CLIMATE VARIABILITY IMPACTS ON DENGUE AND VULNERABILITY IN THE CARIBBEAN

1. CLIMATE VARIABILITY IMPACTS ON DENGUE AND VULNERABILITY IN THE CARIBBEAN Dharmaratne Amarakoon**, Anthony Chen, Roxann Stennett ClimateStudiesGroupMona, 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

2. 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?

3. 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; Studyof 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-CDCMEI 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

4. THE CARIBBEAN Incidence of Dengue

5. Figure b Jn D Figure a En En+1

6. DISTRIBUTION OF EPIDEMICS PEAKS AMONG ENSO PHASES

8. Seasonality of the Epidemics and Relation to Climate Parameters ___________________________________________________________ Country Year Epidemic Peak Temperature Precipitation Peak Peak ___________________________________________________________ T and T1995 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. _____________________________________________________________

9. Recent analysis of Caribbean temperature by Peterson and Taylor et al (2002) show increasing trend

10. Time Series of Rainfall and Temperature anomalies at Piarco in Trinidad

11. TIME SERIES ANALYSIS OF TEMPERATURE AND RAINFALL

12. 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.

13. CORRELATION RESULTS OF ANNUAL DENGUE CASES WITH TEMPERATURE AND RAINFALL

14. LAG CORRELATION RESULTS (Multiple Regression)

18. [Wegbreit (1997)]

19. MonthlyVariability OF Rainfall, MeanT and Breteau Index in 2003: T & T

20. 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.

21. SCENARIOS LEADING TO VULNERABILITY(POTENTIAL BREEDING PLACES)

22. 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.

23. 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.

24. 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.

25. Best Option to reduce Vulerability: “Public Awareness & Education” CLEAN-UPORPAY-UP!

26. Thank you