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10.1371/journal.pone.0236386 http://scihub22266oqcxt.onion/10.1371/journal.pone.0236386 32735581!7394373!32735581     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       PLoS+One 2020 ; 15 (7): e0236386 Nephropedia Template TP {{tp|p=32735581|t=2020. A data-driven model to describe and forecast the dynamics of COVID-19 transmission |pdf=|usr=}}{{32735581}} gab.com Text A data-driven model to describe and forecast the dynamics of COVID-19 transmission JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32735581 #FOAvip This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions. Twit Text FOAVip A data-driven model to describe and forecast the dynamics of COVID-19 transmission ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32735581 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32735581 #FOAvip English Wikipedia <ref>{{Cite pmid|32735581}}</ref> A data-driven model to describe and forecast the dynamics of COVID-19 transmission #MMPMID32735581 Paiva HM; Afonso RJM; de Oliveira IL; Garcia GF PLoS One 2020[]; 15 (7): e0236386 PMID32735581show ga This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions. |*Betacoronavirus[MESH] |*Models, Theoretical[MESH] |Asymptomatic Diseases[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Community-Acquired Infections[MESH] |Coronavirus Infections/*epidemiology/mortality/*transmission/virology[MESH] |Cross Infection[MESH] |Data Accuracy[MESH] |Europe/epidemiology[MESH] |Forecasting/*methods[MESH] |Hospitalization[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/mortality/*transmission/virology[MESH] |SARS-CoV-2[MESH]A data-driven model to describe and forecast the dynamics of COVID-19 (epmc).pdf DeepDyve Pubget Overpricing