Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa
ES/1PE/MTM2014-51992-R ES/1PE/MTM2016-77501-P ES/2PE/MTM2017-82553-R
Risk maps of dengue disease offer to the public health officers a tool to model disease risk in space and time. We analyzed the geographical distribution of relative incidence risk of dengue disease in a high incidence city from Colombia, and its evolution in time during the period January 2009—December 2015, identifying regional effects at different levels of spatial aggregations. Cases of dengu ... [++]
Risk maps of dengue disease offer to the public health officers a tool to model disease risk in space and time. We analyzed the geographical distribution of relative incidence risk of dengue disease in a high incidence city from Colombia, and its evolution in time during the period January 2009—December 2015, identifying regional effects at different levels of spatial aggregations. Cases of dengue disease were geocoded and spatially allocated to census sectors, and temporally aggregated by epidemiological periods. The census sectors are nested in administrative divisions defined as communes, configuring two levels of spatial aggregation for the dengue cases. Spatio-temporal models including census sector and commune-level spatially structured random effects were fitted to estimate dengue incidence relative risks using the integrated nested Laplace approximation (INLA) technique. The final selected model included two-level spatial random effects, a global structured temporal random effect, and a census sector-level interaction term. Risk maps by epidemiological period and risk profiles by census sector were generated from the modeling process, showing the transmission dynamics of the disease. All the census sectors in the city displayed high risk at some epidemiological period in the outbreak periods. Relative risk estimation of dengue disease using INLA offered a quick and powerful method for parameter estimation and inference. [--]
Public Library of Science
PLoS ONE, 2018, 13(9): e0203382
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas / Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematikak Saila / Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. InaMat - Institute for Advanced Materials
This work was supported by grants from the Spanish Ministry of Economy and Competitiveness (projects MTM2014-51992-R-MDU- and MTM2016-77501-P -ALQ-, jointly financed with the European Regional Development Fund), the Spanish Ministry of Economy, Industry, and Competitiveness (MTM2017-82553-R jointly financed with the European Regional Development Fund (FEDER). MDU, AA), and the Colombian Administrative Department of Science and Technology (grant 646-2014 for doctoral studies abroad) DAMB.
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