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VIMC short course - Mathematical Modelling for Vaccine-preventable Diseases

Updated: Dec 6, 2023


The Vaccine Impact Modelling Consortium (VIMC) and the University of Cambridge are running an intensive 6-day short course on Mathematical Modelling for Vaccine-preventable Diseases for participants from low and middle-income countries.


The programme combines lectures, discussions and seminars with a series of in-depth practical sessions that help to facilitate understanding of the underlying theory by putting these approaches into practice. The course will use the open-source programming language of R and is adapted from the well-established Wellcome Genome Campus Mathematical Models for Infectious Disease Dynamics Course.


The programme is aimed at any life scientist, public health, medical or veterinary professional based in a low- or middle- income country interested in learning quantitative approaches to infectious disease dynamics and control by vaccination. The basic concepts of the course are applied to human infectious disease systems, with reference to more general applications in wildlife, livestock and plant systems. Applicants whose existing work /research project involves the use of models or interactions with modellers and is focussed on vaccine-preventable diseases will be selected in priority.


The course starts with an introduction to computer programming from first principles, however participants who are not familiar with R are encouraged to learn the language basics (data analysis, vector manipulation and graphics) before attending.


The programme is generously sponsored by the Vaccine Impact Modelling Consortium (supported by Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation) and is offered free of charge to selected participants. Travel bursaries are also available.


Dates: September 4th to 11th 2022


Location: The Møller Institute, Cambridge, UK


There is a short deadline for applications, which closes on 8th July 2022.


Apply now to learn how to build, interpret and estimate models for infectious disease dynamics and control through vaccination.

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