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Epidemiology Patterns Of Dengue In The Caribbean Under Climate Change A. M. D. Amarakoon**, Epidemiology Patterns Of Dengue In The Caribbean Under Climate Change A. M. D. Amarakoon**, Anthony A. Chen, Michael A. Taylor, Rainaldo F. Crosbourne Climate Studies Group Mona, UWI, Jamaica Samuel C. Rawlins, Karen Polson Caribbean Epidemiology Centre, Trinidad & Tobago Wilma Bailey, Charmaine Thomas-Heslop Department of Geography, UWI, Jamaica [** SPEAKER]

PROJECT: AIACC-SIS 06 The Threat of Dengue Fever Assessment of Impacts and Adaptation to PROJECT: AIACC-SIS 06 The Threat of Dengue Fever Assessment of Impacts and Adaptation to Climate Change in Human Health in the Caribbean An AIACC Project at The University of the West Indies, Mona and Caribbean Epidemiology Centre

THE CARIBBEAN THE CARIBBEAN

OBJECTIVES To determine the extent of the association between climate and the incidence of OBJECTIVES To determine the extent of the association between climate and the incidence of dengue across the Caribbean Region. To explore possible adaptation options The approaches selected to achieve the objectives: Investigate the influence of climate, through temperature and precipitation, on the epidemics Investigate the seasonality (seasonal variability of the epidemic) Investigate the degree of association of dengue epidemics with ENSO events Examine, briefly, some adaptation options.

Previous studies/events that influenced the selected approaches: • Hales et al (1996), Poveda et Previous studies/events that influenced the selected approaches: • Hales et al (1996), Poveda et al (2000), Gagnon et al (2001) • Koopman et al (1991), Focks et al (1995) • Ropelewski and Halpart (1996), Chen et al (1997), Malmgren et al (1998), Taylor (1999), Chen and Taylor (2001) • AIACC V & A workshop, Trieste, Italy, June 2002

DATA & METHODOLOGY • • The data acquired for the CCID project by the 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 (Fisher’s exact test using suitable contingency tables) for observed correlations, wherever applicable. • • ENSO year (El Niño & La Niña) classification: NOAA-CDC MEI index {EN: 1982/83, 1986/87, 1992/93, 1997/98. LN: 1988/89, 1998+/00} Supplementary: 1994/95 Main study period: 1980 to 2001

MEI El Niño La Niña MEI El Niño La Niña

Jn En En+1 D Jn En En+1 D

Some Case Studies: T & T Some Case Studies: T & T

Time Series of Temperature Anomalies: 1980 to 2001 Time Series of Temperature Anomalies: 1980 to 2001

Time Series of Rainfall Anomalies: 1970 to 2001 Time Series of Rainfall Anomalies: 1970 to 2001

Time Series of Reported Cases & Rainfall (mm) in 4 -Week Periods 95 Jan Time Series of Reported Cases & Rainfall (mm) in 4 -Week Periods 95 Jan 96 Jan 97 Jan 98 Jan 99 Dec

A sample of Monthly Variability in House Index: Port of Spain City Co-operation A sample of Monthly Variability in House Index: Port of Spain City Co-operation

Some Results For Jamaica Reported Cases Jn D Some Results For Jamaica Reported Cases Jn D

Au D D SE N Au D D SE N

Statistical Significance Level of ENSO Associations (* with 1994/95) REGION El Niño (N) El Statistical Significance Level of ENSO Associations (* with 1994/95) REGION El Niño (N) El Niño+1 N &N+1 La Niña (N+1) Caribbean (8 Epeds) T&T (8 Epeds) Barbados (6 Epeds) Jamaica (5 Epeds) 88% 64% 88% 74% 53% 80% (90%)* 92% ** (94%)* 92% ** (80%)* 90% ** (95%)* 79% (89%)* -

Results Summary In general, across the region, 19 -nineties are observed to be more Results Summary In general, across the region, 19 -nineties are observed to be more prone to the epidemic than 19 -eighties. There is a periodicity of about 4 to 3 years in the 19 -eighties and 3 to 2 years in the 19 -nineties with more frequent outbursts. May be due to the fact that, in the 19 -ninetees, temperatures were warmer and rainfall was less abundant, for example, as indicated by the anomalies for T & T. These conditions reduce the incubation period and increase the disease transmission rate. The epidemic shows a well defined seasonality over the region. It occurs in the latter half of the year. The warmer and drier conditions (less abundance in rainfall) appear to trigger the epidemic with the onset of the rainfall, which subsequently & speedily develops. Longer spells of less abundant rainfall and warmer temperatures appear to enhance the probability of the epidemic. There is a tendency for the spread to get narrower, from south east to north in the region. Perhaps, this may be due to the warmer & moist climate (tropical warm moist climate, more suitable for vector breeding and propagation) that persists in the SE, in contrast to the tropical climate with seasonal rainfall in the central and the nothern part. The periodicity seen roughly agrees with the periodicity of ENSOs

SYNOPSIS Significance: The work discussed forms a part of the retrospective component of the SYNOPSIS Significance: The work discussed forms a part of the retrospective component of the AIACC Dengue Project-SIS 06. May be stated that, exciting features of the dengue epidemic & evidence of climate influence are seen. Namely; (i) Periodicity & Seasonality. (ii) The influence of the temperature and rainfall. (Iii) Significant association with El Niño episodes (N & N+1 together). We cannot change the Climate Change! But adaptation measures could be provided to minimize the impacts

Impacts on Vector A: Temperature Increase: Increase in numbers, increased frequency of blood meals, Impacts on Vector A: Temperature Increase: Increase in numbers, increased frequency of blood meals, and expanded spatial distribution including highland areas. Also increases rate of extrinsic incubation( period lowers) B: Precipitation: Either increase or decrease in larval habitats (very heavy rainfall could flush out habitats). Humidity increase may increase survival. Flooding, and hence stagnant water, could increase small habitats. Droughts could result in possible decrease in larval habitats, but storage of water increases

COMMON SCENARIOS (POTENTIAL BREEDING PLACES) COMMON SCENARIOS (POTENTIAL BREEDING PLACES)

Possible Adaptation Options Intensify public awareness through propaganda and education Devise early warning systems Possible Adaptation Options Intensify public awareness through propaganda and education Devise early warning systems coupled to climate forecasts Make the public health sector more efficient and effective on issues concerning vector borne diseases (vector control, surveillance, health education) If socioeconomic (SE) conditions, attitudes and practices are contributing factors, make attempts to improve/change them.

 Examples of Adaptation: Venice Trip in June 2002 One of the best Adaptation Examples of Adaptation: Venice Trip in June 2002 One of the best Adaptation Options: “Public Awareness & Education”

CONSTRAINTS Time series of dengue data spanned only 20 years, which limited the number CONSTRAINTS Time series of dengue data spanned only 20 years, which limited the number of ENSO episodes to 4. The influence (+ve or –ve) introduced by migration activities taking place, spraying and serotypes/dengue types have not been considered. Need to complete a socioeconomic & a KAP survey, which presumably will occur soon.