Mathematical Modeling and Machine Learning for Biomedicine and Public Health

Speaker : Prof. Dr. Naveen K VAIDYA (San Diego State University,United States)

The course will offer a structured introduction to the full modeling workflow, beginning with the formulation of real-world problems and the translation of biological or physical assumptions into mathematical modeling. Participants will be guided through the construction of appropriate modeling frameworks—ordinary differential equation models, partial differential equation models, delay differential equation models, and stochastic models—selected according to the nature of the problem under study. The course will also address essential elements of model evaluation, including interpretation, validation, and practical applications to public-health-related scenarios.

The basic course will introduce participants to the use of biological data in mathematical models, covering the fundamentals of parameter estimation, introductory applications in within-host and between-host disease modeling.

Speaker : Dr. Jeremie GUEDJ (French Institute of Health & Medical Research,France)

The advanced level covers nonlinear least squares fitting, Maximum Likelihood Estimation (MLE), and Bayesian inference with MCMC, along with identifiability analysis. Participants also study uncertainty and sensitivity methods, such as Latin Hypercube Sampling (LHS) and PRCC, in conjunction with bootstrapping, AIC/BIC model selection, and cross-validation. Applications to within-host and between-host epidemic modeling.

Speaker : Prof. Dr. Stacey Smith (The University of Ottawa,Canada),Prof. Dr. Jonathan FORDE (Hobart and William Smith Colleges,United States)

This course provides a systematic study of between-host disease transmission, beginning with classical compartmental models such as SIR and SEIR and extending to heterogeneous populations, evolving susceptible networks that capture realistic differences in contact patterns and susceptibility. Participants will construct mathematical models of epidemic spread, derive and interpret reproduction numbers, and examine how theoretical insights can be directly linked to real-world epidemiological applications.

Speaker : Prof. Dr. Stanca Mihaela CIUPE (Virginia Tech,United States),Prof. Dr. Jonathan FORDE (Hobart and William Smith Colleges

This course provides a comprehensive study of within-host modeling, beginning with the foundations of viral dynamics and immune responses, and extending to advanced topics such as multiple timescales, immune evasion, and viral persistence. Participants will formulate and analyze differential equation models of host–pathogen interactions, study key mathematical properties (equilibria, stability, thresholds), and apply these insights to diseases such as HIV, COVID-19, and hepatitis. Laboratory sessions will accompany the lectures, enabling participants to simulate within-host models and link theoretical analysis with computational outcomes.

Speaker : Dr. Jeremie GUEDJ (French Institute of Health & Medical Research,France)

This course introduces participants to stochastic approaches in disease modeling, highlighting how randomness and uncertainty shape disease dynamics. Core topics include branching processes for early outbreak analysis, Gillespie algorithms for simulating infection and recovery events in continuous time, and random network models that capture heterogeneity in contact structures. Additional examples will cover Markov chain models for epidemic progression, stochastic differential equations to describe noisy dynamics, and extinction probabilities for emerging infections. Through guided lab sessions, participants will implement a variety of stochastic simulations and explore how uncertainty influences predictions of epidemic outcomes.

Speaker : Prof. Dr. Feng-Bin WANG (Chang Gung University,Taiwan,China)

This course focuses on rigorous mathematical analysis of models at both the (between-host) and (within-host) levels, with direct relevance to public health applications. Participants will study equilibrium points, reproduction numbers, stability properties, bifurcation analysis, and persistence conditions, and then apply these insights to case studies including dengue, measles, and COVID-19. The analytic framework will be directly connected to evaluating interventions such as vaccination, treatment, and vector control, ensuring that theoretical models inform real-world decision-making.

Speaker : Prof. Dr. Naveen K VAIDYA (San Diego State University,United States)

This course highlights machine learning as a central component of modern disease modeling. Participants will learn fundamental algorithms and hybrid approaches (e.g., physics-informed neural networks) to enhance predictive accuracy in epidemiological models. Case studies will demonstrate how machine learning methods can be integrated with mechanistic models to forecast outbreaks and assess public health interventions such as vaccination and treatment strategies. Lab sessions will focus on parameter estimation, data fitting, and simulation-based policy evaluation using machine learning techniques.

Speaker : Dr. Jeremie GUEDJ (French Institute of Health & Medical Research,France)

Hands-on training in MATLAB/Python, emphasizing parameter estimation, data fitting, model validation, and sensitivity analysis.

Speaker : Prof. Dr. Naveen K VAIDYA (San Diego State University,United States)

Lab sessions will focus on parameter estimation, data fitting, and simulation-based policy evaluation using machine learning techniques.

Speaker : Dr. Jeremie GUEDJ (French Institute of Health & Medical Research,France)

This lab trains participants to implement stochastic epidemic models using branching processes, Gillespie algorithms, and random networks. Through coding and simulations, they will compare stochastic and deterministic outcomes, explore variability and extinction probabilities, and analyze parameter sensitivity with real-data inspired examples.

Speaker : Prof. Dr. Stacey Smith (The University of Ottawa,Canada)

This session will be complemented by exercises and simulation-based labs, allowing participants to test and visualize model outcomes.

Speaker : Prof. Dr. Stanca Mihaela CIUPE (Virginia Tech,United States),Prof. Dr. Jonathan FORDE (Hobart and William Smith Colleges

This session will be complemented by exercises and simulation-based labs, allowing participants to test and visualize within-host model outcomes.

Speaker : Prof. Dr. Stanca Mihaela CIUPE (Virginia Tech,United States),Prof. Dr. Jonathan FORDE (Hobart and William Smith Colleges,Prof. Dr. Stacey Smith (The University of Ottawa,Canada),Dr. Khagendra Adhikari (Trihuvan University,N...

Participants will form small groups to develop, analyze, and simulate disease models under faculty mentorship. Projects will progress from model construction to simulations and results will be presented in the school and later at the poster session of ICMB-2027.