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Acta Trop. 2017 Aug;172:50-57. doi: 10.1016/j.actatropica.2017.04.017. Epub 2017 Apr 24.

Modelling dengue fever risk in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature.

Author information

1
Institute for Marine Remote Sensing, University of South Florida, College of Marine Science, 140 7th Avenue South, Saint Petersburg, FL 33701, USA. Electronic address: elias3@mail.usf.edu.
2
Centro de Investigaciones Regionales, Lab de Arbovirología, Unidad Inalámbrica, Universidad Autónoma de Yucatan, Calle 43 No. 613 x Calle 90, Colonia Inalámbrica, C.P. 97069, Merida, Yucatan, Mexico.
3
Servicios de Salud de Yucatan, Hospital General Agustin O'Horan Unidad de Vigilancia Epidemiologica, Avenida Itzaes s/n Av. Jacinto Canek, Centro, C.P. 97000, Merida, Yucatan, Mexico.
4
Institute for Marine Remote Sensing, University of South Florida, College of Marine Science, 140 7th Avenue South, Saint Petersburg, FL 33701, USA.

Abstract

Accurately predicting vector-borne diseases, such as dengue fever, is essential for communities worldwide. Changes in environmental parameters such as precipitation, air temperature, and humidity are known to influence dengue fever dynamics. Furthermore, previous studies have shown how oceanographic variables, such as El Niño Southern Oscillation (ENSO)-related sea surface temperature from the Pacific Ocean, influences dengue fever in the Americas. However, literature is lacking on the use of regional-scale satellite-derived sea surface temperature (SST) to assess its relationship with dengue fever in coastal areas. Data on confirmed dengue cases, demographics, precipitation, and air temperature were collected. Incidence of weekly dengue cases was examined. Stepwise multiple regression analyses (AIC model selection) were used to assess which environmental variables best explained increased dengue incidence rates. SST, minimum air temperature, precipitation, and humidity substantially explained 42% of the observed variation (r2=0.42). Infectious diseases are characterized by the influence of past cases on current cases and results show that previous dengue cases alone explained 89% of the variation. Ordinary least-squares analyses showed a positive trend of 0.20±0.03°C in SST from 2006 to 2015. An important element of this study is to help develop strategic recommendations for public health officials in Mexico by providing a simple early warning capability for dengue incidence.

KEYWORDS:

Aedes aegypti; Public health; Satellite imagery; Sea surface temperature

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