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ES4 presentation

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Dave MacLeod

on 7 May 2010

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Transcript of ES4 presentation

Seamless integration of climate and epidemiological models to assess the projection uncertainty of future infectious disease in the U.K. and Western Europe. Dave MacLeod Mphys: Physics with Australian Study
(2004-2008)
University of Exeter with 1 year in Sydney


MSc: Mathematical and Numerical modelling of the Atmosphere and Oceans
(2008-2009)
University of Reading
Supervisors:
Andy Morse (Department of Geography)

Matthew Baylis (Vetinary Clinical Science- LUCINDA group)
How does climate impact on disease? What does the future hold? Uncertainty Progress... The future Seamlessness Meteorological conditions influence both pathogen and vector lifecycles


Rainfall is required for vector (e.g. Mosquito) breeding sites

Temperature determines rate of parasite development

Temperature and humidity influence vector development and mortality
Climate models are arguably the best way to get some idea of what a future climate will look like
Epidemiological models are forced with meteorological variables, to produce future risk predictions
Modelled Malaria over Hwange, Zimbabwe (Hoshen & Morse 2004) “All models are wrong, but some are useful”
- George Box
What is seamlessness?
Seamlessness:

noun
1. The state or quality of being seamless Seamless

adj.
1. Having no seams. Temporal seamlessness:
Boundaries between time-scale prediction are only in the mind/models!
Spatial seamlessness:
Climate model resolution >> Important scales for disease Downscaling (statistical vs dynamical) Any other way around it? Neural networks
Statistical models
SIR models forced by climate variables Where does uncertainty come from? Trying to predict the future! Uncertainty in climate prediction Chaotic nature of the atmosphere
Imperfect models
Uncertainty in forcing Uncertainty in disease prediction “Junk in, junk out”
Structural uncertainty in impact models Uncertainty and how it’s dealt with in climate models
Ensemble prediction
Multi-model ensembles
Perturbed parameter
Stochastic physics Techniques for visualising climate data (Ferret, NCL)
Initial plotting of data from ENSEMBLES climate runs
Looking at decadal climate prediction
output from ENSEMBLES
Working with LUCINDA group to work
on forecasting of future infectious disease - improving structure of disease models:
adding land surface, animal/human populations Quantifying uncertainties in prediction of disease - risk assessment of climate change on human health How can uncertainty be communicated? - effectively and responsibly? Thanks for listening - any questions? HI HELLO
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