Vignolles c 20150708_1730_upmc_jussieu_-_room_105

IMPACTS OF CLIMATIC FACTORS ON THE PRODUCTION OF MALARIA

VECTORS IN THE RURAL SAHEL:

APPLICATION TO THE NOUNA REGION (BURKINA-FASO)

The PALUCLIM Project

Cécile Vignolles

Our Common Future Under Climate Change

International Scientific conference

7-10 et July 2015

Paris

Needs for public health actors to adapt their management policy of the human health and

interest to use new tools of risk prediction

Importance of climatic and environmental factors in the outbreak of certain epidemics by

boosting the dynamics of transmission and dissemination of vectors

Difficulty to implement targeted control measures because of the lack of knowledge of the risk

at local scale – when and where?

Rationale on malaria

In Burkina Faso

• Malaria: main cause of deaths and

hospitalizations

• Population at risk : 15 millions

• In 2007, 5.4 million cases and ~12000 deaths

(mortality rate ~2%)

• Endemic disease but linked to the

distribution of rainfall events

Main infectious disease caused by a Plasmodium

parasite, transmitted through the bites of mosquitoes

of the genus Anopheles

• 50% of the world population is exposed

• ~200 millions cases per year (WHO, 2014)

• ~630 000 deaths per year (WHO, 2014)

Major public health concern

Major cause of poverty

Significant barrier to economic development in

developing countries

Need for predicting areas of exposure of humans to the vectors of malaria to implement effective strategies of surveillance and control

2

3

Provide and validate dynamic entomological risk maps at local scale

(village) based on the tele-epidemiology concept developed bu CNES and ist

partners - Mapping of breeding sites and their productivity

Study the malaria risks as a function of the weather/climate spatio-

temporal variability (seasonal, low frequencies, and climate change)

Examine the effectiveness of larval control strategies as adaptation to the

risk of larval productivity

Objectives

4

1 - Improving access to healthcare

Treating patients at remote and mobile sites

2 - Environment / Climate / Health

Monitor, predict and prevent epidemics

3 - Crisis Management

Better management of major humanitarian crises

4 - Education and Training

Improving healthcare and learning thanks to Space

Telehealth Space technology for health

Telehealth activities

Tele-epidemiology consists in studying human and animal

diseases (transmitted by water, air or vectors) which are closely

linked to climate and environment, by using space technology

The French Space Agency (CNES) has thus developed, with its

partners, a concept based on a deterministic/statistical approach of

the climate-environment-health relationships and on an adapted

space offer

Provide to public health actors additional tools/services

helping them in diseases surveillance and in the implementation of

strategies to diseases control

1- Experimental design mainly field studies

• Observing strategy: monitoring and assembling multidisciplinary in-situ datasets

• Diagnostic: extract and identify the main physical and biological mechanisms at stake

2- Obtaining well adapted products from Space • Remote-sensing monitoring of environment, linking epidemics with confounding factors

• Remote-sensing from space: use of products, fully adapted to the various spatio-temporal scales of variability

3- Dedicating modeling for risk mapping •Built predictive models by combining in-situ data and remote sensing product derived from Earth Observation

satellites, geographic data and meteorological data to produce dynamic high spatio-temporal resolution

environmental risk mapping

The tele-epidemiology conceptual approach

Environment Climate Entomology

Veterinary Social Sciences

Microbiology

Multidisciplinary approach based upon the study of the key mechanisms favoring

emergence and propagation of infectious diseases linking disciplines

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6

The study area : the Nouna District (Burkina Faso)

Sahelian Climate

~800 mm Rainfall during ~4 months (June-September)

Burkina Faso & Nouna District

Nouna District

7

Analysis of the impact of climate using an impact model :

• Craig et al, 1999, modified by Tanser et al. 2003 (MARA project), Ermert et al, 2011

• From climatic conditions to malaria risk indices

Climate analyses done for different temporal scales

• Seasonal

• Low-frequencies

• Climate change

Contribution DGClim +GAME/CNRM

Study of the risk based on the spatio-temporal variability of the climate

Christian Viel

8

Climatology of the malaria risk in the Nouna distrcit

05

01

00

15

02

00

pre

cip

en m

mR

H e

n %

jan feb mar apr may jun jul aug sep oct nov dec

20

25

30

35

40

tem

p e

n °

CM

inM

ax

Very favorable precipitations

Very favorable temperature

22°C

32°C

18°C

40°C

possible

possible

possible

unfavorable

unfavorable

unfavorable

80 mm

60 mm

Data used:

ACR2 for rainfall (African Rainfall

Climatology version 2)

ERA-interim for T° & HR

Favorable period to the emergence of

malaria

Assessment of the impact of the climatic conditions on malaria risk based on an impact model

Adaptation of Craig model Calculation of indices (IND) of favorable malaria conditions

for rainfall, temperature and relative humidity

NOUNA Climatology 1983-2011. Monthly total PPN (black), mean RH (orange). Temp. Max/Min (red/blue)

