Special Issue on Data-Driven Biomathematical Modelling and Stochastic Analysis for Infectious Diseases: From Mechanisms to Public Health Action

Introduction:

The field of infectious disease modelling is undergoing a profound transformation, driven by the explosion of multi-source data and the critical need to quantify uncertainty in prediction and control. While traditional compartmental models have provided foundational insights, there is a growing demand for models that are not only mechanistically sound but also empirically grounded and inherently stochastic. This special issue aims to showcase the latest advancements at the intersection of data science, biomathematics, and stochastic analysis, highlighting how integrated approaches are revolutionizing our ability to understand, predict, and control infectious disease dynamics. We seek to bring together contributions that leverage novel data streams to inform model structures and parameters, while employing sophisticated stochastic frameworks to capture the inherent randomness of disease transmission and evolution. The ultimate goal is to bridge the gap between theoretical models and actionable public health strategies, providing robust tools for decision-making under uncertainty.

Topics covered:

We invite the submission of high-quality original research and review articles that address, but are not limited to, the following themes:

1.  Data-Driven Model Development and Calibration

1-1.  Integration of novel data sources (e.g., genomic, serological, mobility, digital surveillance, climate) into mechanistic models.

1-2.  Machine learning and AI techniques for parameter estimation, model selection, and nowcasting/forecasting.

1-3.  Multi-scale modelling linking within-host dynamics to between-host transmission using data.

2.  Stochastic Modelling and Analysis

2-1. Advanced stochastic frameworks: Stochastic Differential Equations, Continuous Time Markov Chains, Branching Processes, and Agent-Based Models.

2-2. Analysis of extinction, persistence, and critical community size in stochastic systems.

2-3. Quantification of outbreak risks and super-spreading events.

3.  Uncertainty Quantification and Risk Assessment

3-1. Methods for propagating and quantifying uncertainty from data to model predictions.

3-2. Robust optimization of intervention strategies (e.g., vaccination, testing, social distancing) under deep uncertainty.

3-3. Bayesian approaches for model inference and forecasting.

4.  Case Studies and Public Health Applications

4-1. Models for emerging and re-emerging infectious diseases (e.g., Coronavirus, Mpox, Avian Influenzas, Dengue, Chikungunya virus and etc.).

4-2. Analysis of antimicrobial resistance spread.

4-3. Evaluating the impact and cost-effectiveness of public health interventions in realistic, stochastic settings.

Submission Instructions: 

Please read the Guide for Authors | Infectious Disease Modelling before submitting. All articles should be Editorial Manager®, please select VSI: Data-Driven Biomathematical Modelling on submission.

Submission deadline: November 1st, 2026

Guest Editors:

If you would like to discuss any aspect of this special issue, please do not hesitate to contact one of the guest editors:

• Weiming Wang, Professor, Huaiyin Normal University, PR China, wangwm_math@hytc.edu.cn

• Sheikh Taslim Ali, Professor, The University of Hong Kong, Hong Kong SAR China, alist15@hku.hk

• Zhihang Peng, Professor, Chinese Center for Disease Control and Prevention, PR China, pengzh@chinacdc.cn

• Kai Wang, Professor, Xinjiang Medical University, PR China, wangkaimath@sina.com

• Kaifa Wang, Professor, Southwest University, PR China, kfwang72@163.com