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10.1371/journal.pone.0240153 http://scihub22266oqcxt.onion/10.1371/journal.pone.0240153 33007054!7531857!33007054     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (10): e0240153 Nephropedia Template TP {{tp|p=33007054|t=2020. Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis |pdf=|usr=}}{{33007054}} gab.com Text Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33007054 #FOAvip The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes. Twit Text FOAVip Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33007054 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33007054 #FOAvip English Wikipedia <ref>{{Cite pmid|33007054}}</ref> Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis #MMPMID33007054 Eshragh A; Alizamir S; Howley P; Stojanovski E PLoS One 2020[]; 15 (10): e0240153 PMID33007054show ga The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes. |*Models, Statistical[MESH] |Australia/epidemiology[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH]Modeling the dynamics of the COVID-19 population in Australia: A proba(epmc).pdf DeepDyve Pubget Overpricing