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10.1186/s12942-020-00221-5 http://scihub22266oqcxt.onion/10.1186/s12942-020-00221-5 C7336093!7336093 !32631358     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32631358 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Int+J+Health+Geogr 2020 ; 19 (1 ): 25 Nephropedia Template TP {{tp|p=32631358 |t=2020. Geostatistical COVID-19 infection risk maps for Portugal |pdf=|usr=}}{{32631358 }} gab.com Text Geostatistical COVID-19 infection risk maps for Portugal JO: Int J Health Geogr http://www.kidney.de/pget.php?&tw=1&pmid=32631358 #FOAvip The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality's population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time. Twit Text FOAVip Geostatistical COVID-19 infection risk maps for Portugal ????Int J Health Geogr http://www.kidney.de/pget.php?&tw=1&pmid=32631358 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32631358 #FOAvip English Wikipedia <ref>{{Cite pmid|32631358 }}</ref> Geostatistical COVID-19 infection risk maps for Portugal #MMPMID32631358 Azevedo L ; Pereira MJ ; Ribeiro MC ; Soares A Int J Health Geogr 2020[Jul]; 19 (1 ): 25 PMID32631358 show ga The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality's population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time. |*Geographic Mapping [MESH] |*Models, Statistical [MESH] |Algorithms [MESH] |Betacoronavirus [MESH] |COVID-19 [MESH] |Coronavirus Infections/*epidemiology [MESH] |Humans [MESH] |Pandemics [MESH] |Pneumonia, Viral/*epidemiology [MESH] |Portugal/epidemiology [MESH]Geostatistical COVID-19 infection risk maps for Portugal (epmc).pdf DeepDyve Pubget Overpricing