance is an inherent feature of the dataset rather than a statistical artifact. Simulations revealed a critical trade-off: while the Untransformed model provided minimal bias, its CI coverage failed significantly in small-sample boundary scenarios (N = 50, p = 0.01, coverage: 39.36%). Under these conditions, the PFT transformation was most robust (98.51% coverage), while the Logit model also maintained high coverage accuracy (91.07%) despite its variance inflation. We conclude that model selection should be context-dependent: the Untransformed model is recommended for well-powered datasets, whereas the PFT transformation is essential for small samples to ensure valid inferential precision. Full article
(This article belongs to the Special Issue Dynamic Model and Analysis of Biology and Epidemiology)
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