Topics we had planned for 2016/7
Robert MacKay on Gaussian Processes
(outline as for 2017-8)
Matteo Icardi on Transport in Porous Media
14.00 on 12 Jan, 14.00 on 19 Jan, 14.00 on 2 Feb, 10.00 on 21 Feb, 13.00&14.00 on 23 Feb
Topics: micro and macro-scale transport equations, molecular vs mechanical dispersion, anomalous diffusion models, Continuous Time Random Walks, Non local and fractional diffusion equations
Annabelle Ballesta on Cancer modelling
10.00 on 10 Jan, 10.00 on 17 Jan, 13.00 on 19 Jan, 10.00 on 24 Jan, 10.00 on 31 Jan, 13.00 on 9 Feb
Content: mathematical models based on differential equations representing the dynamics of cancer cell populations and their control by anticancer drugs.
Deirdre Hollingsworth on Worms: epidemiology of helminth infections
13.00 on 26 Jan, 10.00 on 7 Feb, 14.00 on 9 Feb, 13.00&14.00 on 16 Feb, 13.00 on 2 Mar
Real-world impact: Worm infections affect billions of people globally, and include hookworm, roundworm, whip worm, but also insect-borne infections like river blindness or lymphatic filariasis. They cause chronic health losses, disability and stigma, rather than dramatic outbreaks, and so perpetuate the cycle of poverty. In recent years many of these infections have been targeted for elimination through the use of mass drug administration (MDA), with millions of people being dewormed every year. Some countries have already got infections down to such low levels that they are stopping their mass treatments and moving into a post-MDA surveillance phase. Mathematical modelling is used to inform the treatment strategies (who should be treated, how often should they be treated) and post-MDA surveillance strategies.
Mathematical modelling: The health consequences of a helminth infection and the infectiousness of an infected individual, depend on the number of worms they are infected with, therefore we cannot use the classic susceptible, infectious, recovered framework to model these infections, but rather have to keep track of the number of people who are infected with a certain number of worms or we model the mean worm burden. Because the timescales of these infections are much slower than many directly transmitted pathogens, we also keep track of the infection in the environment or the transmitting insects. We also have poor quality diagnostics, and therefore have to deal with uncertainties in the true underlying worm burden. In these lectures we will discuss the basis of some of these models and different approximations which have been used to characterise their dynamics.
Oral presentations by students (20 mins + 5 mins for questions)
Deadlines for submission of written reports will be announced here when decided.