Transcript Document

Meningitis:
The climate controls
and potential for
prediction
Andy Morse Ph.D.
Department of Geography
University of Liverpool
[email protected]
Andy Morse University of Liverpool MMU Meningitis Lecture
Acknowledgements
• To many – too numerous to mention but special thanks to
• Meningitis – Anna Molesworth, HPA; Madeleine Thomson, IRI, NY
• Malaria – Moshe Hoshen, Physics, University of Liverpool; Anne
Jones, Geography and Physics, University of Liverpool.
• Seasonal Forecasting – ECMWF, The Met. Office and Mark Cresswell.
Andy Morse University of Liverpool MMU Meningitis Lecture
1.0 Background
Meningococcal Meningitis
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Bacterial meningitis (Neisseria
meningitidis) causes epidemics
12 serotypes are know only 4 cause
epidemics A, B, C and W135
Group A generally causes epidemics in
Africa although cases due to serogroups
C, X and W135 are found.
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B and C are more common in the U.K.
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Vaccines exist for A, C, X, Y and W135
Andy Morse University of Liverpool MMU Meningitis Lecture
1.1 Background
Meningococcal Meningitis
• Transmitted person to person
(sneezing, coughing, kissing)
(military recruits, students)
• Average period of incubation 4 days
( 2 to 10days)
• Estimated 10 to 25% carry the
bacterial but can increase in
epidemics
• U.K. matter of education and
seeking treatment
Andy Morse University of Liverpool MMU Meningitis Lecture
1.2 Background
Meningococcal Meningitis in Africa
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Meningitis epidemic disease, highly
seasonal - later half dry season
Epidemics every 5 to 10 years – kills
young adults as well as children
Climatic connections are ‘not proven’ low humidity (vapour pressure) and dust
important factors
Epidemics cease with the onset of the
rains
Figure from Cheesbrough,JS, Morse AP, Green SDR. Meningococcal meningitis
and carriage in western Zaire: a hypoendemic zone related to climate?
Epidemiology and Infection 1995: 114; 75-92
Andy Morse University of Liverpool MMU Meningitis Lecture
1.3 Background
West African Climate
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Area dominated by seasonal rains
produced by a monsoonal system
Strong latitudinal gradient in ‘wetness’
and thus climates and vegetation
Monsoon system is complex and not
well understood
Leads to large interannual climate
Andy Morse University of Liverpool MMU Meningitis Lecture
1.4 Background
West Africa Atlas
Andy Morse University of Liverpool MMU Meningitis Lecture
1.5 Background
West African Climate
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Monsoon System and AMMA
experiments
Andy Morse University of Liverpool MMU Meningitis Lecture
1.6 Background
West African Climate
NDVI February
NDVI August
From MARA eshaw website http://www.mara.org.za/eshaw.htm
Andy Morse University of Liverpool MMU Meningitis Lecture
1.7 Background
West African Climate
Animation from University of Liverpool
Understanding Epidemics Website
http://www.liv.ac.uk/geography/research_
projects/epidemics/MAL_intro.htm
Data from CLIVAR VACS Africa
Climate Atlas at University of Oxford
Andy Morse University of Liverpool MMU Meningitis Lecture
1.10 Background
Epidemic Cycles
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Many infectious diseases, in the tropics, have a strong seasonal cycle related to the
seasonal climatic cycles
Climatically anomalous years can lead to epidemics
Time between trigger threshold to epidemic peak often too short to take effective
intervention – need for skilful and timely seasonal climate forecast
140
Number of cases
120
100
Vaccine
80
60
40
Effect
Threshold
20
Reporting week
Andy Morse University of Liverpool MMU Meningitis Lecture
9847
9844
9841
9833
9830
9827
9824
9821
9818
9815
9812
9809
9806
9802
9751
9748
9745
0
Andy Morse University of Liverpool MMU Meningitis Lecture
2.0 Linking climate to disease
Example for meningitis in Africa
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Extensive literature search was
Spatial Distribution Meningitis
Epidemics 1841-1999 (n = c.425) 1
undertaken to identify reported
epidemics
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Published and grey literature
were consulted
1 Molesworth A.M., Thomson M.C., Connor S.J., Cresswell M.P.,
Morse A.P., Shears P., Hart C.A., Cuevas L.E. (2002) Where is the
Meningitis Belt?, Transactions of the Royal Society of Hygiene and
Tropical Medicine, 96, 242-249.
