Dengue fever cases reported to polyclinics throughout Barbados (left) were examined for clustered “hotspots”, health districts with higher than expected disease activity. Consistencies in the locations of dengue fever hotspots in central and southern Barbados (in red, right) were found during years with high case numbers. This information will help the Ministry of Health plan future studies and improve disease surveillance. Image courtesy The American Journal of Tropical Medicine and Hygiene.

LIPPI, RYANSpatiotemporal Tools for Emerging and Endemic Disease Hotspots in Small Areas: An Analysis of Dengue and Chikungunya in Barbados, 2013–2016

Catherine A. Lippi, Anna M. Stewart-Ibarra, Moory Romero, Avery Q. J. Hinds, Rachel Lowe, Roché Mahon, Cedric J. Van Meerbeeck, Leslie Rollock, Marquita Gittens-St. Hilaire, Adrian R. Trotman, Dale Holligan, Shane Kirton, Mercy J. Borbor-Cordova, and S. J. Ryan

Article first published online: 27 APR 2020 The American Journal of Tropical Medicine and Hygiene

DOI: 10.4269/ajtmh.19-0919

ABSTRACT: Dengue fever and other febrile mosquito-borne diseases place considerable health and economic burdens on small island nations in the Caribbean. Here, we used two methods of cluster detection to find potential hotspots of transmission of dengue and chikungunya in Barbados, and to assess the impact of input surveillance data and methodology on observed patterns of risk. Using Moran’s I and spatial scan statistics, we analyzed the geospatial and temporal distribution of disease cases and rates across Barbados for dengue fever in 2013–2016, and a chikungunya outbreak in 2014. During years with high numbers of dengue cases, hotspots for cases were found with Moran’s I in the south and central regions in 2013 and 2016, respectively. Using smoothed disease rates, clustering was detected in all years for dengue. Hotspots suggesting higher rates were not detected via spatial scan statistics, but coldspots suggesting lower than expected rates of disease activity were found in southwestern Barbados during high case years of dengue. No significant spatiotemporal structure was found in cases during the chikungunya outbreak. Spatial analysis of surveillance data is useful in identifying outbreak hotspots, potentially complementing existing early warning systems. We caution that these methods should be used in a manner appropriate to available data and reflecting explicit public health goals—managing for overall case numbers or targeting anomalous rates for further investigation.

Read the full publication at The American Journal of Tropical Medicine and Hygiene.





LIPPI, RYANNonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study

Rachel Lowe, Antonio Gasparrini, Cédric J. Van Meerbeeck, Catherine A. Lippi, Roché Mahon, Adrian R. Trotman, Leslie Rollock, Avery Q. J. Hinds, Sadie J. Ryan, Anna M. Stewart-Ibarra

Article first published online: 17 JUL 2018 PLOS Medicine

DOI: 10.1371/journal.pmed.1002613


Over the last 5 years (2013–2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016.

Methods and findings
Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure–lag–response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure–lag–response functions of these indicators—rather than linear effects for individual lags—more appropriately described the climate–disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika.

We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region.

Read the full publication at PLOS Medicine






Image credit: Ms. Catherine Lippi. This study was conducted with epidemiological data collected in Barbados, an island located in the Caribbean (left). Population in Barbados (middle) and elevation on the island (right) are shown, as well as the location of the two meteorological stations that provided climate data for the study.

GAINESVILLE, FL – Medical Geography researchers from the University of Florida recently participated in a study that successfully predicted dengue fever outbreaks on the Caribbean island of Barbados, using climate data. This paper is part of a special issue of PLOS MEDICINE, focusing on the impacts of climate change on health, and is a result of an unprecedented collaborative project, funded by USAID to address climate driven health impacts in the Caribbean.

The study, led by Dr. Rachel Lowe from the London School of Hygiene and Tropical Medicine, tested whether dengue outbreaks in the Caribbean island of Barbados could be predicted using weather station data for temperature and a precipitation index (Standardized Precipitation Index- SPI) used to monitor drought and extreme rainfall. Using data from June 1999 to May 2016, researchers found that the statistical model was able to successfully predict months with dengue outbreaks versus non-outbreaks in most years.
Dengue fever is spread by Aedes sp. mosquitos and infects over 350 million people each year, resulting in 25,000 deaths globally and costing households, governments, and businesses over $45 million annually. In recent decades, the disease has emerged as a major public health threat, and as many as 2 in 5 people globally are at risk of contracting dengue fever.

