Infectious Disease Dynamics
En podcast av Cambridge University
53 Avsnitt
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What can we learn from viral phylogenies?
Publicerades: 2013-08-23 -
Future of network modelling
Publicerades: 2013-08-23 -
Network measurement: past and future
Publicerades: 2013-08-23 -
Modelling infectious agents in food webs
Publicerades: 2013-08-23 -
On the Formulation of Deterministic Epidemic Models
Publicerades: 2013-08-23 -
Multiple Data Sources, Missing and Biased Data
Publicerades: 2013-08-23 -
Inference of epidemiological dynamics using sequence data: application to influenza
Publicerades: 2013-08-23 -
Quantifying Uncertainty in Model Predictions
Publicerades: 2013-08-23 -
Theory and practice of infectious disease surveillance
Publicerades: 2013-08-23 -
Design and Analysis of Vaccine Trials
Publicerades: 2013-08-23 -
Early warning signals of critical transitions in infectious disease dynamics
Publicerades: 2013-08-23 -
Stochastic epidemic modelling and analysis: current perspective and future challenges
Publicerades: 2013-08-22 -
Stochastic epidemic modelling and analysis: current perspective and future challenges
Publicerades: 2013-08-22 -
Inference pipelines for nonlinear time series analysis applied to an emerging childhood infection
Publicerades: 2013-08-22 -
Some challenges to make current data-driven (‘statistical’) models even more relevant to public health
Publicerades: 2013-08-22 -
Data and Statistics: New methods and future challenges
Publicerades: 2013-08-22 -
Embracing the complexities of scale and diversity in disease ecology
Publicerades: 2013-08-22 -
Multi-host, multi-parasite dynamics
Publicerades: 2013-08-22 -
Dollars and disease: developing new perspectives for public health
Publicerades: 2013-08-22 -
Infectious diseases in the changing landscape of public health
Publicerades: 2013-08-22
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.
