Climate Change Assessment and
impact studies on Health:
a pilot study of CLARIS project
A project within the EC 6th Framework Programme
1 July 2004 to 30 June 2007
Nicolas Degallier
Nicolas.Degallier@ird.fr
Jean-Philippe Boulanger
Jean-Philippe.Boulanger@lodyc.jussieu.fr
http://www.claris-eu.org
LOCEAN, UMR 182, Paris
The CLARIS consortium
Partner
No.
Partner name
Partner
short name
Country
1
Centre National de la Recherche Scientifique
CNRS
France
2
Centre de coopération Internationale en Recherche Agronomique pour le Développement
CIRAD
France
13
Institut de Recherche pour le Développement
IRD
France
14
Max-Planck Gesellschaft Institut
MPI
Germany
7
Istituto Nazionale di Geofisica e Vulcanologia
INGV
Italy
8
Istituto Sperimentale Colture Industriali
ISCI
Italy
9
Universidad de Castilla-La Mancha
UCLM
Spain
11
Plant Research International
PRI
Holland
3
Consejo Nacional de Investigaciones Cientificas y Técnicas
CONICET Argentine
4
Universidad de Buenos Aires
UBA
Argentine
5
Instituto Nacional de Pesquisas Espacias
INPE
Brazil
10
Universidad de la Republica
UR
Uruguay
12
Universidad de Chile
UCH
Chile
CLARIS strategic objectives
3)
theexchange
communication
1)
2) To
To strengthen
set
facilitate
up andthe
favor
the technical
of
between
climate
researchers
andSystem
transfer
observedand
andexpertise
simulated
in climate
Earth
data
stakeholders,
and
to demonstrate
the
and
between
Regional
the climate
Climate
research
Modeling
groups
feasibility
of using
climate
between
and to create
Europe
a South
and
South
American
America
highinformation
in the
the
decision-making
together
quality climate
with
database
providing
for of
studies
a list in
of
process.
climate
extremedata
events
(observed
and long-term
and simulated)
climate
required
trends. for model validations.
CLARIS
Network Coordination Themes and WorkPackages
 NCT1: Project coordination


WP1.1: CLARIS and the European Commission (J-P Boulanger, CNRS)
WP1.2: CLARIS communication and dissemination activities (J.-P. Boulanger, CNRS, and Carlos Ereño,
UBA)
 NCT2: Observing and modelling South American climate at continental scale


WP2.1: Earth System Modelling (Rafael Terra, UR, and Andrea Carril, INGV)
WP2.2: Climate observations and Earth System Simulations (Hervé Le Treut, CNRS, and Roberto
Mechoso, UR)
 NCT3: From continental to regional and local scales


WP3.1: Climate Change Downscaling in the sub-tropical and mid-latitude South America (Manuel Castro,
UCLM, and Claudio Menendez, CONICET)
WP3.2: High-quality regional daily data base for climate trends and extreme event studies (Matilde
Rusticucci, UBA)
 NCT4: From climate to impact studies



WP4.1: Climate and agriculture: a Pilot Action in the Argentinean Pampa Humeda (J.-P. Boulanger, CNRS,
and Olga Penalba, UBA)
WP4.2: Climate and vector-borne epidemics: a Pilot Action on Dengue and Yellow Fever in Brazil
(Nicolas Degallier, IRD)
WP4.3: A Pilot Action on continental-scale air pollution produced by South American mega cities (Guy
Brasseur, MPI, and Carlos Nobre, INPE-CPTEC)
CLARIS WP 4.2: Climate and
vector-borne epidemics
Nicolas Degallier (IRD)
(CNRS, CONICET, INPE, USP, INGV, UR, UCH, MPI)
Create an epidemiological relational database including
high-quality detailed data on Yellow Fever (YF) cases
(human and monkeys) and Dengue human cases in Brazil to
be accessed on the CLARIS web site.
Determine which climate parameters are key ones in
explaining the spatial and temporal distribution of YF and
Dengue cases.
Generate
epidemiological
scenarios
(Dengue
transmission, level of immune status, vector densities and
control activities), according to various climate change
scenarios.
CLARIS WP 4.2 Deliverables
D4.7 (month 12): Internet-based epidemiological database
for YF and Dengue cases in Brazil.
D4.8 (month 24): Report on Dengue-mosquito model
analysis and validation, and YF spatio-temporal analysis of
cases.
D4.9 (month 36): Maps of Dengue risk evolution for
different climate change scenarios and risk assessment indices
for YF vaccination decision-making and Dengue integrated
control
Dengue in Brazil:
data, model, project
Epidemiological
data
N.ј
N.ј.
SINANW
NOME
DATA
SINTOMAS
ENDER E‚O
REGIONAL
EXAME
(SOR./CLIN)
DATA
FECHAM.

