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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
5
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
Spatio-temporal analysis of the climate to different scales 2/3
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
12
Seasonal forecasting
Spatio-temporal analysis of the climate to different scales 2/3
13
Seasonal forecasting
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-03: mai-sep/5 mois
False Alarm Rate
Hit
Rat
e
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-04: mai-oct/6 mois
False Alarm Rate
Hit
Rat
e
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-05: jun-oct/5 mois
False Alarm Rate
Hit
Rat
e
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-06: jul-oct/4 mois
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
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-03: mai-sep/5 mois
False Alarm Rate
Hit
Rat
e
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-04: mai-oct/6 mois
False Alarm Rate
Hit
Rat
e
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-05: jun-oct/5 mois
False Alarm Rate
Hit
Rat
e
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-06: jul-oct/4 mois
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
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-03: mai-sep/5 mois
False Alarm Rate
Hit
Rat
e
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-04: mai-oct/6 mois
False Alarm Rate
Hit
Rat
e
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-05: jun-oct/5 mois
False Alarm Rate
Hit
Rat
e
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-06: jul-oct/4 mois
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
Spatio-temporal analysis of the climate to different scales 2/3
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-03: mai-sep/5 mois
False Alarm Rate
Hit R
ate
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-04: mai-oct/6 mois
False Alarm Rate
Hit R
ate
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-05: jun-oct/5 mois
False Alarm Rate
Hit R
ate
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
init-06: jul-oct/4 mois
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
Spatio-temporal analysis of the climate to different scales 3/3
« 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