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10.1038/s41598-021-89517-5 http://scihub22266oqcxt.onion/10.1038/s41598-021-89517-5 33976342!8113491!33976342     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 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       Sci+Rep 2021 ; 11 (1): 10024 Nephropedia Template TP {{tp|p=33976342|t=2021. Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina |pdf=|usr=}}{{33976342}} gab.com Text Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33976342 #FOAvip We have studied the dynamic evolution of the Covid-19 pandemic in Argentina. The marked heterogeneity in population density and the very extensive geography of the country becomes a challenge itself. Standard compartment models fail when they are implemented in the Argentina case. We extended a previous successful model to describe the geographical spread of the AH1N1 influenza epidemic of 2009 in two essential ways: we added a stochastic local mobility mechanism, and we introduced a new compartment in order to take into account the isolation of infected asymptomatic detected people. Two fundamental parameters drive the dynamics: the time elapsed between contagious and isolation of infected individuals ([Formula: see text]) and the ratio of people isolated over the total infected ones (p). The evolution is more sensitive to the [Formula: see text]parameter. The model not only reproduces the real data but also predicts the second wave before the former vanishes. This effect is intrinsic of extensive countries with heterogeneous population density and interconnection.The model presented has proven to be a reliable predictor of the effects of public policies as, for instance, the unavoidable vaccination campaigns starting at present in the world an particularly in Argentina. Twit Text FOAVip Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33976342 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33976342 #FOAvip English Wikipedia <ref>{{Cite pmid|33976342}}</ref> Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina #MMPMID33976342 Barreiro NL; Govezensky T; Bolcatto PG; Barrio RA Sci Rep 2021[May]; 11 (1): 10024 PMID33976342show ga We have studied the dynamic evolution of the Covid-19 pandemic in Argentina. The marked heterogeneity in population density and the very extensive geography of the country becomes a challenge itself. Standard compartment models fail when they are implemented in the Argentina case. We extended a previous successful model to describe the geographical spread of the AH1N1 influenza epidemic of 2009 in two essential ways: we added a stochastic local mobility mechanism, and we introduced a new compartment in order to take into account the isolation of infected asymptomatic detected people. Two fundamental parameters drive the dynamics: the time elapsed between contagious and isolation of infected individuals ([Formula: see text]) and the ratio of people isolated over the total infected ones (p). The evolution is more sensitive to the [Formula: see text]parameter. The model not only reproduces the real data but also predicts the second wave before the former vanishes. This effect is intrinsic of extensive countries with heterogeneous population density and interconnection.The model presented has proven to be a reliable predictor of the effects of public policies as, for instance, the unavoidable vaccination campaigns starting at present in the world an particularly in Argentina. |*Models, Theoretical[MESH] |Argentina/epidemiology[MESH] |Asymptomatic Diseases/*epidemiology[MESH] |COVID-19/*epidemiology/virology[MESH] |Humans[MESH] |Pandemics[MESH]Detecting infected asymptomatic cases in a stochastic model for spread(epmc).pdf DeepDyve Pubget Overpricing