1. Meningitis: The climate controls and potential for prediction Andy Morse Ph.D. Department of Geography University of Liverpool A.P.Morse@liv.ac.uk Andy Morse University of Liverpool MMU Meningitis Lecture

2. 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.

3. 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

4. 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

5. 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

6. 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

7. 1.4 Background Andy Morse University of Liverpool MMU Meningitis Lecture West Africa Atlas

8. 1.5 Background Monsoon System and AMMA experiments Andy Morse University of Liverpool MMU Meningitis Lecture West African Climate

9. 1.6 Background Andy Morse University of Liverpool MMU Meningitis Lecture West African Climate NDVI FebruaryNDVI August From MARA eshaw website http://www.mara.org.za/eshaw.htm

10. 1.7 Background Andy Morse University of Liverpool MMU Meningitis Lecture 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

11. 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 Vaccine Threshold Effect

12. Andy Morse University of Liverpool MMU Meningitis Lecture

13. 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 Africa Spatial 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.

14. 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.

15. 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.

16. 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) 4012.514.5 301214 3011.513.6 101113.1

17. 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

18. 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

19. 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

20. 3.1 Potential of Seasonal Forecasting Andy Morse University of Liverpool MMU Meningitis Lecture Dynamic EPS products from ECMWF Typical Products

21. 3.2 Potential of Seasonal Forecasting Typical Products Andy Morse University of Liverpool MMU Meningitis Lecture Dynamic EPS products from ECMWF Probabilistic Seasonal 2 to 4 month lead time

22. 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

23. 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

24. 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 skill white through dark blue - little or no better than guesswork Units = Gerrity skill score

25. 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

26. 4.0 Summary Andy Morse University of Liverpool MMU Meningitis Lecture Users Forecasts Demand DisseminationFeedback Training + Product Guidance and Development Providers Developers with users and providers The Forecasting Triangle

27. 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

28. 4.3 Summary 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 Current and recent research projects

29. 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

30. 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/