Climate Change Assessment andimpact studies on Health:

a pilot study of CLARIS project

A project within the EC 6th Framework Programme1 July 2004 to 30 June 2007

Nicolas Degallier

Nicolas.Degallier@ird.fr

Jean-Philippe BoulangerJean-Philippe.Boulanger@lodyc.jussieu.fr

http://www.claris-eu.orgLOCEAN, 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

1) To set up and favor the technical transfer and expertise in Earth System and Regional Climate Modeling between Europe and South America together with the providing of a list of climate data (observed and simulated) required for model validations.

3) To strengthen the communication between climate researchers and stakeholders, and to demonstrate the feasibility of using climate information in the decision-making process.

2) To facilitate the exchange of observed and simulated climate data between the climate research groups and to create a South American high-quality climate database for studies in extreme events and long-term climate trends.

NCT1: Project coordinationWP1.1: 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 scalesWP3.1: 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 studiesWP4.1: 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 Network Coordination Themes and WorkPackages

CLARIS WP 4.2: Climate andvector-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:Dengue in Brazil:datadata, model, project, model, project

Epidemiological Epidemiological datadata

N.º N.º.SINANW

NOME DATASINTOMAS

ENDEREÇO REGIONAL EXAME(SOR./CLIN)

DATAFECHAM.

• 136.718 AN A MARIA ALVE S DA SILVA 17/01 /2004 COND. RES . CARAVE LO CS28 SOBRAD. SORO 03/02 /2004

• 136.748 CELI D A ROC HA FREIRES LIMA 15/01 /2004 COND. RES . CARVEL 2O CS 7-A SOBRAD. SORO 03/02 /2004

• 137.042 CHIL ON GUIMARÃE S FIGUEIREDO 26/01 /2004 CR 26 CS 14 – VA LE DOAMANHECER PLANALT INA SORO 12/02 /2004

• 137.197 ED SON QUIRIN O DA SILVA 26/02 /2004 CR 71 CS 99, VAL E DO AMANHECER PLANALT INA SORO 17/03 /2004

• 136.719 ELISANGELA SIL VA E SILVA 15/01 /2004 COND. RES . CARAVE LO CS 28 SOBRAD. SORO 03/02 /2004

• 137.315 FABIO DIAS ALVES 02/03 /2004 QD. 6 CONJ . 62 C 1S 1, VNSF PLANALT INA SORO 19/03 /2004

• 137.044 FRANCIS CO JO SÉ DE MORAES 29/01 /2004 CR 34 CS 36 – VA LE DOAMANHECER PLANALT INA SORO 12/02 /2004

• 137.316 GRACILE NE LACER DA DOS SANTOS 07/03 /2004 CR 78 CS 40, VAL E DO AMANHECER PLANALT INA SORO 19/03 /2004

• 136.736 IREN E BARBOSA ALVES 07/01 /2004 COND. RES . CARAVE LO CS26-A SOBRAD. SORO 30/01 /2004

• 137.046 ISAB EL DA CONCEIÇÃO PEREIRA 28/01 /2004 CR 75 CS 42 - VALE DO AMANHECER PLANALT INA SORO 13/02 /2004

• 137.019 J E AN J EFERSO N ROCH A GUEDES 21/012004 CR 28 CS 10 – VA LE DO AMANHECER PLANALT INA SORO 05/02 /2004

• 136.753 J O ÃO EUDES A. GUEDES 20/01 /2004 CR 28 CS 10 – VA LE DOAMANHECER PLANALT INA SORO 03/02 /2004

• 137.161 J O ÃO LOPES D A COSTA 27/02 /2004 CR 34 CAS 2A 0, VA LE DO AMANHECER PLANALT INA SORO 09/03 /2004

• 137.198 J O ÃO SIRILO SOBRINHO 02/03 /2004 CR 74 CS 03-A, VAL E DO AMANHECER PLANALT INA SORO 19/03 /2004

• 137.241 KELMA FERNANDE S FREI RE CIPRIANO 23/02 /2004 QD. 55 9LT 74, ASSENTAMENTO BRAZLÂNDIA SORO 12/03 /2004

jours cage 1 Jours cage 2 jours cage 30 1 0 1 0 12 0,963624 1 0,928571 20 0,99778

10 0,990045 6 0,984119 27 0,99337613 0,971413 13 0,987647 28 0,90476215 0,953463 28 0,993666 29 0,89473720 0,979148 30 0,948683 30 0,88235324 0,970984 38 0,985385 34 0,94574229 0,973647 41 0,956466 35 0,91666737 0,980916 42 0,857143 36 0,90909138 0,833333 48 0,97007 37 0,941 0,843433 50 0,774597 44 0,96473548 0,943722 51 0,666667 49 0,96964

