Transcript abdelkrim-ben-mohamed-universit - World Conference on Climate

WORLD CONFERENCE ON CLIMATE CHANGE
October 24-26, 2016 VALENCIA, SPAIN
Impacts of Climate Change on Meningitis
Epidemics in the Western African Sahel:
enhanced threats or part of the solution?
by
A. Ben Mohamed, A. Kelani, E.O. Adehossi,
and M. C. Thomson1
Université Abdou Moumouni, Niamey, NIGER
1IRI, Columbia University, New-York, USA
On the aspects, other than climate, intervening in
the Cerebrospinal Meningitis Epidemics (CSME)
AGENT:
• Neisseria meningitidis (the meningococcus)
• Only form of bacterial meningitis to cause epidemics
• Groups A and C most commonly associated with
epidemics
RESERVOIR:
• Asymptomatic carriage in nasopharyngeal mucosa
SPREAD:
• Direct contact with infected people (respiratory
droplets)
• Most cases acquired through exposure to carriers

ASSOCIATED RISK FACTORS
IMMUNOLOGICAL SUSCEPTIBILITY
• Susceptibility to virulent strain
DEMOGRAPHIC PROCESSES
• Travel and migration
• Population displacement
SOCIO-ECONOMIC CONDITIONS
• Poor living conditions
• Overcrowding
 THE PUBLIC HEALTH PROBLEM
• Morbidity is enormous and case fatality is high
• Consequences to individuals and communities
are significant
•
Vaccines are difficult to procure
Concerned Geographical Area: THE “MENINGITIS BELT”
Niger
 ~ 4200km from W to E
in a band of average width
600km
 North of the 300mm
isoheyet and south of the
1100mm isoheyet,
boundaries of CSME
between the Sahara and
the equator, no extensive
CSME epidemics recorded,
only sporadic cases.
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Death Toll
Inc. Rates
600
2500
300
2000
1500
200
1000
100
500
0
0
Incidence Rates
CSME periodic recurrence in Niger
Epidémies de MCS au Niger (1960-2015)
Death Toll
4000
3500
500
3000
400
CSME and its Seasonality in Niger
District level monthly meningitis incidence rates for Niger (1993-2000 data)
100
90
80
70
Cas/100000 hbts
60
50
40
30
20
10
0
Janvier
Février
Mars
Avril
Mai
Juin
Juillet
Août
Septembre
Octobre
Novembre
-10
Districts
Gaya
B.N'konni
Magaria
Maradi
Zinder
Niamey
N'Guigmi
Tillabéri
Agadez
Décembre
CSME and local weather & climate: the Sahelian (Harmattan)
Dust Factor (mass concentrations from converted daily mean
horizontal visibility data): the post 70’s build-up
Estimated average monthly dust mass concentrations (μg/m3) for (1950-1974) & (1975-1999)
(SL = Sahel zone of Niger)
SL_5074
SL_7599
1900
1700
1500
1300
1100
900
700
500
Oct
Nov
Dec
Jan
Feb
Mar
Apr
Month
May
Jun
Jul
Aug
Sep
V=4500m, Mass Concentration=490µg m-3
25 E
to
15W
10N to
20N
V<2K
m
M=
4MT
V=700m, Mass Concentration=1,700 µg m-3
What might be the main Physical Effects of this
suspended dust?
Data From the US DOE ARM Mobile Facility deployment (Nov05 – Jan07)
primary goal = studying Radiative divergence using AMF, GERB, and AMMA Network
+ 4 Atm. Soundings per day
(Courtesy of Mark Miller)
84360
82080
79800
77520
75240
72960
70680
68400
66120
63840
61560
59280
57000
54720
52440
50160
295
0
290
-5,00
-10,00
Dusty means V< 1Km; M> 1340 µg/m3 or 1.7 T/km2
82800
79200
75600
72000
68400
64800
61200
57600
54000
50400
lv_e_Hidust
84240
81900
79560
77220
74880
72540
70200
67860
65520
63180
60840
58500
56160
53820
51480
49140
46800
44460
42120
39780
37440
35100
32760
30420
28080
25740
23400
21060
18720
16380
14040
11700
9360
7020
4680
RelHlow
46800
43200
0
%
Met_VDS Temp Hi & low turbidity_Niamey2006
39600
VDS_sirt_lowdst
36000
32400
28800
325
25200
30,00
21600
Mean diurnal cycle sirt High dust&low turbidity (March 2006 Niamey)
18000
330
14400
35,00
10800
335
2340
20
7200
84240
81900
79560
77220
74880
72540
70200
67860
65520
63180
60840
58500
56160
53820
51480
49140
46800
°C
TempHi
3600
VDS_sirt_hidst
47880
44460
42120
39780
37440
35100
32760
30420
28080
25740
23400
21060
18720
16380
14040
11700
9360
7020
4680
2340
0
TempLow
45600
43320
41040
38760
36480
34200
31920
29640
27360
25080
22800
20520
18240
15960
13680
11400
9120
6840
4560
2280
0
Key Findings (high time resolution data)
VDS RelHum Hi & low dust
20
RelhHi
45
18
40
16
14
35
12
10
30
8
6
25
4
2
0
VDS lv_e High dust and low turbidity (March 2006 Niamey)
lv_e-clr
25,00
320
20,00
315
310
15,00
305
10,00
300
5,00
0,00
Aspects of CC (RClimdex Software): specific trend in night
time warming (daily meteorological data Niamey, Niger)
Indices
SYear
EYear
Slope
STD_of_Slope P_Value
tx90p
1950
2010 0.144
0.042
tn90p
1950
2010 0.44
0.036
0
dtr
1950
2010 -0.045
0.005
0
For 2 Hot SPOTS", and time period 1960-2000,
regression equations for Tn90p:
Niamey (NGR)
= 0,4566x + 3,1923 (r=0.69)
Fada NGourma (BF) = 0,5321x + 1,6673 (r=0.82)
0.001
RCMs runs for the same Tn90p:
GKSS-CCLM4.8 and METO_HCHadRMP, both 50km
resolution and 2021-2050 running period
(corr – quantile-quantile correction applied)
Courtesy of Inoussa Abdou Saley
Most possible consequences : focusing on what might
affect the human physiology:
The impact of suspended atmospheric dust on CSME
are physical at both individual and large scale levels:
1. Individual level: huge mass concentration and size
distribution issues related to air pollution impact on
health; the observed maximum of Mass distribution of
airborne dust particles lie in the 2-8 µm diameter
range - fine particle mode (penetration into deep lung
tissue and the subepithelial environment, bacteria
pick-up, …etc).
2. Large scale effect via radiative effects, particularly
increase of daily minimum temperature: warmer
nights, hyperthermia and dehydration, leading to
increased mortality risk.
Concerning the Relative humidity issues:
i) Climatological mean dry season minimum relative
humidity Un lies between 10 and 20% and highly
correlated along 13° latitude, lowest Un frequently
encountered between 1960 and 2005 = 4%
ii) Climatological mean dry season daily maximum
relative humidity Ux is between 45 and 70%, and
highly correlated along 13° latitude stations.
This situation, combined with high dust particles mass
concentrations, warmer nights and population
dynamics factors, could suggest existence of some
uniform impact which could explain fast propagation
of epidemics along these latitudes of the Meningitis
Belt.
Other Aspects of CSME and key climate
parameters in Sahel
Seasonal patterns for meningitis incidence rates, mass concentrations & annual rainfall
for Niger (1990 to 1999)
Men*X
Rain_Mean
Mass
250
1200
Dry Season Dust
1100
200
CSME
1000
150
900
800
100
700
50
600
0
500
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Rainfall as a key regulating factor!
Aug
Sep
The West African FMA “Mango rains” (useless?)




