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10.1038/s41467-020-20687-y http://scihub22266oqcxt.onion/10.1038/s41467-020-20687-y 33462211!7814036!33462211     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2021 ; 12 (1): 418 Nephropedia Template TP {{tp|p=33462211|t=2021. Mathematical model of COVID-19 intervention scenarios for Sao Paulo-Brazil |pdf=|usr=}}{{33462211}} gab.com Text Mathematical model of COVID-19 intervention scenarios for Sao Paulo-Brazil JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=33462211 #FOAvip With COVID-19 surging across the world, understanding the effectiveness of intervention strategies on transmission dynamics is of primary global health importance. Here, we develop and analyze an epidemiological compartmental model using multi-objective genetic algorithm design optimization to compare scenarios related to strategy type, the extent of social distancing, time window, and personal protection levels on the transmission dynamics of COVID-19 in Sao Paulo, Brazil. The results indicate that the optimal strategy for Sao Paulo is to reduce social distancing over time with a stepping-down reduction in the magnitude of social distancing every 80-days. Our results also indicate that the ability to reduce social distancing depends on a 5-10% increase in the current percentage of people strictly following protective guidelines, highlighting the importance of protective behavior in controlling the pandemic. Our framework can be extended to model transmission dynamics for other countries, regions, states, cities, and organizations. Twit Text FOAVip Mathematical model of COVID-19 intervention scenarios for Sao Paulo-Brazil ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=33462211 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33462211 #FOAvip English Wikipedia <ref>{{Cite pmid|33462211}}</ref> Mathematical model of COVID-19 intervention scenarios for Sao Paulo-Brazil #MMPMID33462211 Pinto Neto O; Kennedy DM; Reis JC; Wang Y; Brizzi ACB; Zambrano GJ; de Souza JM; Pedroso W; de Mello Pedreiro RC; de Matos Brizzi B; Abinader EO; Zangaro RA Nat Commun 2021[Jan]; 12 (1): 418 PMID33462211show ga With COVID-19 surging across the world, understanding the effectiveness of intervention strategies on transmission dynamics is of primary global health importance. Here, we develop and analyze an epidemiological compartmental model using multi-objective genetic algorithm design optimization to compare scenarios related to strategy type, the extent of social distancing, time window, and personal protection levels on the transmission dynamics of COVID-19 in Sao Paulo, Brazil. The results indicate that the optimal strategy for Sao Paulo is to reduce social distancing over time with a stepping-down reduction in the magnitude of social distancing every 80-days. Our results also indicate that the ability to reduce social distancing depends on a 5-10% increase in the current percentage of people strictly following protective guidelines, highlighting the importance of protective behavior in controlling the pandemic. Our framework can be extended to model transmission dynamics for other countries, regions, states, cities, and organizations. |*Algorithms[MESH] |*Models, Theoretical[MESH] |Brazil/epidemiology[MESH] |COVID-19/epidemiology/*prevention & control/virology[MESH] |Cities[MESH] |Global Health[MESH] |Humans[MESH] |Pandemics[MESH] |Physical Distancing[MESH] |SARS-CoV-2/*isolation & purification/physiology[MESH]Mathematical model of COVID-19 intervention scenarios for Sao Paulo-Br(epmc).pdf DeepDyve Pubget Overpricing