Objetivo. Evaluar la evolución de pandemia de la COVID-19 entre los países de las Américas, comparando datos de los sistemas de salud previo a la llegada del virus a la Región, frente a los casos y muertes acumuladas antes del despliegue de las estrategias de inmunización de la población, y el estado actual de la vacunación. Métodos. Se realizo un análisis multivariante HJ-Biplot y análisis de cluster, para 28 países de la Región de las Américas, en tres momentos del tiempo: diciembre de los años 2019, 2020 y 2021. Resultados. En el continente americano se observa heterogeneidad en las acciones implementadas para contener la pandemia, la cual se refleja en diferentes grupos de naciones. Conclusiones. No todos los países de la Región de las Américas contaban con las condiciones de salubridad necesarias para afrontar la contención de la COVID-19. A cierre de 2019 Estados Unidos, Canadá, Brasil y Cuba se observaban con ventajas frente a los demás países de la Región, sin embargo, la pertinencia de las acciones implementadas durante el año 2020 para contener la pandemia, generaron diferentes grupos de países según la prevalencia de contagios y muertes. En tal momento, Bolivia, Ecuador y México, presentaban niveles críticos de letalidad. A cierre de 2021, tras la implementación de los planes de vacunación, Argentina, Brasil, Canadá, Chile, Colombia, Costa Rica, Cuba, Panamá, Estados Unidos y Uruguay registran más del 60% de su población con el esquema de vacunación completo.
19Meteorological drought indicators are commonly used for agricultural drought contingency planning in 20Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by 21 meteorological droughts, the timing and duration of agricultural droughts need not coincide with the 22 onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can 23 persist even after the cessation of meteorological droughts due to delayed hydrologic processes. 24Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An 25 evaluation framework was developed to compare meteorological and agricultural droughts using a suite 26 of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand 27 the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-28 2, SPEI-2 and Palmer Z-index to assess intra-seasonal droughts and between SPI-6, SPEI-6 and PDSI for full-29 season evaluations. SPI was seen to correlate well with selected agricultural drought indicators but did 30 not explain all the variability noted in agricultural droughts. The relationships between meteorological 31 and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited 32to predict non-agricultural drought states more so than agricultural drought states. Differences between 33 agricultural and meteorological droughts must be accounted for better drought-preparedness planning. 34 blue water) causing hydrologic droughts. The relationships between meteorological, agricultural and 1 hydrological droughts are not always straightforward. The onset and cessation of agricultural and 2 hydrological droughts do not typically coincide with meteorological droughts as the former are affected 3 by other factors (e.g., soil and watershed characteristics) that control the rate of water movement and 4 storage in soil, surface water, and groundwater compartments [6]. 5Understanding the relationship between meteorological and agricultural droughts is important for proper 6 drought contingency planning in rural areas of Ethiopia. As most of the agriculture is rainfed, a strong 7 correlation between meteorological and agricultural drought is to be expected. However, meteorological 8 and agricultural droughts need not be coincident nor the relationships between these two types of 9 drought be perfect or even strong. The soil moisture at any time can be affected by precipitation in 10 previous months or seasons and is also affected by other factors including but not limited to soil type and 11 atmospheric temperature. In Ethiopia, while many farmers grow crops during the Meher growing season 12 that coincides with the longer Kerimt (June -October) rainy season, the shorter Belg (February -May) 13 rains often provides the soil moisture necessary for tillage and planting activities and also improve 14 pastures for livestock [7]. Therefore, lagged re...
En este estudio, se analizan los últimos datos del Índice de Pobreza Multidimensional (IPM) en Colombia, publicados por el Departamento Administrativo Nacional de Estadística (DANE); examinando privaciones en los hogares del país y variables sociodemográficas de las personas que los integran. Se busca caracterizar los factores condicionantes de la pobreza según regiones, a través de un análisis cuantitativo de carácter descriptivo y mediante la aplicación de la técnica multivariante: Análisis de Componentes Principales (PCA). Los resultados obtenidos muestran que variables como el número de integrantes del hogar, su logro educativo y el acceso al empleo formal, determinan diferencias considerables entre los hogares considerados como pobres y no pobres. Adicionalmente, se observa preponderancia de circunstancias de pobreza en las regiones costeras del país y la región amazónica.
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