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