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10.1371/journal.pcbi.1009210 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1009210 34252078!8297945!34252078     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+Comput+Biol 2021 ; 17 (7): e1009210 Nephropedia Template TP {{tp|p=34252078|t=2021. Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic |pdf=|usr=}}{{34252078}} gab.com Text Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34252078 #FOAvip Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve. Twit Text FOAVip Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34252078 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34252078 #FOAvip English Wikipedia <ref>{{Cite pmid|34252078}}</ref> Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic #MMPMID34252078 Li T; White LF PLoS Comput Biol 2021[Jul]; 17 (7): e1009210 PMID34252078show ga Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve. |*Databases, Factual[MESH] |*Models, Statistical[MESH] |Algorithms[MESH] |Bayes Theorem[MESH] |COVID-19/*epidemiology[MESH] |Computational Biology/*methods[MESH] |Computer Simulation[MESH] |Humans[MESH] |Pandemics[MESH]Bayesian back-calculation and nowcasting for line list data during the(epmc).pdf DeepDyve Pubget Overpricing