136.718
ANA MAR IA ALVES DA SILVA
17/01/2004
COND. RES. CARAVELO CS 28
SOBRAD.
SORO
03/02/2004

136.748
CELI DA ROCHA FREIRES LIMA
15/01/2004
COND. RES. CARVELO CS 27-A
SOBRAD.
SORO
03/02/2004

137.042
CHILON GUIMARМES FIGUEIREDO
26/01/2004
CR 26 CS 14 Р VALE DO AMAN HECER
PLANALTINA
SORO
12/02/2004

137.197
EDSON QUIRINO DA S ILVA
26/02/2004
CR 71 CS 99, VALE DO AM ANHECER
PLANALTINA
SORO
17/03/2004

136.719
ELISANGELA SILVA E SILVA
15/01/2004
COND. RES. CARAVELO CS 28
SOBRAD.
SORO
03/02/2004

137.315
FABIO DIAS ALVES
02/03/2004
QD. 6 CONJ. 62 CS 11, VNSF
PLANALTINA
SORO
19/03/2004

137.044
FRANCISCO JOSѓ D E MORAES
29/01/2004
CR 34 CS 36 Р VALE DO AMAN HECER
PLANALTINA
SORO
12/02/2004

137.316
GRACILENE LACERDA DOS SANTOS
07/03/2004
CR 78 CS 40, VALE DO AM ANHECER
PLANALTINA
SORO
19/03/2004

136.736
IRENE BARBOSA ALVES
07/01/2004
COND. RES. CARAVELO CS 26-A
SOBRAD.
SORO
30/01/2004

137.046
ISABEL DA CONCEI‚МO PEREIRA
28/01/2004
CR 75 CS 42 - VALE DO AMANHECER
PLANALTINA
SORO
13/02/2004

137.019
JEAN JEFERSON ROCHA GUE DES
21/012004
CR 28 CS 10 Р VALE DO AM ANHECER
PLANALTINA
SORO
05/02/2004

136.753
JOМO EUDES A. GUEDES
20/01/2004
CR 28 CS 10 Р VALE DO AMAN HECER
PLANALTINA
SORO
03/02/2004

137.161
JOМO LOPES DA COS TA
27/02/2004
CR 34 CASA 2 0, VALE DO AMANHECER
PLANALTINA
SORO
09/03/2004

137.198
JOМO SIRILO SOBRINHO
02/03/2004
CR 74 CS 03-A, VALE DO AM ANHECER
PLANALTINA
SORO
19/03/2004

137.241
KELMA FERNANDES FREIRE CIPRIANO
23/02/2004
QD. 55 LT 974, ASSENTAMENTO
BRAZLеNDIA
SORO
12/03/2004
Dengue in Brazil:
data, model, project
Entomological data
e Breteau Jours
Predial 1
кndices
jours
cage
cage 2
jours
1,05 de InfestaЌ‹o
Planaltina Candangol‰ndia Taguatinga Guar‡
Sobradinho Gama
Data
3,98
5,01
6,06 1
ip-jan98
0 5,12
0 6,33
1
4,55
7,2
8,99
12,03
ip-fev98
1
2 8,070,963624
1 4,21
0,928571
8,64
4,49
5
6,94
ip-mar98
0,96
1,49
4,76
2,3
3,53
1,87
ip-abr98
10 0,990045
6 1,44
0,984119
1,56
1,35
1,36
3,53
3
ip-mai98
0,95
0,68
0,92
0,82
1,4
2,26
1,1
ip-jun98
13 0,50,971413
13 0,29
0,987647
0,33
0,69
1,36
0,62
ip-jul98
0,9
0,39
0,19
0,42
0,39
0,62
0,4
ip-ago98
15 0,210,953463
28 0,993666
0,29
0,17
0
0,12
0,35
ip-set98
0,52
0,63
0
0,27
0,87
0,75
ip-out98
cage 1
20 2,310,979148
30 0,948683
0,85
1,46
1,34
0,2
1,02
1,6
ip-nov98
cage
2
4,49
2,24
0,46
2,47
3,12
6,67
ip-dez98
24 3,890,970984
38 0,91
0,985385
3,36
3,29
3,28
3,54
ip-jan99
0,8
cage2,47
3
2,88
ip-fev99
29 3,250,973647
41 0,84
0,956466
1,14
1,85
1,92
3,15
3,22
4,42
ip-mar99
1,52
0,61
0,61
1,19
ip-abr99
37
0,980916
42
0,857143
0,75
0,75
0,54
ip-mai99
38 0,833333
48 0,97007
ip-jun99
ip-jul99
0,7
41 0,843433
50 0,66
0,774597
0,32
0,06
0,06
ip-ago99
0,2
0,07
0,73
0,85
0,32
0,71
ip-set99
48 1,020,943722
51 0,91
0,666667
0,41
0,35
0,79
1,05
ip-out99
0,65
ip-nov99
ip-dez99
0,6
ib-jan99
ib-fev990
3,76
3,4
1,94
2,67
5,8
5
10
1
1,64
5,18
2,06
5,28
20
0,9
1
30
3,67
1,85
cage 3
L. Norte
Ceil‰ndia N. Bandeirante Brazl‰ndia L. Sul
0,74
3,88
4,88
5,78
0,74
1,25
2,01
0
0,94
0,54
0,26
0,07
0,1
0
0,11
0
0,11
0,07
0,12
0,14
0,39
0,88
0,74
0,59
0,92
4,53
1,2
0,53
0,57
0,19
3,66
0,46
0,21
0,56
0,99
0,21
0,51
0
0
20
27
28
29
30
34
35
36
37
440,08
490,08
0,32
510,58
1,7
55
40
57
1
0,99778
0,993376
0,904762
0,894737
0,882353
0,945742
0,916667
0,909091
0,9
0,964735
3,12
0,34
0,96964
1,64
2,98
