Revista de Biología Tropical ISSN Impreso: 0034-7744 ISSN electrónico: 2215-2075

OAI: https://revistas.ucr.ac.cr/index.php/rbt/oai
Modeling of Equivalent Effective Temperature and its possible incidence on larval density of Anopheles mosquitoes (Diptera: Culicidae) in Villa Clara province, Cuba
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Keywords

Anopheles
larval density
modeling
Equivalent Effective Temperature
Villa Clara
Cuba
Anopheles
densidad larval
modelación
Temperatura Efectiva Equivalente
Villa Clara
Cuba.

How to Cite

Fimia Duarte, R., Alarcón-Elbal, P. M., Osés Rodríguez, R., Argota Pérez, G., Iannacone Oliver, J., & Capote Cabrera, J. (2017). Modeling of Equivalent Effective Temperature and its possible incidence on larval density of Anopheles mosquitoes (Diptera: Culicidae) in Villa Clara province, Cuba. Revista De Biología Tropical, 65(2), 565–573. https://doi.org/10.15517/rbt.v65i2.24709

Abstract

The wind chill or Equivalent Effective Temperature (EET) is the thermic sensation that a person feels when being exposed to a certain combination of temperature from the air, relative humidity and wind velocity. The objective of this investigation was directed to determine the possible incidence of the EET upon the larval density of Anopheles mosquitoes in Villa Clara province, Cuba. The Climatological data were compiled from the Yabú station in Santa Clara, and a total of 5 370 measurements were included in a database every three hours, using the aggregate function of the Statistical Package of Social Science software version 13 (SPSS), from January 1st, 2011 to September 30th, 2013. A long term forecast (1 year of advance) was made to obtain EET and the Anopheles larval density in the locality of Santo Domingo was modelled. These entomological data were taken at the same time but monthly, so the EET data were converted to monthly scale to be correlated with the monthly data of the larval density. The result was a 97.1 % of variance with a standard error (SE) of 3.57 °C for the model of the EET with a year of anticipation; therefore, the tendency in time was significant. The modeling also included the Anopheles larval density of mosquitoes in Santo Domingo, Villa Clara province, observing an increase of the EET, while the Anopheles mosquito larvae also increased. The most important variables in the model were the EET that were back in 1, 2, 3, 4, 7, 16, 24, 40 for the previous year; that is to say 2 920, 2 921, and so on, which explained a strong contagion among the data. EET correlation compared with itself in previous year was high; therefore, it may be used as a predictable variable. The anophelinic density in Santo Domingo explained the 66 % of the variance, with a SE of 0.66 larvae.m-2. The tendency of the Anopheles larval density was to diminish. In conclusion, EET has an important impact in larval density of Anopheles with EET increase associated with larval density increase.

 

https://doi.org/10.15517/rbt.v65i2.24709
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