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10.1371/journal.pone.0236860 http://scihub22266oqcxt.onion/10.1371/journal.pone.0236860 32726361!7390340!32726361     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 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 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (7): e0236860 Nephropedia Template TP {{tp|p=32726361|t=2020. Estimation of COVID-19 spread curves integrating global data and borrowing information |pdf=|usr=}}{{32726361}} gab.com Text Estimation of COVID-19 spread curves integrating global data and borrowing information JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32726361 #FOAvip Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19. Twit Text FOAVip Estimation of COVID-19 spread curves integrating global data and borrowing information ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32726361 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32726361 #FOAvip English Wikipedia <ref>{{Cite pmid|32726361}}</ref> Estimation of COVID-19 spread curves integrating global data and borrowing information #MMPMID32726361 Lee SY; Lei B; Mallick B PLoS One 2020[]; 15 (7): e0236860 PMID32726361show ga Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19. |*Betacoronavirus[MESH] |Bayes Theorem[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/*transmission/virology[MESH] |Global Health/*trends[MESH] |Humans[MESH] |Models, Theoretical[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/*transmission/virology[MESH] |Prognosis[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Travel[MESH]Estimation of COVID-19 spread curves integrating global data and borro(epmc).pdf DeepDyve Pubget Overpricing