Dear Allstaters,

Please find below a reminder about a half-day meeting on `Stochastic models of
populations and epidemics' to be held next Friday, 28th April.

Further details can be found on our webpage:

http://www.maths.leeds.ac.uk/statistics/rss/

All welcome!

Regards, Paul
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Dr. Paul D. Baxter
Secretary/Treasurer, RSS Leeds/Bradford Local Group,
Department of Statistics, University of Leeds, Leeds, LS2 9JT,  UK.
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Leeds/Bradford: Friday 28th April, 2.30pm, Room 6.10 (Biology Museum),
L.C. Miall building, Leeds University (Refreshments from 2pm)

Stochastic models of populations and epidemics

Hosted by the Institute of Integrative and Comparative Biology,
University of Leeds, this half-day meeting will provide an opportunity
to discuss some important problems in stochastic population modelling. A
list of speakers appears below.

Jon Pitchford (University of York)
Stochastic models of growth, competition and evolution

The world of biology is an unpredictable place, and we should not be
surprised when deterministic models give misleading results. In fish,
where only a tiny minority of juveniles survive to adulthood, stochastic
differential and difference equations provide insights at scales ranging
from individuals to populations and fisheries management. Applying
similar methods to plant competition can reveal simple messages about
the underlying ecology. The consequences of stochasticity for
evolutionary optima will also be explored.

Stephen Cornell (University of Leeds)
Spatial and stochastic populations: beyond the method of moments

It is notoriously difficult to obtain mathematically exact results for
spatial, stochastic, interacting populations. I shall describe a method
for calculating the properties of such systems exactly in the limit of
long- (but finite-) range interactions. I shall illustrate the method
with examples from population ecology, and discuss how it is related to
moment closure.

Louise Matthews (University of Glasgow)
Super-shedding of E coli O157 in cattle and its implications for control

Super-shedding cattle shed much higher concentrations of the pathogenic
bacteria E. coli O157 than other infected individuals. By fitting
stochastic epidemiological models to prevalence data we are able to
quantify the relationship between infectiousness and bacterial load.
This allows us to identify targets for control which will reduce the
basic reproduction number below 1.