American tegumentary leishmaniasis (ATL) is a disease transmitted to humans by the female sandflies of the genus Lutzomyia. Several factors are involved in the disease transmission cycle. In this work only rainfall and deforestation were considered to assess the variability in the incidence of ATL. In order to reach this goal, monthly recorded data of the incidence of ATL in Orán, Salta, Argentina, were used, in the period 1985–2007. The square root of the relative incidence of ATL and the corresponding variance were formulated as time series, and these data were smoothed by moving averages of 12 and 24 months, respectively. The same procedure was applied to the rainfall data. Typical months, which are April, August, and December, were found and allowed us to describe the dynamical behavior of ATL outbreaks. These results were tested at 95% confidence level. We concluded that the variability of rainfall would not be enough to justify the epidemic outbreaks of ATL in the period 1997–2000, but it consistently explains the situation observed in the years 2002 and 2004. Deforestation activities occurred in this region could explain epidemic peaks observed in both years and also during the entire time of observation except in 2005–2007.
The present study aimed to understanding some of the process underling in the dynamics of American Tegumentary Leishmaniasis (ATL), which had occurred in Department of Orán, Argentina, in the period 1985-2007. The monthly empirically estimations occurrence probabilities of cases of ATL could be to help understand how epidemiological mechanisms of enhancement during the endemic period or outbreaks of ATL. We have estimated monthly probabilities of occurrence thereof, the monthly empirical cumulative distribution function, the monthly probability mass function, in order to establish a partition of [0,1] and apply-
En este trabajo se realizó un estudio estadístico de variablesfísico químicas asociadas al fenómeno de contaminación ambiental,en particular concentración media mensual de SO2, medidas en la ciudad Salta Capital, Argentina, simultáneamente a concentracionesde NO2 y O3. Las series bajo estudio presentaban comportamientosdinámicos no lineales, datos atípicos y cambios estructurales, lo quehizo imposible modelarlas con tipologías econométricas tradiciones(AR, MA, ARMA, ARIMA, entre otras). Una solución eficiente quese encontró, hace uso de la teoría de los perceptrones multicapa.Mediante el modelo estructural de series de tiempo, esta solución sepresenta como un proceso matemático iterativo que permite obtenerun modelado final el cual tiene una muy alta confiabilidad (95%),para realizar pronósticos a futuro sobre el comportamiento de lavariable estudiada.
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