CLIMATE VARIABILITY IMPACTS ON DENGUE AND VULNERABILITY IN THE CARIBBEAN
description
Transcript of CLIMATE VARIABILITY IMPACTS ON DENGUE AND VULNERABILITY IN THE CARIBBEAN
CLIMATE VARIABILITY IMPACTS ON DENGUE AND VULNERABILITY IN THE CARIBBEAN
Dharmaratne Amarakoon**, Anthony Chen, Roxann Stennett
Climate Studies Group Mona, UWI, Jamaica
Samuel C. Rawlins, David Chadee
UWI, St. Augustine Campus & Ministry of Health, Trinidad
2nd AIACC REGIONAL MEETING, Buenos Aires, Argentina: August 24-27, 2004
QUESTIONS THAT ARE BEING ANSWERED
• What was the geographical distribution and the nature of dengue patterns in the Caribbean?
• What was the nature of the climate variability in the Caribbean over the last few decades?
• What are the factors that may impact Dengue epidemics, revealed from other studies?
• What were the impacts of climate variability on DENGUE seen in the Caribbean?
• What communities are expected to be potentially vulnerable and possible reasons for the vulnerability?
• How could the results from this impact study be utilized to reduce vulnerability?
DATA & METHODOLOGY• The data acquired for the CCID project by the CSGM provided the bulk
of the climate data: Temperature (maximum, minimum and mean) and Precipitation, daily or monthly values
• CAREC provided the epidemiology data in the form of reported dengue cases and vector indices, annual, 4-week period, monthly, quarterly values. More attention was focused on reported dengue cases
• Data analysis: Time series analysis of annual reported cases and their rates of change, mean temperature, mean precipitation, temperature and precipitation anomalies; Study of the climatology of temperature, precipitation, and reported cases; Performance of statistical significance tests for observed correlations and multiple linear regression, wherever applicable.
• ENSO year (El Niño & La Niña) classification: NOAA-CDC MEI index and NCEP/CPC Quarterly SST index
{EN: 1982/83, 1986/87, 1992/93, 1997/98. LN: 1988/89, 1998+/00} Supplementary: 1994/95
• Main study period: 1980 to 2001
THE CARIBBEAN
Incidence of Dengue
Caribbean- Reported Cases
-6000
-4000
-2000
0
2000
4000
6000
8000
10000
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Annual totals
Rate of change
CAREC 4-WEEK ACCUMULATION (1995-2001)
0
1000
2000
3000
4000
5000
6000
1 2 3 4 5 6 7 8 9 10 11 12 13
4- Week period
Accumulated reported cases
Average 4-weekperiod accumulation
Jn D
En+1En
Figure b
Figure a
DISTRIBUTION OF EPIDEMICS PEAKS AMONG ENSO PHASES
REGION TOTAL El Nino & +1
La Nina Neutral
Caribbean 8 7 -
1
T & T 8 6 - 2
Barbados 6 5 - 1
Jamaica 5 4 - 1
Belize 4 3 1
Variability of 4-week Cases(T & T), rainfall and Temperature in Trinidad
-100
100
300
500
700
900
1100
Reported Cases/Rainfall (mm)
25
26
27
28
29
30
31
32
Temperature (C)
4-week period dengue cases
4-Week period rainfall
4-Week period temperature
Jan 95 Jan 96 Jan 97 Jan 98 Jan 99
Seasonality of the Epidemics and Relation toClimate Parameters
___________________________________________________________Country Year Epidemic Peak Temperature Precipitation
Peak Peak___________________________________________________________
T and T 1995 August (weak) Apr. to Nov Jun. to Sep.
1996 September (strong) Apr. to Dec. May to Oct. 1997 December (strong) May to Dec. July and Nov. 1998 July to Sep. (strong) March to Nov. May to Sep.
1999 September (weak) Apr. to Dec. Jul. to Oct.
Barbados1995 October (strong) Apr. to Nov. Jul. to Oct. 1996 September (weak) Apr. to Nov. May to Nov. 1997 November (strong) Apr. to Nov. June to Nov.
1998 Aug. to Sep. (weak) Apr. to Oct. Jul. to Nov. 1999 November (weak) Apr. to Nov. Jun. to Nov.
_____________________________________________________________
Recent analysis of Caribbean temperature by Peterson and Taylor et al (2002) show increasing trend
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000
Rainfall AnomaliesTemperature Anomalies
1980-2000: r = -0.4415, p = 0.0451980-1989: r = -0.6574, p =0.0391990-2000: r = 0.4, p = 0.22
RF
1980-2000: r = 0.7056, p = 0.00031980-1989: r = 0.2668, p = 0.45621990-2000: r = 0.634, p = 0.036
Temp
Time Series of Rainfall and Temperature anomalies at Piarco in Trinidad
TIME SERIES ANALYSIS OF TEMPERATURE AND RAINFALL
COUNTRY 1980-2000Temperature
r:p
1990-2000Temperature
r:p
1980-2000
Rainfall
r:p
1990-2000
Rainfall
r:p
T & T 0.706:0.003 0.634:0.036 -0.441:0.045 0.04:0.22
Barbados 0.171:0.47 0.598:0.068 0.064:0.788 -0.149:0.681
Jamaica 0.572:0.007 0.272:0.418 0.223:0.330 0.025:0.941
Belize 0.522:0.026 0.102:0.779 0.247:0.323 0.512:0.131
IMPACTS SEEN IN OTHER STUDIES
• Hales et al.,(1996)- Association of upsurges of dengue in south pacific islands with ENSO events.
