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10.1016/j.ijmedinf.2021.104400 http://scihub22266oqcxt.onion/10.1016/j.ijmedinf.2021.104400 33667930!7843148!33667930     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Med+Inform 2021 ; 149 (ä): 104400 Nephropedia Template TP {{tp|p=33667930|t=2021. Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic |pdf=|usr=}}{{33667930}} gab.com Text Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic JO: Int J Med Inform http://www.kidney.de/pget.php?&tw=1&pmid=33667930 #FOAvip Introduction The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. Objectives The primary objective of this study was to develop a real-time geospatial surveillance system to monitor the spread of COVID-19 across the UK. Methods Using self-reported app data and the Secure Anonymised Information Linkage (SAIL) Databank, we demonstrate the use of sophisticated spatial modelling for near-real-time prediction of COVID-19 prevalence at small-area resolution to inform strategic government policy areas. Results We demonstrate that using a combination of crowd-sourced app data and sophisticated geo-statistical techniques it is possible to predict hot spots of COVID-19 at fine geographic scales, nationally. We are also able to produce estimates of their precision, which is an important pre-requisite to an effective control strategy to guard against over-reaction to potentially spurious features of 'best guess' predictions. Conclusion In the UK, important emerging risk-factors such as social deprivation or ethnicity vary over small distances, hence risk needs to be modelled at fine spatial resolution to avoid aggregation bias. We demonstrate that existing geospatial statistical methods originally developed for global health applications are well-suited to this task and can be used in an anonymised databank environment, thus preserving the privacy of the individuals who contribute their data. Twit Text FOAVip Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic ????Int J Med Inform http://www.kidney.de/pget.php?&tw=1&pmid=33667930 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33667930 #FOAvip English Wikipedia <ref>{{Cite pmid|33667930}}</ref> Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic #MMPMID33667930 Fry R; Hollinghurst J; Stagg HR; Thompson DA; Fronterre C; Orton C; Lyons RA; Ford DV; Sheikh A; Diggle PJ Int J Med Inform 2021[May]; 149 (ä): 104400 PMID33667930show ga Introduction The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. Objectives The primary objective of this study was to develop a real-time geospatial surveillance system to monitor the spread of COVID-19 across the UK. Methods Using self-reported app data and the Secure Anonymised Information Linkage (SAIL) Databank, we demonstrate the use of sophisticated spatial modelling for near-real-time prediction of COVID-19 prevalence at small-area resolution to inform strategic government policy areas. Results We demonstrate that using a combination of crowd-sourced app data and sophisticated geo-statistical techniques it is possible to predict hot spots of COVID-19 at fine geographic scales, nationally. We are also able to produce estimates of their precision, which is an important pre-requisite to an effective control strategy to guard against over-reaction to potentially spurious features of 'best guess' predictions. Conclusion In the UK, important emerging risk-factors such as social deprivation or ethnicity vary over small distances, hence risk needs to be modelled at fine spatial resolution to avoid aggregation bias. We demonstrate that existing geospatial statistical methods originally developed for global health applications are well-suited to this task and can be used in an anonymised databank environment, thus preserving the privacy of the individuals who contribute their data. |*COVID-19[MESH] |Disease Outbreaks[MESH] |Humans[MESH] |Pandemics[MESH] |SARS-CoV-2[MESH]Real-time spatial health surveillance: Mapping the UK COVID-19 epidemi(epmc).pdf DeepDyve Pubget Overpricing