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.
Highlights
This re-emerging and life-threatening disease is responsible for one of the main worldwide health problems, on social and economic scales (Greenwood, 1997; WHO, 2005)
Anopheles larval density in Santo Domingo locality was modeled and the Effective Temperature (EET) of Yabu station was included as independent variable (EET_mean), as shown in the model of table 2; variables «ST» (Sawtooth) and «IST» (Inverted Sawtooth) were significant
Predictor variables were EET of the previous year: «Step4207» is the case number 4207 that had an important impact in the modeling; «ST» and «IST» were dichotomous variables; «Stepmenor1» represented the variable that captured the impact of the residuals that were minor than 1 larvae/m2; «Lag2959TEE», «Lag2922TEE», «Lag2935TEE», «Lag2920TEE», «Lag2926TEE», «Lag2923TEE», «Lag2921TEE» and «Lag2927TEE» were the lag variables of EET in a previous year; and «NoC», which is the tendency of the model
Summary
This re-emerging and life-threatening disease is responsible for one of the main worldwide health problems, on social and economic scales (Greenwood, 1997; WHO, 2005). According to the latest WHO estimates, released in December 2015, there were 214 million cases of malaria in 2015 and 438 000 deaths, mostly in Sub-Saharan Africa, which carries a disproportionately high share of the global malaria burden (WHO, 2016). This parasitic disease was successfully eradicated from Cuba as of 1967 and the country was certified “malaria-free” by WHO in November 1973 (OPS-OMS, 1972). Cuba’s climate is tropical and seasonally wet; with many different mosquito species thriving under these conditions (García, 1997) Of these numerous species, six species fall into the genus Anopheles and have been reported from several regions within the country (González, 2006). The objective of this research was to model the wind chill for the meteorological station of Yabu, located in the municipality of Villa Clara named Santo Domingo, and to determine/to establish the possible relation of this indicator with the Anopheles larval density
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