0,816497
1,47
1,2
0,840896
2,2
50
0,707107
0,18
0,26
0,91
2,6
5,13
0,06
0,09
0,46
1,86
0,26
0,92
60
0,5
0,66
1,49
15,05
14,89
0,59
Dengue in Brazil:
data, model, project
Entomological data
Dengue in Brazil:
data, model, project
Mosquito: development
Survival
Gonotrophic cycle
Disease: basic
reproduction
number (R0)
Vectorial capacity
Virus: extrinsic cycle
Infection rate
Other factors: trophic preferences;
Interrupted meals; virus genetics;
mosquito genetics; transovarial
transmission; human behavior
Dengue in Brazil:
data, model, project
Human environment
Eggs,
larvae,
pupae
Human
Reproduction
Viral transmission
Hatching
Epidemic
curves
Adults
Climate
R0
Vectorial capacity
Basic reproduction number
R0
R0 = m a2 b c exp (- / 
= number of secondary cases deriving from one case =
1/viremic
period
Relative density
Mosquito/human
Extrinsic
rate
duration
Number of
meals/day
Probability of
host infection
Probability of
mosquito
infection
Mosquito
mortality
rate /day
Basic reproduction number
R0
R0 = m a2 b c exp (- / 
= number of secondary cases deriving from one case =
1/viremic
period
Relative density of
mosquito/human
Extrinsic
rate
duration
Number of
meals/day
Probability of
host infection
Probability of
mosquito
infection
Mosquito
mortality
rate /day
Dengue in Brazil:
data, model, project
Is Ro a good
estimator of
risk?
Can Ro be
spatialized?
Dengue in Brazil:
data, model, project
Databases
Localized cases
Epidemiological Number, nature & history of cases
Climatological
Measures
Simulations, Scenarios
Entomological
Literature
Measures: PI, BI, RI
Experiments
Geographic information
Risk mapping
Preliminary results
Dengue
transmission
model
RThe
estimation
from
epidemic
curves
Survival
of
the
vector
0
600
35,00
14
Casos observados
cumulated
survival
Casos modelo
S‹o Sebasti‹o,
DF: 2001-2002
Ro
1
500
30,00
12
0,9
10
25,00
400 0,8
model
female
male
0,7
20,00
300 0,6
8
6
0,5
15,00
200 0,4
4
10,000,3
100
0,2
2
5,00
0,1
13/12/04
8/02 1000
13/09/04
13/06/04
13/03/04
29/01
800
13/12/03
13/09/03
19/01
13/06/03
13/03/03
13/12/02
9/01
600
13/09/02
13/06/02
30/12
13/03/02
13/12/01
20/12
400
13/09/01
13/06/01
30/11200 10/12
13/03/01
13/12/00
13/09/00
13/06/00
13/03/00
0
13/12/99
13/09/99
0
0 20/11
13/06/99
0
0,00
Perspectives
Present
To be
situation
done
To
A warmer
fund pluri-disciplinary
(+2°C) and more
european
rainy climate
teams
in southern
to do SA is a
fundamental
real prediction;
and applied
research
The risk ofonexpansion
specific climate-risk
of
projects
epidemics
(Dengue,
of Dengue
malaria,
(and
agriculture,
urban YF) inpollution…)
subtropical regions
of SA is serious (Buenos Aires,
Lima, Salvador…)
Thanks!
CLARIS WP 4.2 Tasks
Task 1: To compare various existing Aedes aegypti - Dengue models.
Task 2: to couple the chosen mosquito-Dengue model to a temporalevolution epidemics model already developed and applied with success
in simulating Dengue epidemics.
Task 3: to diagnose the climate impact on simulated past epidemics
and to suggest a potential evolution of the regions at risk (Dengue) for
different climate change scenarios.
Task 4: to propose to Brazilian Ministry of Health (FUNASA) a risk
index for decision-making during vaccination campaigns (Yellow Fever)
or vector control (Dengue) based on seasonal climate predictions.