Meningitis: The climate controls and potential for prediction Andy Morse Ph.D. Department of Geography University of Liverpool [email protected] Andy.

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.

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


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


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


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


West African Climate

1.4 Background


West Africa Atlas

1.5 Background

• Monsoon System and AMMA experiments



1.6 Background



NDVI February NDVI August

From MARA eshaw website http://www.mara.org.za/eshaw.htm

1.7 Background



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


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

98

30

98

33

98

41

98

44

98

47

Reporting week

Nu

mb

er o

f ca

ses

Vaccine

Threshold Effect


2.0 Linking climate to disease

• Extensive literature search was

undertaken to identify reported

epidemics

• Published and grey literature

were consulted


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.


• 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


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.


• 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.



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)


• Interannual variability in rainfall• Results in interannual variability in seasonal T dew cycles


T dew variability


• 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.



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


Background and applications



Dynamic EPS products

from ECMWF

• Typical Products


• Typical Products


Dynamic EPS products

from ECMWFProbabilistic Seasonal 2 to 4 month lead time



Combined products

International Research Institute for Climate Prediction (IRI), Columbia University, New York

Seasonal Forecast 2 to 4 month lead time


• 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


Dynamic EPS – issues for users and producers



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


• 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


Liverpool Malaria Model – LMM

4.0 Summary


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


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


Current and recent research projects

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

Websites


• 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/

Meningitis: The climate controls and potential for prediction Andy Morse Ph.D. Department of Geography University of Liverpool [email protected] Andy.

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