Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time–series and ARIMAX analyses

Chadsuthi, Sudarat; Modchang, Charin; Lenbury, Yongwimon; Iamsirithaworn, Sopon; Triampo, Wannapong

doi:10.1016/s1995-7645(12)60095-9

Cited by 119 publications

(142 citation statements)

References 19 publications

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“…Estos resultados coinciden con los obtenidos por Papa & Kotrotsiou (2015) quienes encontraron que la mayoría de los casos de leptospirosis en Grecia se presentan durante los meses de verano y otoño, que coinciden específicamente con el período lluvioso en este país. Además concuerdan con los resultados alcanzados por Martins et al (2010), Chadsuthi et al (2012) y Desvars et al (2013) quienes plantean que la leptospirosis en humanos es altamente estacional y vinculada a la estación lluviosa, destacando que éstas son favorables en el mantenimiento ambiental y la transmisión de la bacteria. Según Weinberger et al (2014) el incremento de las precipitaciones conduce a un aumento de la exposición de los humanos a través de dos vías fundamentales: la elevada supervivencia de la bacteria y el incremento de la exposición de los humanos a aguas superficiales.…”

Section: Materials Y Métodos Resultados Y Discusiónunclassified

Comportamiento Epidemiológico De La Leptospirosis Humana Y Animal en La Provincia De Villa Clara, Cuba

Castillo-Cuenca¹,

Iannacone²,

Fimia-Duarte³

et al. 2016

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Section: Materials Y Métodos Resultados Y Discusiónunclassified

Comportamiento Epidemiológico De La Leptospirosis Humana Y Animal en La Provincia De Villa Clara, Cuba

Castillo-Cuenca¹,

Iannacone²,

Fimia-Duarte³

et al. 2016

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“…During the rainy season the soil remains moist and can lead to formation of pools of water which help leptospires survive for longer periods of time, and ultimately can lead to an increase in human and animal exposure to the bacteria. 9,15 During dry periods, high concentrations of Leptospira in the soil are limited to a few meters around waste accumulation sources. 12 In the District of Gampaha, the rainy season period is about 6-8 months every year.…”

Section: Discussionmentioning

confidence: 99%

Spatial and seasonal analysis of human leptospirosis in the District of Gampaha, Sri Lanka

Denipitiya

Chandrasekharan

Abeyewickreme

et al. 2016

Sri Lankan J Infec Dis

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“…The reason is that the mensuration model is imperfect. Therefore, it is necessary to create a model with multivariate ARIMAX model (Fan et al 2009;Chadsuthi et al 2012).…”

Section: Arimaxmentioning

confidence: 99%

Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index

Jalalkamali

Moradi

2014

Int. J. Environ. Sci. Technol.

117

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Drought is among the most important natural disasters influencing different aspects of human life. In recent decades, intelligent techniques have shown to be highly capable of modeling and forecasting nonlinear and dynamic time series. Hence, the present study aimed to forecast drought using and comparing the multilayer perceptron artificial neural network (MLP ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector machine (SVM) model, and the autoregressive integrated moving average (ARIMAX) multivariate time series. To this end, the precipitation data obtained from the Yazd synoptic station for a 51-year statistic period were used. Moreover, the humidity levels for short-term (3 and 6 months) and long-term (9, 12, 18, and 24 months) periods were calculated using the Standardized Precipitation Index (SPI). Next, based on the results of calculations, the 1961-2002 period was selected as the control group and the 2003-2012 period was selected as the experimental group. In order to forecast the SPI for the t ? 1 period, values of SPI, precipitation, and temperature of previous eras were used. Results indicated that in a 9-months period (as the timescale), the ARIMAX model gives SPI values and forecast drought with more precision than the SVM, ANFIS, and MLP models.

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Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time–series and ARIMAX analyses

Abstract: The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions. The models can also be used to predict the next seasonal peak quite accurately.

Cited by 119 publications

References 19 publications

Comportamiento Epidemiológico De La Leptospirosis Humana Y Animal en La Provincia De Villa Clara, Cuba

Comportamiento Epidemiológico De La Leptospirosis Humana Y Animal en La Provincia De Villa Clara, Cuba

Spatial and seasonal analysis of human leptospirosis in the District of Gampaha, Sri Lanka

Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index

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