Andy Morse University of Liverpool MMU Meningitis Lecture
2.1 Linking climate to disease
Example for meningitis in Africa
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Statistical Model to produce a map of risk
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Epidemiological data and climatic and
environmental variables
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Risk factors:
Land cover type and seasonal absolute
humidity profile
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Seasonal dust profile, Population density,
Soil type
Significant but not included
in final model
Molesworth, A.M., Cuevas,L.E., Connor, S.J., Morse A.P.,
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Thomson, M.C. (2003). Environmental risk and meningitis
epidemics in Africa, Emerging Infectious Diseases, 9 (10),
1287-1293.
Human factors not included
Andy Morse University of Liverpool MMU Meningitis Lecture
2.2 Linking climate to disease
Example for meningitis in Africa
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Cluster analysis to define areas with
common seasonal cycle
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Absolute humidity values
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Used to produce
risk map shown above
Molesworth, A.M., Cuevas,L.E., Connor, S.J., Morse A.P., Thomson, M.C. (2003). Environmental
risk and meningitis epidemics in Africa, Emerging Infectious Diseases, 9 (10), 1287-1293.
Andy Morse University of Liverpool MMU Meningitis Lecture
2.4 Linking climate to disease
Values to give an absolute humidity of about 10 gm-3
T (temperature celsius)
T dew (celsius)
e (vapour pressure hPa)
40
12.5
14.5
30
12
14
30
11.5
13.6
10
11
13.1
Andy Morse University of Liverpool MMU Meningitis Lecture
2.5 Linking climate to disease
T dew variability
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Interannual variability in rainfall
Results in interannual variability in seasonal
T dew cycles
Gao, Mali 16.3 N 0.1W
Tdew
25.0
20.0
T dew (C)
15.0
10.0
5.0
0.0
Oct-95
Feb-96
May-96
Aug-96
Dec-96
-5.0
-10.0
Months
Andy Morse University of Liverpool MMU Meningitis Lecture
Mar-97
Jun-97
2.6 Linking climate to disease
Example for meningitis in Africa
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Disease is complex and dry air and dust
are not the only factors
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Many human ones – immunity, nutrition
and co-infection
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However the environmental variables may
lead to the population becoming more
susceptible
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The environmental variables may be
predictable months in advance.
Andy Morse University of Liverpool MMU Meningitis Lecture
3.0 Potential of Seasonal Forecasting
Background and applications
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Probabilistic forecasts are made
routinely
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Statistical models – more
established – more regionally and
single variable orientated – cannot
work outside their training data –
can work well e.g. spring SST to
summer rains (West Africa)
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Dynamic models – Ensemble
Prediction Systems – experimental
also operational too
Loaded dice example – loading and
hence predictability changes with
time and location
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Andy Morse University of Liverpool MMU Meningitis Lecture
3.1 Potential of Seasonal Forecasting
Dynamic EPS products
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Typical Products
from ECMWF
Andy Morse University of Liverpool MMU Meningitis Lecture
3.2 Potential of Seasonal Forecasting
Dynamic EPS products
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Typical Products
from ECMWF
Probabilistic Seasonal
2 to 4 month lead time
Andy Morse University of Liverpool MMU Meningitis Lecture
3.3 Potential of Seasonal Forecasting
Combined products
International Research
Institute for Climate
Prediction (IRI),
Columbia University, New
York
Seasonal Forecast 2 to 4
month lead time
Andy Morse University of Liverpool MMU Meningitis Lecture
3.4 Potential of Seasonal Forecasting
Dynamic EPS – issues for users
and producers
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Tailored verification
Verification of user parameters
Scale – downscaling
Bias correction
Weighting
Application model and method
development – run with EPS
Product derived time scale cut off –
medium, monthly, seasonal and
beyond
Interdisciplinary nature of research
Taking of academic risk
Andy Morse University of Liverpool MMU Meningitis Lecture
3.5 Potential of Seasonal Forecasting
Product Verification
Met. Office Seasonal Forecast Precip.
