Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series.

Fire risk mapping - mapping the probability of fire occurrence and spread - is essential for pre-fire management as well as for efficient firefighting efforts. Most fire risk maps are generated using static information on variables such as topography, vegetation density, and fuel instantaneous wetness. Satellites are often used to provide such information. However, long-term vegetation dynamics and the cumulative dryness status of the woody vegetation, which may affect fire occurrence and spread, are rarely considered in fire risk mapping. Here, we investigate the impact of two satellite-derived metrics that represent long-term vegetation status and dynamics on fire risk mapping - the long-term mean normalized difference vegetation index (NDVI) of the woody vegetation (NDVIW) and its trend (NDVIT). NDVIW represents the mean woody density at the grid cell, while NDVIT is the 5-year trend of the woody NDVI representing the long-term dryness status of the vegetation. To produce these metrics, we decompose time-series of satellite-derived NDVI following a method adjusted for Mediterranean woodlands and forests. We tested whether these metrics improve fire risk mapping using three machine learning (ML) algorithms (Logistic Regression, Random Forest, and XGBoost). We chose the 2007 wildfires in Greece for the analysis. Our results indicate that XGBoost, which accounts for variable interactions and non-linear effects, was the ML model that produced the best results. NDVIW improved the model performance, while NDVIT was significant only when NDVIW was high. This NDVIW-NDVIT interaction means that the long-term dryness effect is meaningful only in places of dense woody vegetation. The proposed method can produce more accurate fire risk maps than conventional methods and can supply important dynamic information that may be used in fire behavior models.

Prevalencia de ascariasis en pacientes pediátricos del medio nosocomial público y privado / Ascariasis prevalence in pediatric patients from nosocomial public and private environment

Se realizó un estudio propectivo a fin de determinar la prevalencia de ascariasis en niños del medio hospitalario público y privado. Tras un muestreo simple al azar, se tomaron 68 pacientes del medio público y 39 del privado, con edades compredidas entre 1 mes y 12 años. Se practicó examen coprológico simple y según la técnica de concentración de Kato. Se revisó la existencia de antecedentes de parasitosis intestinal y sintomatología. El análisis estadístico se realizó mediante x², F y Z. La prevalencia fue menor a la reportada en la literatura y no difirió (p>0,05) entre el medio público (11,76 por ciento) y privado (5,13 por ciento). La edad fue menor a la reportada en otros estudios. La sintomatología, fue vaga y demostró una especificidad diagnóstica de 61 por ciento. El antecedente como elemento diagnóstico presuntivo (especificidad 93,81 por ciento probabilidad pos-prueba 33 por ciento) demostró utilidad para pesquisa