ΔVBD/ΔCWC. Andy Morse, University of Liverpool Prashant Goswami, C-MMACS, Bangalore. plus others

Transcription

1 Changes in distribution of vector and water borne diseases due to CWC ΔVBD/ΔCWC Andy Morse, University of Liverpool Prashant Goswami, C-MMACS, Bangalore plus others

2 Changes in distribution of vector and water borne diseases due to CWC Key scientific issue gaps, mutual interest, contacts and existing knowledge Interesting projects might benefit UK-India work Type of project that might be needed (training, exchanges, facilities)

3 Changes in distribution of vector and water borne diseases due to CWC Key scientific issue gaps, mutual interest, contacts and existing knowledge PEK - Dynamic VBD model (malaria LMM), multiple climate model simulations (C-MMACS), downscaling algorithm, station data targets (14 over India), debiasing, ENSEMBLES and other archives, risk change Gaps India parameter values LMM, validation data (clinical), public health contacts Mutual Interest malaria test bed for other VBDs (emerging) e.g. dengue, chik, horizon scanning current climate for potential emergence in UK of current endemic/epidemic disease in India, also Africa to India, common development of generic VBD models, zoonotics Contacts ICT mosquito, NERC and EC FP7 funded projects, Oxfam, CAFOD etc.

4 Changes in distribution of vector and water borne diseases due to CWC Interesting projects - might benefit UK-India work malaria risk maps - current and future over India including population dynamics generic VBD model climate diagnostics for disease uncertainty bounds emergence of disease in future climates India/UK large cluster run very high resolution, parameter perturbation

5 Changes in distribution of vector and water borne diseases due to CWC Type of project that might be needed (training, exchanges, facilities) Organised pilot activity Small NoE up to 3 India 3 UK max Exchange of Investigators and Research staff in KE Contact with Indian malaria community?? Indian Govenment Ministries. Disk space, data transfers, data licensing (operational archives) Role of RCUK and High Commission involvement of stakeholders, NGOs

6 Mapping Malaria Risk over India: Preliminary results and African Examples Workshop Delhi, India Thanks to Cyril Caminade and Anne Jones

7 Rainfall JJA / Malaria Incidence SON Left: Mean Summer (JJA) rainfall (mm/day) climatology over the period Right: Mean autumn (SON) Simulated Malaria Incidence (%) climatology over the period > Higher Risk

8 Rainfall SON / Malaria Incidence DJF Left: Mean Autumn (SON) rainfall (mm/day) climatology over the period Right: Mean Winter (DJF) Simulated Malaria Incidence (%) climatology over the period > Moderate Risk / High risk near the coasts

9 Rainfall / Malaria Incidence Annual Left: Mean Autumn (SON) rainfall (mm/day) climatology over the period Right: Mean Winter (DJF) Simulated Malaria Incidence (%) climatology over the period > Moderate Risk / High risk near the coasts

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11 Rainfall / Malaria Seasonal cycle (Land) Shading: Mean Rainfall seasonal cycle (mm/day) averaged between 65 E and 98 E (land grid points) over the period (ERAINTERIM). Contours: Mean Malaria Incidence seasonal cycle (%) averaged between 65 E and 98 E (land grid points) over the period (ERAINTERIM). 3 month lag

12 Skill in epidemic zones in West Africa FP6 ENSEMBLES Seasonal EPS May 4-6 (ASO) upper tercile epidemic transmission zone ROCSS Jones and Morse (2010) in prep

13 Cost-loss assessment (DEMETER) Theoretical cost/loss versus potential economic value, V Action taken Yes Event occurs No Yes C C No L 0 DEMETER (ROCA=0.67) ERA-40 (ROCA=0.88) V E E c lim c lim E E forecast perfect For probability forecasts, choose decision threshold to maximise value V Cheap to take action (always act) Malaria forecasts for above upper tercile malaria, Botswana, November forecast months 4-6 (FMA), compared to observed anomalies from published index. Expensive to take action (never act) from Jones and Morse, Journal of Climate (in second review)

14 Malaria climate modelling: Future projections LMM driven by the ENSEMBLES RCMs Mean changes in malaria Incidence (%) with respect to the climatology for the season SON, based on the SRESA1B experiments performed within ENSEMBLES RT3. -> decrease in prevalence at the Northern fringe of the Sahel

15 Comments on India LMM Malaria Incidence driven by the rainfall conditions occurring 2-3 months before Lower risk in spring, higher risk in autumn, The north-eastern part of India (and especially the coasts) shows high simulated malaria incidence based on climate indicators. ERAINTERIM closer to CRU, but NCEP and ERAINTERIM runs provide similar results

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17 Extra Slides

18 Rainfall DJF / Malaria Incidence MAM Left: Mean Winter (DJF) rainfall (mm/day) climatology over the period Right: Mean Spring (MAM) Simulated Malaria Incidence (%) climatology over the period Lower/No Risk

19 Rainfall MAM / Malaria Incidence JJA Left: Mean Spring (MAM) rainfall (mm/day) climatology over the period Right: Mean Summer (JJA) Simulated Malaria Incidence (%) climatology over the period > Low/Moderate Risk

20 Rainfall / Malaria Seasonal cycle (All) Shading: Mean Rainfall seasonal cycle (mm/day) averaged between 65 E and 98 E (all grid points) over the period (ERAINTERIM). Contours: Mean Malaria Incidence seasonal cycle (%) averaged between 65 E and 98 E (all grid points) over the period (ERAINTERIM). 3 months lag

21 Few tracks (2/2) Information and maps I ve found on the web (You can use 1 to compair with the LMM outputs): This one no idea what the scale is: LMM seems to do a good job at least in terms of Hot spots: North east / North eastern coasts of India are shown to be hot spots as expected...