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Mathematical modeling of infectious diseases

Offered to postgraduate students (MSc in Biostatistics & Health Data Science)
 
Course Topics

I. Foundational Concepts

  • Introductory concepts in infectious diseases.

  • Basic concepts in epidemics: Basic Reproduction Number, epidemic curve, generation time, incubation period, latent period, herd immunity, Effective Reproduction Number.

  • Methods for estimating basic & effective reproduction number

II. Deterministic Models

  • Introduction to the design of mathematical models for infectious diseases (SIR, SEIR).

  • Application of SIR and SEIR models.

III. Age mixing

  • Age mixing of the population and Social Contact Matrices.

  • Estimation of the effect of social distancing measures using social contact matrices.

  • Age-structured mathematical models.

  • Social Contact Matrices and Age-structured models.

IV. Specialized Models and Networks

  • Models for vector-borne disease transmission – application to hospital-acquired infections (nosocomial infections).

  • Networks and infectious diseases – centrality measures.

  • Models for vector-borne disease transmission. Networks.

V. Stochastic and Bayesian Methods

  • Introduction to stochastic epidemic models and their relationship with deterministic models.

  • Introduction to Bayesian Statistics and associated computational techniques.

  • Analysis of stochastic models and their behavior at the start of an epidemic. Final epidemic size. Parameter estimation.

  • Inference for stochastic chain binomial models using the BUGS software.

  • Bayesian inference for epidemic models using the Stan software.

VI. Advanced Applications

  • Models in structured populations, spread in households, and epidemics on networks.

  • Indirect observation, COVID and flu data, multiple data sources, and time-varying transmission.

  • Statistical inference for structured epidemic models.

Semester: 

Winter