UF Medical Geography professor Dr. Sadie Ryan and doctoral student Ms. Catherine Lippi collaborated on models that explored the delayed effect of climate indicators like extreme rainfall and drought on future outbreaks of dengue fever on the Caribbean island.
“This study highlights the importance of keeping long term records of climate and health data so that we can learn about how a changing climate will impact our health and well-being in the future,” said Dr. Ryan.
The model found a sharp increase in disease transmission one to two months after extreme rainfall events, but a surprising result of the model was an increase in infections four to five months after a drought event. Lippi explained “During droughts, people store water in containers near their homes,” she said, “which creates the perfect habitat for Aedes mosquitos.” Senior author, Dr Stewart-Ibarra, from SUNY Upstate Medical University said she and others working on the project had heard from locals that this was a recurring trend but it wasn’t until they studied the data that they found it to be true. “Barbados is a water-scarce country. During periods of drought, people have to store water.”

The findings have been published in a paper titled Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study in PLOS Medicine.

The study was part of a collaboration between UF and the Caribbean Agency for Public Health, the Pan American Health Organization, the Caribbean Institute for Meteorology and Hydrology, as well as investigators from the London School of Hygiene and Tropical Medicine, SUNY Upstate Medical University, and the Escuela Superior Politecnica del Litoral of Ecuador.

Spring 2016 PhD graduate Dr. José Javier Hernández Ayala has accepted the position of Visiting Assistant Professor of Physical Geography and Climatology in the Department of Geography at Texas A&M University. At Texas A&M, Javi will be teaching undergraduate and graduate sections of the courses Global Climate Regions and Hydrology & Environment. Javi will also continue his research hydroclimatic variability, change  and extremes and their socioeconomic impacts in the Caribbean.

Congratulations Javi!

MUELLER, YANGMarket accessibility and hotel prices in the Caribbean: The moderating effect of quality-signaling factors

Yang Yang, Noah J. Mueller, Robertico R. Croes

Article first published online: 21 MAR 2016 Tourism Management

DOI: 10.1016/j.tourman.2016.03.021

ABSTRACT: The purpose of the paper is to investigate the influence of market accessibility on hotel prices and how this influence is moderated by various quality-signaling factors, such as online user ratings, “thumbs up” (recommendation) percentage, hotel class, and chain affiliation. Using a randomized sample of hotels in the Caribbean islands, we employ a three-level mixed-effect linear regression model to investigate the plausible relationship between market accessibility and hotel prices. After controlling for unobserved island-level and hotel-level characteristics, the model indicates that in most periods, low market accessibility (high flight costs) leads to lower hotel prices, and this influence is mitigated by well-established positive reputations as represented by the quality-signaling factors. Our findings imply that hotels should work to increase their reputations to help buffer the impacts of inaccessibility. In an effort to increase market accessibility, one course of action is to reduce airport landing taxes and fees.

Read the full publication at Tourism Management.

HERNANDEZ AYALA, MATYAS – Tropical cyclone rainfall over Puerto Rico and its relations to environmental and storm-specific factors

José J. Hernández Ayala and Corene J. Matyas

Article first published online: 17 Sep 2015 International Journal of Climatology

DOI: 10.1002/joc.4490

ABSTRACT:  Although tropical cyclone rainfall (TCR) is common over Puerto Rico, the factors that cause this rain to vary from one storm to another have not been studied. The aim of this article is to understand how storm-specific characteristics including storm location, duration, storm centre proximity to land, intensity, horizontal translation speed (HTS) and environmental factors like moisture and vertical wind shear affect TCR variability over Puerto Rico. TCR was determined at rain gauge locations for days when storms were within a 500 km radius of Puerto Rico. The station data were then used to calculate an island-averaged total rainfall value for 86 storms during 1970–2010. The maximum observed rainfall was also examined. Correlation analyses of the individual predictors, principal component regression (PCR) procedures and Mann–Whitney U tests identified precipitable water, storm centre proximity to land, mid-level relative humidity (MRH), duration, HTS and longitude as the predictors with the strongest influence on rainfall. The PCR showed that a component comprised of precipitable water, MRH and longitude accounted for more than 60% in TCR variability. When an additional component comprised of storm duration, storm centre proximity to land and translation speed was considered, the PCR model explained 70% (52%) of the variability in mean (maximum) TCR. Key threshold values for high rainfall across Puerto Rico are a storm centre distance of 233 km or less and moisture exceeding 44.5 mm of precipitable water and 44.5% of relative humidity with forward speeds of 6.4ms−1 or less. Extreme rainfall at a single location can occur when a TC’s centre is over 450 km away.

Read the full publication at International Journal of Climatology