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10.1136/bmjgh-2021-005542 http://scihub22266oqcxt.onion/10.1136/bmjgh-2021-005542 33958393!8103560!33958393     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 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 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMJ+Glob+Health 2021 ; 6 (5): ä Nephropedia Template TP {{tp|p=33958393|t=2021. Challenges in reported COVID-19 data: best practices and recommendations for future epidemics |pdf=|usr=}}{{33958393}} gab.com Text Challenges in reported COVID-19 data: best practices and recommendations for future epidemics JO: BMJ Glob Health http://www.kidney.de/pget.php?&tw=1&pmid=33958393 #FOAvip The proliferation of composite data sources tracking the COVID-19 pandemic emphasises the need for such databases during large-scale infectious disease events as well as the potential pitfalls due to the challenges of combining disparate data sources. Multiple organisations have attempted to standardise the compilation of disparate data from multiple sources during the COVID-19 pandemic. However, each composite data source can use a different approach to compile data and address data issues with varying results.We discuss some best practices for researchers endeavouring to create such compilations while discussing three key categories of challenges: (1) data dissemination, which includes discrepant estimates and varying data structures due to multiple agencies and reporting sources generating public health statistics on the same event; (2) data elements, such as date formats and location names, lack standardisation, and differing spatial and temporal resolutions often create challenges when combining sources; and (3) epidemiological factors, including missing data, reporting lags, retrospective data corrections and changes to case definitions that cannot easily be addressed by the data compiler but must be kept in mind when reviewing the data.Efforts to reform the global health data ecosystem should bear such challenges in mind. Standards and best practices should be developed and incorporated to yield more robust, transparent and interoperable data. Since no standards exist yet, we have highlighted key challenges in creating a comprehensive spatiotemporal view of outbreaks from multiple, often discrepant, reporting sources and provided guidelines to address them. In general, we caution against an over-reliance on fully automated systems for integrating surveillance data and strongly advise that epidemiological experts remain engaged in the process of data assessment, integration, validation and interpretation to identify, diagnose and resolve data challenges. Twit Text FOAVip Challenges in reported COVID-19 data: best practices and recommendations for future epidemics ????BMJ Glob Health http://www.kidney.de/pget.php?&tw=1&pmid=33958393 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33958393 #FOAvip English Wikipedia <ref>{{Cite pmid|33958393}}</ref> Challenges in reported COVID-19 data: best practices and recommendations for future epidemics #MMPMID33958393 Badker R; Miller K; Pardee C; Oppenheim B; Stephenson N; Ash B; Philippsen T; Ngoon C; Savage P; Lam C; Madhav N BMJ Glob Health 2021[May]; 6 (5): ä PMID33958393show ga The proliferation of composite data sources tracking the COVID-19 pandemic emphasises the need for such databases during large-scale infectious disease events as well as the potential pitfalls due to the challenges of combining disparate data sources. Multiple organisations have attempted to standardise the compilation of disparate data from multiple sources during the COVID-19 pandemic. However, each composite data source can use a different approach to compile data and address data issues with varying results.We discuss some best practices for researchers endeavouring to create such compilations while discussing three key categories of challenges: (1) data dissemination, which includes discrepant estimates and varying data structures due to multiple agencies and reporting sources generating public health statistics on the same event; (2) data elements, such as date formats and location names, lack standardisation, and differing spatial and temporal resolutions often create challenges when combining sources; and (3) epidemiological factors, including missing data, reporting lags, retrospective data corrections and changes to case definitions that cannot easily be addressed by the data compiler but must be kept in mind when reviewing the data.Efforts to reform the global health data ecosystem should bear such challenges in mind. Standards and best practices should be developed and incorporated to yield more robust, transparent and interoperable data. Since no standards exist yet, we have highlighted key challenges in creating a comprehensive spatiotemporal view of outbreaks from multiple, often discrepant, reporting sources and provided guidelines to address them. In general, we caution against an over-reliance on fully automated systems for integrating surveillance data and strongly advise that epidemiological experts remain engaged in the process of data assessment, integration, validation and interpretation to identify, diagnose and resolve data challenges. |*COVID-19[MESH] |*Research Design[MESH]Challenges in reported COVID-19 data: best practices and recommendatio(epmc).pdf DeepDyve Pubget Overpricing