Meningitis:

1

• Meningitis
• The role of climate for prediction
• Andy Morse Ph.D.
• Department of Geography
• University of Liverpool
• A.P.Morse_at_liv.ac.uk

Mark Cresswell Ph.D EGS Manchester Metropolitan
University
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1.0 Background
Meningococcal Meningitis

• 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

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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

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1.2 Background
Meningococcal Meningitis in Africa

• 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

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1.3 Background
West African Climate

• 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

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1.4 Background
West Africa Atlas

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1.5 Background
West African Climate

• Monsoon System and AMMA experiments

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1.6 Background
West African Climate
NDVI February
NDVI August
From MARA eshaw website http//www.mara.org.za/esh
aw.htm

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1.7 Background
Look at Hutchinson rainfall climate maps in unit
folder
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

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1.10 Background
Epidemic Cycles

• 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

Vaccine
Effect
Threshold

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2.0 Linking climate to disease
Spatial Distribution Meningitis Epidemics
1841-1999 (n c.425) 1
Example for meningitis in Africa

• Extensive literature search was undertaken to
  identify reported epidemics
• 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.

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2.1 Linking climate to disease
Example for meningitis in Africa

• 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

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.

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2.2 Linking climate to disease
Example for meningitis in Africa

• Cluster analysis to define areas with common
  seasonal cycle
• Absolute humidity values
• 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.

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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

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2.6 Linking climate to disease
Example for meningitis in Africa

• 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.

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3.0 Potential of Seasonal Forecasting
Background and applications

• 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

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3.1 Potential of Seasonal Forecasting
Dynamic EPS products

• Typical Products

from ECMWF

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3.2 Potential of Seasonal Forecasting
Dynamic EPS products

• Typical Products

from ECMWF Probabilistic Seasonal 2 to 4 month
lead time

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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

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3.4 Potential of Seasonal Forecasting
Dynamic EPS issues for users and producers

• 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

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3.5 Potential of Seasonal Forecasting
Product Verification
yellow through red - increasing predictive
skillwhite through dark blue - little or no
better than guesswork Units Gerrity skill
score
Met. Office Seasonal Forecast Precip. AMJ 2 to
4 month lead time

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3.8 Potential of Seasonal Forecasting
Liverpool Malaria Model LMM

• 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, 332 (6th September 2004)
  doi10.1186/1475-2875-3-32 (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

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4.0 Summary
The Forecasting Triangle
Providers
Users
Dissemination Feedback
Forecasts
Demand

Training Product Guidance and Development
Developers with users and providers

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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

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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

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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

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Websites

• WHO meningitis site http//www.who.int/mediacent
  re/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.mediasfran
  ce.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/cli
  matechange/ccafrica-study/