51 0,81649755 0,84089657 0,707107

0,6

0,65

0,7

0,75

0,8

0,85

0,9

0,95

1

1,05

0 10 20 30 40 50 60

cage 1

cage 2

cage 3

Dengue in Brazil:Dengue in Brazil:datadata, model, project, model, project

Entomological dataEntomological dataÍndices de Infestação Predial e BreteauData Sobradinho Gama Planaltina Candangolândia Taguatinga Guará Ceilândia N. Bandeirante Brazlândia L. Sul L. Norteip-jan98 5,12 6,06 6,33 5,01 3,98 3,88 0,74ip-fev98 12,03 8,99 7,2 4,55 5,78 4,88ip-mar98 8,07 6,94 5 4,21 4,49 8,64 1,25 0,74ip-abr98 1,87 3,53 2,3 4,76 1,49 0,96 2,01ip-mai98 3 3,53 1,36 1,44 1,35 1,56 0,94 0ip-jun98 1,1 2,26 1,4 0,82 0,92 0,68 0,26 0,54ip-jul98 0,5 0,62 1,36 0,29 0,69 0,33 0,1 0,07ip-ago98 0,4 0,62 0,39 0,42 0,19 0,39 0,11 0ip-set98 0,21 0,35 0,12 0 0,17 0,29 0,11 0ip-out98 0,75 0,87 0,27 0 0,63 0,52 0,12 0,07ip-nov98 2,31 1,6 1,02 0,2 1,34 1,46 0,39 0,14ip-dez98 6,67 3,12 2,47 0,46 2,24 4,49 0,74 0,88ip-jan99 3,89 3,54 3,28 0,91 3,29 3,36 0,53 1,2 4,53 0,92 0,59ip-fev99 3,25 2,88 2,47 0,84 0,21 0,46 3,66 0,19 0,57ip-mar99 4,42 3,22 3,15 1,92 1,85 1,14 0,99 0,56ip-abr99 1,19 0,61 0,61 1,52 0,51 0,21ip-mai99 0,54 0,75 0ip-jun99ip-jul99ip-ago99 0,06 0,66 0,06 0,32 0,08 3,12 0,18 0,06 0,5ip-set99 0,71 0,32 0,85 0,73 0,07 0,2 0,08 0,34 0,26 0,09 0,66ip-out99 1,02 1,05 0,79 0,91 0,35 0,41 0,32 1,64 0,91 0,46 1,49ip-nov99 3,76 1,94 2,06 5,18 1 3,67 0,58 2,98 2,6 1,86 15,05ip-dez99 3,4 2,67 5,28 1,64 1,85 1,7 1,47 5,13 0,26 14,89ib-jan99 5,8 0,9 1,2 0,92 0,59ib-fev99 5 1 2,2

Dengue in Brazil:Dengue in Brazil:datadata, model, project, model, project

Entomological dataEntomological data

Mosquito: developmentMosquito: developmentSurvivalSurvivalGonotrophic cycle Gonotrophic cycle

Virus: extrinsic cycleVirus: extrinsic cycleInfection rateInfection rate

Vectorial capacityVectorial capacity

Disease: basic Disease: basic reproduction reproduction number (Rnumber (R00))

Other factors: trophic preferences;Other factors: trophic preferences;Interrupted meals; virus genetics; Interrupted meals; virus genetics; mosquito genetics; transovarial mosquito genetics; transovarial transmission; human behaviortransmission; human behavior

Dengue in Brazil:Dengue in Brazil:data, data, modelmodel, project, project

Adults

Eggs, larvae, pupae

Climate

Human environment

R0

Vectorial capacity

Human

Epidemiccurves

Reproduction

HatchingViral transmission

Dengue in Brazil:Dengue in Brazil:data, data, modelmodel, project, project

R0 = m a2 b c exp (- /

Basic reproduction number= number of secondary cases deriving from one case = R0

Relative densityMosquito/human

Number of meals/day

Probability of host infection

Probability of mosquito infection

Mosquito mortality rate /day

Extrinsic rate duration

1/viremic period

R0 = m a2 b c exp (- /

Basic reproduction number= number of secondary cases deriving from one case = R0

Relative density of mosquito/human

Number of meals/day

Probability of host infection

Probability of mosquito infection

Mosquito mortality rate /day

Extrinsic rate duration

1/viremic period

Can Ro be spatialized?

Is Ro a good estimator of

risk?

Dengue in Brazil:Dengue in Brazil:data, data, modelmodel, project, project

ClimatologicalClimatological

EntomologicalEntomological

Geographic informationGeographic information

Number, nature & history of cases Number, nature & history of cases Localized casesLocalized cases

MeasuresMeasuresSimulations, ScenariosSimulations, Scenarios

LiteratureLiteratureMeasures: PI, BI, RIMeasures: PI, BI, RI

ExperimentsExperiments

Risk mappingRisk mapping

Dengue in Brazil:Dengue in Brazil:data, model, data, model, projectproject

EpidemiologicalEpidemiological

DatabasesDatabases

The Dengue transmission modelThe Dengue transmission modelPreliminary resultsPreliminary results

RR00 estimation from epidemic curves estimation from epidemic curvesSurvival of the vectorSurvival of the vectorcumulated survival

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

20/11 30/11 10/12 20/12 30/12 9/01 19/01 29/01 8/02

modelfemalemale

0

100

200

300

400

500

600

13/06/9913/09/9913/12/9913/03/0013/06/0013/09/0013/12/0013/03/0113/06/0113/09/0113/12/0113/03/0213/06/0213/09/0213/12/0213/03/0313/06/0313/09/0313/12/0313/03/0413/06/0413/09/0413/12/04

0

2

4

6

8

10

12

14Casos observadosCasos modeloRo

São Sebastião, DF: 2001-2002

0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

0 200 400 600 800 1000

To be To be donedone

To fund pluri-disciplinary To fund pluri-disciplinary european teams to do european teams to do fundamental and applied fundamental and applied research on specific climate-risk research on specific climate-risk projects (Dengue, malaria, projects (Dengue, malaria, agriculture, pollution…)agriculture, pollution…)

PerspectivesPerspectives

A warmer (+2°C) and more A warmer (+2°C) and more rainy climate in southern SA is a rainy climate in southern SA is a real prediction;real prediction;The risk of expansion of The risk of expansion of epidemics of Dengue (and epidemics of Dengue (and urban YF) in subtropical regions urban YF) in subtropical regions of SA is serious (Buenos Aires, of SA is serious (Buenos Aires, Lima, Salvador…)Lima, Salvador…)

Present Present situationsituation

Thanks!Thanks!

CLARIS WP 4.2 Tasks

Task 1: o compare various existing Aedes aegypti - Dengue models.

Task 2: to couple the chosen mosquito-Dengue model to a temporal-evolution 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.