AMJ
2 to 4 month lead time
yellow through red - increasing predictive skill
white through dark blue - little or no better than
guesswork
Units = Gerrity skill score
Andy Morse University of Liverpool MMU Meningitis Lecture
3.8 Potential of Seasonal Forecasting
Liverpool Malaria Model – LMM
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Dynamic model
Daily time step
Driven by temperature and precipitation
Observations, reanalysis, ensemble prediction systems
Developed within a probabilistic forecasting system – DEMETER
Continuing in EMSEMBLES
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Model details Hoshen, M.B.and Morse, A.P. (2004) A weather-driven model
of malaria transmission, Malaria Journal, 3:32 (6th September 2004)
doi:10.1186/1475-2875-3-32 (14 pages)
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Applied in an EPS in Morse, A.P., Doblas-Reyes, F., Hoshen, M.B.,
Hagedorn, R. and Palmer, T.N.(2005). A forecast quality assessment of an
end-to-end probabilistic multi-model seasonal forecast system using a
malaria model, Tellus A, 57 (3), 464-475
Andy Morse University of Liverpool MMU Meningitis Lecture
4.0 Summary
The Forecasting Triangle
Providers
Users
Dissemination
Demand
Forecasts
Feedback
Training + Product Guidance and Development
Developers
with users and providers
Andy Morse University of Liverpool MMU Meningitis Lecture
4.1 Summary
• Probabilistic (and deterministic) forecasts are routinely produced
operationally leads times days to seasons
• This potential resource is under utilised by application user communitiesgaps in knowledge and awareness
issues with forecast skill and guidance in products
lack of user application know how and appropriate user application
models
Andy Morse University of Liverpool MMU Meningitis Lecture
4.3 Summary
Current and recent research projects
• DEMETER EU FP5
ENSEMBLES EU FP6
Addressing development and
application of ensemble prediction
systems
• AMMA-EU FP6,
AMMA-UK NERC,
West African monsoon
observations, modelling impacts
Andy Morse University of Liverpool MMU Meningitis Lecture
5.0 Conclusions
Infectious diseases must be modelled to allow use
within emerging long range forecast technologies.
Much has been done to bridge gaps between
forecaster and health user but still many gaps
Work is on going and a new ‘epimeteorology’
community is emerging
Andy Morse University of Liverpool MMU Meningitis Lecture
Websites
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WHO meningitis site http://www.who.int/mediacentre/factsheets/fs141/en/
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Meningitis Research Foundation http://www.meningitis.org/
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EU and NERC funded AMMA improve ability to predict the West African Monsoon
and its impacts on intra-seasonal to decadal timescales. http://www.amma-eu.org/
and http://amma.mediasfrance.org/
EU funded ENSEMBLES probabilistic forecasts of climate variability and climate
change over timescales of seasons to centuries and the application and potential
impacts of these predictions. http://www.ensembles-eu.org/
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Washington, R., Harrison, M, Conway, D., Black, E., Challinor, A., Grimes, D., Jones,
R., Morse, A. and Todd, M (2004). African Climate Report - A report commissioned
by the UK Government to review African climate science, policy and options for
action, DFID/DEFRA, London, December 2004, pp45
http://www.defra.gov.uk/environment/climatechange/ccafrica-study/
Andy Morse University of Liverpool MMU Meningitis Lecture