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Climate change and Vector born disease

EGU 2010 presentation short Caminade Cyril

Cyril Caminade

on 1 June 2011

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Transcript of Climate change and Vector born disease

Double click anywhere & add an idea Impact of climate change upon vector born diseases in Europe and Africa using ENSEMBLES Regional Climate Models C. Caminade, A. Morse, M. Baylis, H. Guis
University of Liverpool European Geosciences Union General Assembly 2010 Vienna, Austria, 02– 07 May 2010 Pathogen Vector born diseases Diseases transmitted by blood-sucking arthropods Host Vector Climate variability:
Winds... Survival
Replication rate Survival
Development rate
Length of gonotrophic cycle
Vector competence Growth rate
Life habits Ingredients and Method Epidemiological measurements (incidence, number of Infectious mosquitoes...)

Disease Model (statistical or dynamical) driven by climate observations (and other parameters) -> Validation1

Disease Model driven by "current climate" simulations -> Validation2

Disease Model driven by climate scenarios -> Projections and quantification of the uncertainties Climate datasets Climate Observations:

EOBS (gridded dataset at 25km2 resolution over Europe) based on station mesurements (ENSEMBLES)
ERAINTERIM /NCEP /ERA40 Reanalyses Climate Models 25km2 resolution

2 types of experiments
CTL: driven by ERA40 (1961-2000)
SRESA1B: driven by different
GCMs forced by the SRESA1B
scenario (1950-2050)

RCMs list (12 selected) C4IRCA3 (MettEire, Ireland), CNRM-RM4.5 (CNRM, Meteo-France), DMI-HIRAM5 (DMI, Denmark),  ETHZ-CLM (ETHZ, Switzerland), ICTP-RegCM3 (ICTP, Italy), KNMI-RACMO2 (KNMI, Netherlands), METNOHIRAM (MET.NO, POLAND) METO-HC(Met Office, UK), MPI-M-REMO (MPI, Germany), OURANOSMRCC (OURANOS, Canada), SMHIRCA (SMHI, Sweden), UCLM-PROMES (UCLM, Spain). 50km2 resolution

2 types of experiments
SRESA1B: driven by different
GCMs forced by the SRESA1B
scenario (1970-2050)

RCMs list (8 selected) CKNMI-RACMO2.2b (KNMI, Netherlands), METNOHIRAM (MET.NO, POLAND) ), DMI-HIRAM5 (DMI, Denmark), METO-HC(Met Office, UK) ), ICTP-RegCM3 (ICTP, Italy), MPI-M-REMO (MPI, Germany), GKSS-CLM (Canada), SMHIRCA (SMHI, Sweden). http://sites.google.com/site/rt3validation/ Bluetongue over Europe Bluetongue disease or catarrhal fever is a non-contagious, non-zoonotic, insect-borne, viral disease of ruminants, mainly sheep and less frequently cattle, goats, buffalo, deer, dromedaries and antelope. It is caused by the Bluetongue virus and transmitted by midges (Culicoides Imicola spp and Culicoides Obsoletus spp).

BT outbreaks mainly occured in August-September-October Simulated Bluetongue Risk over Europe: recent climate and future Ro=f(T2m, Rain, Vectors) monthly
Vectors=f(T2m, Rain) annual
Host density (sheep and cattle) from the FAO dataset (2005) Realistic High BT risk over Spain, Portugal, south western France, Sardegna and Sicilia.
Imicola spread over Southern France
This misses out observed outbreaks in Corsica.
Unrealistic values over mountains, Northern Italy and Eastern Europe. From Guis et al, 2010 Main results BT model Shading: Ro risk (arbitrary scaled between 0 and 1) Mean Blue tongue risk (EOBS) ASO 1961-2008 From Guis et al, 2010 2006: BT outbreak in France Benelux and Germany captured by EOBS and the CTL exp

SimCTL ensemble close to the BT runs driven by climate observation

Simulated Increasing trend for the future over Northern Europe (SRESA1B)

Large spread among the different RCMs realizations for SRESA1B Blue tongue risk: past and future Simulated Relative Ro BT changes (with respect to 1961-2000) over Northern Europe.

Black: BT risk based on EOBS
Blue: BT risk based on CTL exp
Red: BT risk based on SRESA1B exp

The relative envelope depicts the spread within the RCMs ensemble (1 Stddev) BT risk changes (ASO) 2030-2050 vs 1961-2000 MULTI-MODEL CHANGES:
SIGN CONSISTENCY The BT risk increases over UK, Southern France and North-western Spain (Galicia)

Changes in Northern Europe are related to the pathogen properties

Changes in Southern Europe are associated with the spread of the Afro-Tropical vector (Imicola) Malaria over Africa Malaria alone is responsible for at least one million deaths annually, with 80% of malaria deaths occurring in sub-Saharan Africa (WHO, 2005)

Malaria is caused by a parasite (protozoan Plasmodium) that is carried by female mosquito from the Anopheles spp.

Symptoms: fever, pain, chills and aches; and sometimes nausea and diarrhoea that can lead to more serious health issues.

The mosquitoes’ breeding sites and the lifecycle of the malaria parasite are both strongly connected to climate, especially rainfall and temperature.

Malaria Model: The Liverpool Malaria Model (LMM) dynamic model based on daily rainfall and Temperature (Hoshen and Morse, 2004) Simulated Malaria over West Africa: recent climate and future Mean Annual Malaria Incidence (1990-2007) Mean seasonal cycle 1990-2007 Hovmoeller like diagram:
zonal average between 16W-16E

Shading: Rainfall
Contours: Malaria Incidence Underestimation of the Northern extension of the malaria incidence belt by LMM

2-3 months LAG between Rainfall and Malaria Incidence Mean Incidence changes (SON) 2031-50 vs 1990-2010 Simulated changes in Malaria Incidence (SON) based on the different RCMs

-> common feature: decrease of the Malaria Incidence at the Northern fringe of the Sahel

-> Related to changes in the number of rainy days (and not the seasonal amounts) Shift of the epidemic belt 2031-50 vs 1990-2010 Gray: Location of the epidemic belt 1990-2010

Black dots: Future location of the epidemic belt 2030-2050

The epidemic belt location is defined by the coefficient of variation, namely:

Mean Incidence > 1%
1stddev > 50% of the average Southward shift of the epidemic belt over WA
-> to more populated areas...

Decrease of the mean malaria incidence at the Northern fringe of the Sahel (related to a decrease of the number of rainy days). Conclusions High resolution Climate Observations and RCMs simulations have been employed to model disease risks

Bluetongue risk is increasing over Western Europe for the future. Changes in Southern (Northern) Europe are related to the spread of the vector (changes in the virus properties).

The simulated Malaria epidemic fringe is shifted southward over West Africa for the future

Research projects:
ENHanCE ERA Net Health and Climate in Europe
QWeCI EU funded FP7 (Theme6) Project: Quantifying Weather
and Climate Impacts on Health in Developing Countries

Uncertainties: non-climatic parameters & RCMs biases & emission scenarios ENSEMBLES Regional Climate Simulations: Europe ENSEMBLES / AMMA Regional Climate Simulations: Africa Acknowledgments:
ENSEMBLES RT3 Acknowledgments:
ENSEMBLES RT3 From Guis et al, 2010 Extra-slides
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