Infectious Disease Dynamics

A podcast by Cambridge University

53 Episodes

  1. Exponential Family Random Graph Models: A data-driven bridge between networks and epidemics

    Published: 8/22/2013

  2. Epidemics and population structure: One step forward, and two steps back

    Published: 8/22/2013

  3. Veterinary epidemiology: where mathematical modellers , biologists, animal scientists, and veterinarians (should) meet

    Published: 8/21/2013

  4. The evolution of pathogen evolution

    Published: 8/21/2013

  5. Linking models and data for infectious disease dynamics: rubella as a case-study

    Published: 8/21/2013

  6. Linking models and data: Sense and Susceptibility

    Published: 8/21/2013

  7. Twenty years of statistical methods for the study of infectious diseases

    Published: 8/21/2013

  8. Mathematical Models for the Control of Infectious Diseases With Vaccines

    Published: 8/21/2013

  9. Deterministic models: twenty years on. II. Spatially inhomogeneous models

    Published: 8/21/2013

  10. Deterministic models: twenty years on. I. Spatially homogeneous models

    Published: 8/21/2013

  11. Stochastic Methods - past, present and future

    Published: 8/21/2013

  12. Stochastic methods: past, present and future. Part I

    Published: 8/21/2013

  13. Setting the scene

    Published: 8/21/2013

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On 1 January 2013, it will be twenty years since Epidemic Models started as a 6-month programme in the first year of the Isaac Newton Institute for Mathematical Sciences. Since then, the field has grown enormously, in topics addressed, methods and data available (e.g. genetics/genomics, immunological data, social, contact, spatial, and movement data were hardly available at the time). Apart from these advances, there has also been an increase in the need for these approaches because we have seen the emergence and re-emergence of infectious agents worldwide, and the complexity and non-linearity of infection dynamics, as well as effects of prevention and control, are such that mathematical and statistical analysis is essential for insight and prediction, now more than ever before. Read more at http://www.newton.ac.uk/programmes/IDD/. Image from The New England Journal of Medicine, Gardy, 'Whole-Genome Sequencing and Social-Network Analysis of a Tuberculosis Outbreak', Volume 364, pp 730-9. Copyright ©2011 Massachusetts Medical Society. Reprinted with permission from Massachusetts Medical Society.