Infectious Disease Dynamics

A podcast by Cambridge University

53 Episodes

  1. What can we learn from viral phylogenies?

    Published: 8/23/2013

  2. Future of network modelling

    Published: 8/23/2013

  3. Network measurement: past and future

    Published: 8/23/2013

  4. Modelling infectious agents in food webs

    Published: 8/23/2013

  5. On the Formulation of Deterministic Epidemic Models

    Published: 8/23/2013

  6. Multiple Data Sources, Missing and Biased Data

    Published: 8/23/2013

  7. Inference of epidemiological dynamics using sequence data: application to influenza

    Published: 8/23/2013

  8. Quantifying Uncertainty in Model Predictions

    Published: 8/23/2013

  9. Theory and practice of infectious disease surveillance

    Published: 8/23/2013

  10. Design and Analysis of Vaccine Trials

    Published: 8/23/2013

  11. Early warning signals of critical transitions in infectious disease dynamics

    Published: 8/23/2013

  12. Stochastic epidemic modelling and analysis: current perspective and future challenges

    Published: 8/22/2013

  13. Stochastic epidemic modelling and analysis: current perspective and future challenges

    Published: 8/22/2013

  14. Inference pipelines for nonlinear time series analysis applied to an emerging childhood infection

    Published: 8/22/2013

  15. Some challenges to make current data-driven (‘statistical’) models even more relevant to public health

    Published: 8/22/2013

  16. Data and Statistics: New methods and future challenges

    Published: 8/22/2013

  17. Embracing the complexities of scale and diversity in disease ecology

    Published: 8/22/2013

  18. Multi-host, multi-parasite dynamics

    Published: 8/22/2013

  19. Dollars and disease: developing new perspectives for public health

    Published: 8/22/2013

  20. Infectious diseases in the changing landscape of public health

    Published: 8/22/2013

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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.