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!