Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Más filtros












Base de datos
Intervalo de año de publicación
1.
Sci Total Environ ; 724: 138269, 2020 Jul 01.
Artículo en Inglés | MEDLINE | ID: mdl-32408457

RESUMEN

We studied the dynamics of dengue disease in two epidemic regions in Sri Lanka, the densely populated Colombo district representing the wet zone and the relatively less populated Batticaloa district representing the dry zone. Regional differences in disease dynamics were analysed against regional weather factors. Wavelets, Granger causality and regression methods were used. The difference between the dynamical features of these two regions may be explained by the differences in the climatic characteristics of the two regions. Wavelet analysis revealed that Colombo dengue incidence has 6 months periodicity while Batticaloa dengue incidence has 1 year periodicity. This is well explained by the dominant 6 months periodicity in Colombo rainfall and 1 year periodicity in Batticaloa rainfall. The association between dengue incidence and temperature was negative in dry Batticaloa and was insignificant in wet Colombo. Granger causality results indicated that rainfall, rainy days, relative humidity and wind speed can be used to predict Colombo dengue incidence while only rainfall and relative humidity were significant in Batticaloa. Negative binomial and linear regression models were used to identify the weather variables which best explain the variations in dengue incidence. Most recent available incidence data performed as best explanatory variables, outweighing the importance of past weather data. Therefore we recommend the health authorities to closely monitor the number of cases and to streamline recording procedures so that most recent data are available for early detection of epidemics. We also noted that epidemic responses to weather changes appear quickly in densely populated Colombo compared to less populated Batticaloa. The past dengue incidence and weather variables explain the dengue incidence better in Batticaloa than in Colombo and thus other exogenous factors such as population density and human mobility may be affecting Colombo dengue incidence.


Asunto(s)
Dengue , Humanos , Incidencia , Lluvia , Sri Lanka , Tiempo (Meteorología)
2.
Infect Dis (Lond) ; 52(5): 350-360, 2020 05.
Artículo en Inglés | MEDLINE | ID: mdl-32043410

RESUMEN

Background: Dengue occurs epidemically in Sri Lanka and every year, when the monsoon season begins, health authorities warn on rising numbers of dengue cases. The popular belief is that dengue epidemics are driven by the two monsoons which feed different parts of the country over different time periods. We analysed the time series of weekly dengue cases in all districts of Sri Lanka from 2007 to 2019 to identify the spatiotemporal patterns of dengue outbreaks and to explain how they are associated with the climatic, geographic and demographic variations around the country.Methods: We used time-series plots, statistical measures such a community-wide synchrony and Kendall-W and a time-varying graph method to understand the spatiotemporal patterns and links.Results and conclusions: The southwest wet zone and surrounding areas which receive rainfall in all four seasons usually experience two epidemic waves per year. The northern and eastern coastal region in the dry zone which receives rainfall in only two seasons experiences one epidemic wave per year. The wet zone is a highly synchronised epidemic unit while the northern and eastern districts have more independent epidemic patterns. The epidemic synchrony in the wet zone may be associated with the frequent mobility of people in and out of the wet zone hot spot Colombo. The overall epidemic pattern in Sri Lanka is not a sole outcome of the two monsoons but the regional epidemic patterns are collectively shaped by monsoon an inter-monsoon rains, human mobility, geographical proximity and other climate and topographical factors.


Asunto(s)
Dengue/epidemiología , Brotes de Enfermedades , Clima , Humanos , Incidencia , Lluvia , Estaciones del Año , Análisis Espacio-Temporal , Sri Lanka/epidemiología
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...