Malaria Conditions from climatological Mapping

9

months in white are for no favorable

conditions

For PPN months with black X are for total rainfall >80 mm,

whilst red X are for the first month with rainfall >60 mm

Years in red are for unfavorable

conditions

T et HR always favorable

P limiting factor

Climatology of the malaria risk in the Nouna distrcit

Precipitation

10

Spatio-temporal analysis of the climate to different scales 1/3

Natural climate variations of precipitation: impact of AMO (Atlantic Multi-decadal Oscillation)

0,0

1,0

2,0

3,0

4,0

5,0

6,0

7,0

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

vale

ur

de l'in

dic

e

AMO - AMO +

AMO- reduction of rainfall in the Sahel (including the Nouna area), less favorable conditions for the development of malaria

AMO+ increase in rainfall in the Sahel (including the Nouna area), more favorable conditions for the development of malaria

AMO -

AMO +

« p

reci

pit

atio

ns

» In

dex

Precipitation

from a PPN cumulative monthly threshold >80 mm and

from AMO phases:

AMO<0 1883-1995, AMO>0 1996-2011

% d

e p

révi. c

orr

ecte

s

jan feb mar apr may jun jul aug sep oct nov dec

50

60

70

80

90

10

0

Monthly probability of having PPN threshold >80mm % of Correct forecast for 1983-2011 (red)

% Correct forecast following AMO phases (orange)

0.0

0

.4

0.8

pro

b

jan feb mar apr mai jun jul aug sep oct nov dec

amo - amo +

11

Seasonal forecasting

Probability for Rainfall >80 mm during a given month using: • The climatology • The climatology following the AMO phases • The seasonal forecasting using the ARPEGE model from Météo-France


Seasonal Forecast using ARPEGE (v.3) coupled model

• Initial conditions from ERA 40

• 7-month post-initialization

• Atmospheric conditions from ECMWF: 41 members post-2007

• Oceanic Re-analysis from Mercator Oceans: 11 members post-2007

• Use of 4 grid-points around NOUNA

• Use of AMO phases and NAO index (from NOAA)

• 6 models tested depending upon combination of: month, AMO,NAO

Probability for Rainfall >80 mm during a given month

Initialization dates Forecasting periods

March April to october 1983,………,April to october2011

Avpril May to october 1983,………….., May to october 2011

May June to october 1983,………….., June to october 2011

June July to october 1983,………..., July to october 2011

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0.0 0.2 0.4 0.6 0.8 1.0

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1.0

init-03: mai-sep/5 mois

False Alarm Rate

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Rat

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init-04: mai-oct/6 mois

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init-06: jul-oct/4 mois

False Alarm Rate

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Rat

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clim_amo

clim

simu.amo.month

simu.amo.nao.month

simu.nao.month

simu.amo.nao

simu.nao

simu.amo

0.0 0.2 0.4 0.6 0.8 1.0

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False Alarm Rate

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False Alarm Rate

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False Alarm Rate

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clim_amo

clim

simu.amo.month

simu.amo.nao.month

simu.nao.month

simu.amo.nao

simu.nao

simu.amo

0.0 0.2 0.4 0.6 0.8 1.0

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False Alarm Rate

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Rat

e

0.0 0.2 0.4 0.6 0.8 1.0

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False Alarm Rate

Hit

Rat

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0.0 0.2 0.4 0.6 0.8 1.0

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False Alarm Rate

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Rat

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False Alarm Rate

Hit

Rat

e

clim_amo

clim

simu.amo.month

simu.amo.nao.month

simu.nao.month

simu.amo.nao

simu.nao

simu.amo

Forecast using only the climatology

Forecast using the climatology following the AMO phases

Forecast using the ARPEGE model in different configurations

The easiest and most efficient approach to forecast the risk

of precipitation

No improvement when compared to

seasonal forecast using AMO phases


0.0 0.2 0.4 0.6 0.8 1.0

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False Alarm Rate

Hit R

ate

0.0 0.2 0.4 0.6 0.8 1.0

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False Alarm Rate

Hit R

ate

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False Alarm Rate

Hit R

ate

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False Alarm Rate

Hit R

ate

clim_amo

clim

simu.amo.month

simu.amo.nao.month

simu.nao.month

simu.amo.nao

simu.nao

simu.amo

14

Future climate vulnerability in the Nouna region to malaria

Future climate simulation exercise

at the global level CMIP-5

Multi-models approach Resolution ~2.5° • CCCMA-CanESM2 • CNRM-CM5 • Hadgem2-ES • INM-CM4 • IPSL-CM5A-LR • IPSL-CM5A-MR • MIROC5 • NCC

« Temperatures » Index

« Precipitations » Index

rcp 8.5 rcp 8.5

rcp 4.5 rcp 4.5

According to the model of Craig, the temperature rise would become a

limiting factor for the malaria risk


« Precipitations » Index

« Temperatures » Index

Scenarios from IPCC Representative Concentration Pathways (RCP) 45 and 85:

• RCP45= Radiative forcing of 4.5W/m2 with [CO2] stabilized at 660 ppm in 2100

• RCP85= Radiative forcing of 8.5 W/m2 with [CO2] increasing to 1370 ppm in 2100

Re-Emergent Diseases & Global Environment Monitoring from Space

An Innovant and Multidisciplinary Health Information System

Objectives : Highlight linkages between Climate, Environment and Public Health, using Space Data

Geography

http://RedGems.eu

The RedGems Information System

15

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