Statistical and Stochastic Models in Epidemiology

Dear Colleagues,

The growing complexity and frequency of infectious disease outbreaks underscore an urgent need for robust statistical and stochastic modelling frameworks. Such frameworks are essential for accurately capturing disease dynamics, quantifying uncertainty, and informing public health decision-making. Global experience has consistently demonstrated the pivotal role of mathematical and statistical models in elucidating transmission mechanisms, evaluating interventions, and guiding evidence-based policy.

The progression of an epidemiological process can be formally described as a sequence of state transitions—exposure, infection, latency, infectiousness, recovery, immunity, and potential reinfection—each governed by probabilistic mechanisms involving biological, behavioural, and environmental factors. Mathematically modelling these transitions and their interactions is fundamental to predicting outbreak trajectories, estimating disease burden, and optimising control strategies.

This Special Issue covers the full spectrum of diseases within epidemiology, including both infectious and non-infectious, directly and indirectly transmitted, and ranging in severity from latent and chronic to subacute and acute. The scope encompasses all levels of population occurrence, from sporadic to pandemic, and diverse clinical presentations.

The aim of this Special Issue is to collate recent advances and innovative applications in statistical and stochastic epidemiological modelling. We welcome contributions that address both methodological foundations and practical applications, including but not limited to probabilistic modelling, likelihood-based and Bayesian inference, machine learning integration, and simulation-based methodologies. Relevant model classes include compartmental models (e.g., SIR, SEIR, Ross–Macdonald), stochastic differential equations, agent-based models, network models, metapopulation frameworks, time series and stochastic processes, geospatial and autocorrelation methods, and modern machine learning or hybrid techniques.

We encourage submissions that advance the understanding of epidemic and endemic processes through novel modelling paradigms, rigorous inference from data, and computational statistics. Topics of interest include uncertainty quantification, model identifiability and validation, spatio-temporal dynamics, causal inference for interventions, and predictive analytics.

This Special Issue seeks to promote interdisciplinary research that bridges mathematics, statistics, epidemiology, computer science, and public health. Our objective is to foster collaboration between theoreticians and practitioners, champion reproducible and interpretable methodologies, and highlight cutting-edge developments that enhance global preparedness and response to diverse disease threats.

We look forward to receiving your valuable contributions.

Sincerely,

Dr. Babak Jamshidi
Guest Editor

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