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    Stochastic epidemic models with inference 2026

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    More information may appear later

    Course Instructors

    Tom Britton, Stockholm University

    Lorenzo Pellis, University of Manchester

     
    Course contents:

    Basic stochastic epidemic models and their properties
    Extensions (SEIR, multitype, …)
    Household epidemic models (overview)
    Network epidemic models (overview)
    Endemic models
    Interventions & Vaccination
    Applications
    Inference (based on final size or during epidemic), R_0, R_t, household models

    Time and Place

    All lectures take place at Stockholm University in Campus Albano, house 1, level 2. See "    Registration and Venue" page for further details.

    Lecture times (includes also problem solving, tutorials, computer sessions, ...): 

    Monday June 22: 9.00-10.30, 11.00-12.30, 14.00-15.30, 16.00-17.30

    Tuesday June 23: 9.00-10.30, 11.00-12.30, 14.00-15.30, 16.00-17.30

    Wednesday June 24: 9.00-10.30, 11.00-12.30

    General prerequisites

    It is expected that course participants have basic knowledge of statistics and mathematics and rudimentary knowledge of infectious disease epidemiology. It is also expected that participants have basic computer software knowledge and preferably are familiar with the software R.

    IMPORTANT: All participants are expected to bring a laptop with R installed on it.

    Additional prerequisites for this course

     Nothing additional beside general prerequisites, but good to know basic probability and statistics

    Relevant literature

    There are several monographs in the area, for example Diekmann, et al (2013): Mathematical tools for understanding infectious disease dynamics. Princeton UP.

    There are also some overview papers, some egocentric examples are:

    Britton, T. Epidemic models on social networks - with inference. (2020). Statistica Neerlandica  (Special issue on Network modelling and analysis), 74:3, 222-241, https://doi.org/10.1111/stan.12203

    Britton, T.: Stochastic epidemic models: a survey. (2010) Math. Biosci, 225, 24-35. (available below)

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