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10.1073/pnas.2006520117 http://scihub22266oqcxt.onion/10.1073/pnas.2006520117 32616574!7382213!32616574     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       Proc+Natl+Acad+Sci+U+S+A 2020 ; 117 (29): 16732-16738 Nephropedia Template TP {{tp|p=32616574|t=2020. The challenges of modeling and forecasting the spread of COVID-19 |pdf=|usr=}}{{32616574}} gab.com Text The challenges of modeling and forecasting the spread of COVID-19 JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32616574 #FOAvip The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies. Twit Text FOAVip The challenges of modeling and forecasting the spread of COVID-19 ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32616574 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32616574 #FOAvip English Wikipedia <ref>{{Cite pmid|32616574}}</ref> The challenges of modeling and forecasting the spread of COVID-19 #MMPMID32616574 Bertozzi AL; Franco E; Mohler G; Short MB; Sledge D Proc Natl Acad Sci U S A 2020[Jul]; 117 (29): 16732-16738 PMID32616574show ga The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies. |*Models, Theoretical[MESH] |Betacoronavirus/*pathogenicity[MESH] |COVID-19[MESH] |Coronavirus Infections/epidemiology/*prevention & control/*transmission/virology[MESH] |Humans[MESH] |Infection Control/*methods/*organization & administration[MESH] |Pandemics/*prevention & control[MESH] |Pneumonia, Viral/epidemiology/*prevention & control/*transmission/virology[MESH] |Public Health[MESH] |SARS-CoV-2[MESH]The challenges of modeling and forecasting the spread of COVID-19 (epmc).pdf DeepDyve Pubget Overpricing