These are necessary rains to bring mangoes to
maturity in the Sudanian part of West Africa
during FMA;
Due to fluctuations of the position of the ITCZ
during this transitional period, these rains can
affect during usually 3 to 5 days the Sahelian
part of WAf;
Today, the occurrence of these rains seem to be
very sensitive to ‘local’ & ‘remote’ extremes of
climate change;
Composite analysis of the highest positive
anomalies of the FMA rains between 1960 and
2005 reveals the existence of some ocean
signal.
Meteorology of dry season FMA rains: main
atmospheric patterns for FMA rain production
EL NIÑO Signal : seasonal predictability of FMA rains?
On link between FMA rains and extremes of
climate change
CA suggests some El Nino signal for Sahel
FMA rains (marked SW flow from Gulf of Guinea).
This seems also to corroborate with composites of
both FMA El Nino Snow and Temperature
Anomalies at East Coast of the US, according to
NOAA CPC data. So, this could suggest some
seasonal predictability like in the case of summer
monsoon rains (usually JAS-BNR during EL Nino,
and JAS-ANR during La Nina).

Suggested links between FMA rains and CC
extremes:

CC extremes outside the region (the 2015
Winter US East Coast Blizzard).

CC extremes within the region (the 2016 heat
waves over Northern Africa)

Impact from remote CC extreme
Impact from regional CC extreme
El NIÑO 2015!
Discussion on rainfall potential in FMA
How to convert “Mango rains” into useful tools for
modulating CSME in the region, taking advantage of
both local and remote climate extremes?

The prevailing cloud types during rain production
in FMA are Ac and As which are ‘seedable’ under the
following conditions:

i) seed at cloud base using hygroscopic salts, to
take advantage of the cloud updrafts;
ii) seeding particles diameters should be around
40 µm;
iii) a specific dispenser system should be used
instead of the traditional flares.
Early 70’s Weather Modification Experiments!
Discussion on rainfall potential in FMA (ctnd)

Advantage of rainfall enhancement by seeding
clouds twofold: get rid of the suspended dust, bring
back reduced relative humidity to climatologically
normal value this season (lying between 20% and
60%) and subsequent reduced nocturnal warming.
What could be the strategies to combat
CSME in West African Sahel in the context of
climate change?
The « BAU » solution – targeting
vaccination, there are needs for:
1.
Seasonal prediction of both the epidemics
and prevailing group/strain (atmospheric dust
forecasting is not enough)

Take example from the solution to Malaria
(Roll Back Malaria and insecticide treated
bednets) to adapt it to masks that can prevent
further drying during night time (technological
solution).

2. Take advantage of possible side effects
of negative impacts of Climate Change on
temperature extremes to design geoengineering (WM) solution to increase
existing “Mango Rains” mass during FMA
So, both enhanced treats and solution to
CSME in the Sahel are possible, hence
there is a need to find and apply innovative
solutions to manage CSME in that area in
the context of climate change.
Extremes of CC will surely be
unavoidable, but nevertheless we
might also think of how we could
make them useful, in certain
circumstances, to bring at least
partial solutions to some pending
public health problems like the
meningitis epidemics in the Sahel
zone.
Thank you for attention.