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10.1098/rstb.2020.0283 http://scihub22266oqcxt.onion/10.1098/rstb.2020.0283 34053260!8165604!34053260     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Philos+Trans+R+Soc+Lond+B+Biol+Sci 2021 ; 376 (1829): 20200283 Nephropedia Template TP {{tp|p=34053260|t=2021. Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of COVID-19 in England |pdf=|usr=}}{{34053260}} gab.com Text Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of COVID-19 in England JO: Philos Trans R Soc Lond B Biol Sci http://www.kidney.de/pget.php?&tw=1&pmid=34053260 #FOAvip The time-varying reproduction number (R(t): the average number of secondary infections caused by each infected person) may be used to assess changes in transmission potential during an epidemic. While new infections are not usually observed directly, they can be estimated from data. However, data may be delayed and potentially biased. We investigated the sensitivity of R(t) estimates to different data sources representing COVID-19 in England, and we explored how this sensitivity could track epidemic dynamics in population sub-groups. We sourced public data on test-positive cases, hospital admissions and deaths with confirmed COVID-19 in seven regions of England over March through August 2020. We estimated R(t) using a model that mapped unobserved infections to each data source. We then compared differences in R(t) with the demographic and social context of surveillance data over time. Our estimates of transmission potential varied for each data source, with the relative inconsistency of estimates varying across regions and over time. R(t) estimates based on hospital admissions and deaths were more spatio-temporally synchronous than when compared to estimates from all test positives. We found these differences may be linked to biased representations of subpopulations in each data source. These included spatially clustered testing, and where outbreaks in hospitals, care homes, and young age groups reflected the link between age and severity of the disease. We highlight that policy makers could better target interventions by considering the source populations of R(t) estimates. Further work should clarify the best way to combine and interpret R(t) estimates from different data sources based on the desired use. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. Twit Text FOAVip Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of COVID-19 in England ????Philos Trans R Soc Lond B Biol Sci http://www.kidney.de/pget.php?&tw=1&pmid=34053260 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34053260 #FOAvip English Wikipedia <ref>{{Cite pmid|34053260}}</ref> Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of COVID-19 in England #MMPMID34053260 Sherratt K; Abbott S; Meakin SR; Hellewell J; Munday JD; Bosse N; Jit M; Funk S Philos Trans R Soc Lond B Biol Sci 2021[Jul]; 376 (1829): 20200283 PMID34053260show ga The time-varying reproduction number (R(t): the average number of secondary infections caused by each infected person) may be used to assess changes in transmission potential during an epidemic. While new infections are not usually observed directly, they can be estimated from data. However, data may be delayed and potentially biased. We investigated the sensitivity of R(t) estimates to different data sources representing COVID-19 in England, and we explored how this sensitivity could track epidemic dynamics in population sub-groups. We sourced public data on test-positive cases, hospital admissions and deaths with confirmed COVID-19 in seven regions of England over March through August 2020. We estimated R(t) using a model that mapped unobserved infections to each data source. We then compared differences in R(t) with the demographic and social context of surveillance data over time. Our estimates of transmission potential varied for each data source, with the relative inconsistency of estimates varying across regions and over time. R(t) estimates based on hospital admissions and deaths were more spatio-temporally synchronous than when compared to estimates from all test positives. We found these differences may be linked to biased representations of subpopulations in each data source. These included spatially clustered testing, and where outbreaks in hospitals, care homes, and young age groups reflected the link between age and severity of the disease. We highlight that policy makers could better target interventions by considering the source populations of R(t) estimates. Further work should clarify the best way to combine and interpret R(t) estimates from different data sources based on the desired use. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. |*Pandemics[MESH] |Bias[MESH] |COVID-19/*epidemiology/prevention & control/transmission/virology[MESH] |England/epidemiology[MESH] |Humans[MESH]Exploring surveillance data biases when estimating the reproduction nu(epmc).pdf DeepDyve Pubget Overpricing