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Transcript of Meningitis: The climate controls and potential for prediction Andy Morse Ph.D. Department of...
Meningitis:The climate controls
and potential for
prediction
Andy Morse Ph.D.
Department of Geography
University of Liverpool
Andy Morse University of Liverpool MMU Meningitis Lecture
Acknowledgements
Andy Morse University of Liverpool MMU Meningitis Lecture
• 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.
1.0 Background
• 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.
• B and C are more common in the U.K.
• Vaccines exist for A, C, X, Y and W135
Andy Morse University of Liverpool MMU Meningitis Lecture
Meningococcal Meningitis
1.1 Background
• 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
Meningococcal Meningitis
1.2 Background
• 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
Andy Morse University of Liverpool MMU Meningitis Lecture
Meningococcal Meningitis in Africa
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
1.3 Background
• 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
West African Climate
1.4 Background
Andy Morse University of Liverpool MMU Meningitis Lecture
West Africa Atlas
1.5 Background
• Monsoon System and AMMA experiments
Andy Morse University of Liverpool MMU Meningitis Lecture
West African Climate
1.6 Background
Andy Morse University of Liverpool MMU Meningitis Lecture
West African Climate
NDVI February NDVI August
From MARA eshaw website http://www.mara.org.za/eshaw.htm
1.7 Background
Andy Morse University of Liverpool MMU Meningitis Lecture
West African Climate
Animation from University of Liverpool Understanding Epidemics Websitehttp://www.liv.ac.uk/geography/research_projects/epidemics/MAL_intro.htm
Data from CLIVAR VACS Africa Climate Atlas at University of Oxford
1.10 Background
• 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
Andy Morse University of Liverpool MMU Meningitis Lecture
Epidemic Cycles
0
20
40
60
80
100
120
140
97
45
97
48
97
51
98
02
98
06
98
09
98
12
98
15
98
18
98
21
98
24
98
27
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30
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41
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44
98
47
Reporting week
Nu
mb
er o
f ca
ses
Vaccine
Threshold Effect
Andy Morse University of Liverpool MMU Meningitis Lecture
2.0 Linking climate to disease
• Extensive literature search was
undertaken to identify reported
epidemics
• Published and grey literature
were consulted
Andy Morse University of Liverpool MMU Meningitis Lecture
Example for meningitis in AfricaSpatial Distribution Meningitis
Epidemics 1841-1999 (n = c.425) 1
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.
2.1 Linking climate to disease
• Statistical Model to produce a map of risk
• Epidemiological data and climatic and environmental variables
• Risk factors:• Land cover type and seasonal absolute
humidity profile
• Seasonal dust profile, Population density, Soil type
• Significant but not included in final model
• Human factors not included
Andy Morse University of Liverpool MMU Meningitis Lecture
Example for meningitis in Africa
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.
2.2 Linking climate to disease
• Cluster analysis to define areas with common seasonal cycle
• Absolute humidity values
• Used to produce risk map shown above
Andy Morse University of Liverpool MMU Meningitis Lecture
Example for meningitis in Africa
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.
2.4 Linking climate to disease
Andy Morse University of Liverpool MMU Meningitis Lecture
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
Gao, Mali 16.3 N 0.1W Tdew
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
25.0
Oct-95 Feb-96 May-96 Aug-96 Dec-96 Mar-97 Jun-97
Months
T d
ew (
C)
2.5 Linking climate to disease
• Interannual variability in rainfall• Results in interannual variability in seasonal T dew cycles
Andy Morse University of Liverpool MMU Meningitis Lecture
T dew variability
2.6 Linking climate to disease
• Disease is complex and dry air and dust are not the only factors
• Many human ones – immunity, nutrition and co-infection
• However the environmental variables may lead to the population becoming more susceptible
• The environmental variables may be predictable months in advance.
Andy Morse University of Liverpool MMU Meningitis Lecture
Example for meningitis in Africa
3.0 Potential of Seasonal Forecasting
• Probabilistic forecasts are made routinely
• 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)
• Dynamic models – Ensemble Prediction Systems – experimental also operational too
• Loaded dice example – loading and hence predictability changes with time and location
Andy Morse University of Liverpool MMU Meningitis Lecture
Background and applications
3.1 Potential of Seasonal Forecasting
Andy Morse University of Liverpool MMU Meningitis Lecture
Dynamic EPS products
from ECMWF
• Typical Products
3.2 Potential of Seasonal Forecasting
• Typical Products
Andy Morse University of Liverpool MMU Meningitis Lecture
Dynamic EPS products
from ECMWFProbabilistic Seasonal 2 to 4 month lead time
3.3 Potential of Seasonal Forecasting
Andy Morse University of Liverpool MMU Meningitis Lecture
Combined products
International Research Institute for Climate Prediction (IRI), Columbia University, New York
Seasonal Forecast 2 to 4 month lead time
3.4 Potential of Seasonal Forecasting
• 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
Dynamic EPS – issues for users and producers
3.5 Potential of Seasonal Forecasting
Andy Morse University of Liverpool MMU Meningitis Lecture
Product Verification
Met. Office Seasonal Forecast Precip. AMJ
2 to 4 month lead time
yellow through red - increasing predictive skillwhite through dark blue - little or no better than guesswork
Units = Gerrity skill score
3.8 Potential of Seasonal Forecasting
• 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
• 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)
• 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
Liverpool Malaria Model – LMM
4.0 Summary
Andy Morse University of Liverpool MMU Meningitis Lecture
Users
Forecasts Demand
DisseminationDissemination
FeedbackFeedback
Training + Product Guidance and Development
Providers
Developers with users and providers
The Forecasting Triangle
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 communities-
gaps 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
• DEMETER EU FP5ENSEMBLES EU FP6Addressing 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
Current and recent research projects
5.0 Conclusions
Andy Morse University of Liverpool MMU Meningitis Lecture
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
Websites
Andy Morse University of Liverpool MMU Meningitis Lecture
• WHO meningitis site http://www.who.int/mediacentre/factsheets/fs141/en/
• Meningitis Research Foundation http://www.meningitis.org/
• 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/
• 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/