• Gagnon et al.,(2001)- Statistically significant correlation (>90% confidence level) between dengue epidemics and El Nino events in French Guiana, Indonesia, Colombia and Surinam.
• Poveda et al.,(2000)- Association of dengue peaks in Colombia during El Nino+1 years due to temperature increases and stagnant water collected for use during drought.
• Campione-Piccardo et al.,(2003)-Monthly reports of dengue cases and virus isolates following the rainfall with a lag of two to three months, in Trinidad and Tobago.
• Focks et al.,(1995)- Possibility of shortening of EIP (Extrinsic Incubation Period) at higher temperatures.
• Koopman et al.,(1991)-Possibility of higher transmission rates of dengue at shorter incubation periods.
• Wegbreit (1997)-Statistically significant relationship between temperature and dengue incidence rates in T & T, given a lag of about six months.
CORRELATION RESULTS OF ANNUAL DENGUE CASES WITH TEMPERATURE AND RAINFALL
COUNTRY TEMPERATURE
r:p
RAINFALL
r:p
T & T (1980-01)
El Nino (1980-01)El Nino (1990-01)
0.5663:0.006
0.6798:0.031
0.8271:0.0423
Not Significant
Not Significant
0.8784:0.0213
Barbados(1980-02)
El Nino (1980-02)
El Nino (1990-02)
0.479:0.0207
0.5854:0.0584
0.6261:0.1326
Not Significant
Not Significant
Not significant
Jamaica (1980-00) 0.4284:0.053 Not Significant
LAG CORRELATION RESULTS (Multiple Regression)
YEAR COUNTRY LAG-Temp LAG-Precip r p
1995 T & T weak weak weak weak
Barbados
5-M
5-M
2-M
1-M
0.863
0.843
0.065
0.083
Jamaica 5-M 1-M 0.877 0.054
1996
LAG
T & T 5-4W
3-4W
3-4W
2-4W
0.830
0.817
0.054
0.021
Barbados 4-M 1-M 0.815 0.066
1997
LAG
T & T 3-4W
3-4W
1-4W
2-4W
0.792
0.785
0.032
0.035
Barbados 5-M
5-M
1-M
2-M
0.937
0.958
0.015
0.007
1998
LAG
T & T 2-4W
1-4W 0.702 0.066
Barbados 3-M
1-M 0.820 0.035
Jamaica 1-M 0-M 0.684 0.081
1999
LAG
T & T 2-4W
3-4W
1-4W
1-4W
0.947
0.965
<0.000
<0.000
Barbados 5-M
5-M
1-M
4-M
0.862
0.957
0.066
0.007
[Wegbreit (1997)]
MonthlyVariability OF Rainfall, MeanT and Breteau
Index in 2003: T & T
0
50
100
150
200
250
300
350
400
450
Jan FebMarch April May June July Aug Sept. Oct. Nov. Dec.
Precipitation (mm)
20
25
30
35
40
45
MeanT(C) & BI
Rainfall
Mean Temperature
Breteau Index(BI)
MeanT:26.4 to 28.6 C
DFC: June to October
RESULTS SUMMARY
• There is a well defined seasonality in the epidemics.
• Probability of epidemics during El Nino and El Nino+1 years is high.
• Both temperature and rainfall influence dengue outbreaks. Inter-annual variability is more associated with temperature (warming) and intra-annual variability is linked more to rainfall variability.
SCENARIOS LEADING TO VULNERABILITY
(POTENTIAL BREEDING PLACES)
POTENTIALLY VULNERABLE COMMUNITIES
• Having no knowledge of the disease and vulnerabilty.
• With poor environmental conditions, including sanitation.
• That are densely populated.
• Without suitable water supplies (pipe borne water) which results in water collection in containers for longer periods of use.
POSSIBLE REASONS FOR VULNERABILITY
• Lack of resources (funds, manpower).
• Absence of active vector eradication programmes (no regular spraying, no use of bacteria like BT [Bacilus Thuringien]).
• Absence of relevant education programmes on awareness.
• Absence of procedures to monitor the communities and environmental conditions and upkeep.
• Socio-economic status of communities (poverty, high population density).
• Insufficient knowledge on vector dynamics and virus replication.
How could the results from this impact study be utilized to reduce vulnerability ?
Develop early warning systems based on the seasonality, lag and future climate predictions, leading to effective programmes on public awareness and education.
“Public Awareness & Education”
Best Option to reduce Vulerability:
CLEAN-UP OR PAY-UP!