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10.4178/epih.e2020045 http://scihub22266oqcxt.onion/10.4178/epih.e2020045 32580532!7644942!32580532     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemiol+Health 2020 ; 42 (ä): e2020045 Nephropedia Template TP {{tp|p=32580532|t=2020. Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation |pdf=|usr=}}{{32580532}} gab.com Text Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation JO: Epidemiol Health http://www.kidney.de/pget.php?&tw=1&pmid=32580532 #FOAvip OBJECTIVE: In 2020, the coronavirus disease 2019 (COVID-19) respiratory infection is spreading in Korea. In order to prevent the spread of an infectious disease, infected people must be quickly identified and isolated, and contact with the infected must be blocked early. This study attempted to verify the intervention effects on the spread of an infectious disease by using these measures in a mathematical model. METHODS: We used the susceptible-infectious-recovery (SIR) model for a virtual population group connected by a special structured network. In the model, the infected state (I) was divided into I in which the infection is undetected and Ix in which the infection is detected. The probability of transitioning from an I state to Ix can be viewed as the rate at which an infected person is found. We assumed that only those connected to each other in the network can cause infection. In addition, this study attempted to evaluate the effects of isolation by temporarily removing the connection among these people. RESULTS: In Scenario 1, only the infected are isolated; in Scenario 2, those who are connected to an infected person and are also found to be infected are isolated as well. In Scenario 3, everyone connected to an infected person are isolated. In Scenario 3, it was possible to effectively suppress the infectious disease even with a relatively slow rate of diagnosis and relatively high infection rate. CONCLUSION: During the epidemic, quick identification of the infected is helpful. In addition, it was possible to quantitatively show through a simulation evaluation that the management of infected individuals as well as those who are connected greatly helped to suppress the spread of infectious diseases. Twit Text FOAVip Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation ????Epidemiol Health http://www.kidney.de/pget.php?&tw=1&pmid=32580532 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32580532 #FOAvip English Wikipedia <ref>{{Cite pmid|32580532}}</ref> Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation #MMPMID32580532 Kwon O; Son WS; Kim JY; Kim JH Epidemiol Health 2020[]; 42 (ä): e2020045 PMID32580532show ga OBJECTIVE: In 2020, the coronavirus disease 2019 (COVID-19) respiratory infection is spreading in Korea. In order to prevent the spread of an infectious disease, infected people must be quickly identified and isolated, and contact with the infected must be blocked early. This study attempted to verify the intervention effects on the spread of an infectious disease by using these measures in a mathematical model. METHODS: We used the susceptible-infectious-recovery (SIR) model for a virtual population group connected by a special structured network. In the model, the infected state (I) was divided into I in which the infection is undetected and Ix in which the infection is detected. The probability of transitioning from an I state to Ix can be viewed as the rate at which an infected person is found. We assumed that only those connected to each other in the network can cause infection. In addition, this study attempted to evaluate the effects of isolation by temporarily removing the connection among these people. RESULTS: In Scenario 1, only the infected are isolated; in Scenario 2, those who are connected to an infected person and are also found to be infected are isolated as well. In Scenario 3, everyone connected to an infected person are isolated. In Scenario 3, it was possible to effectively suppress the infectious disease even with a relatively slow rate of diagnosis and relatively high infection rate. CONCLUSION: During the epidemic, quick identification of the infected is helpful. In addition, it was possible to quantitatively show through a simulation evaluation that the management of infected individuals as well as those who are connected greatly helped to suppress the spread of infectious diseases. |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques/*statistics & numerical data[MESH] |Coronavirus Infections/*diagnosis/epidemiology/*prevention & control/transmission[MESH] |Epidemics/*prevention & control[MESH] |Humans[MESH] |Models, Theoretical[MESH] |Pandemics/*prevention & control[MESH] |Patient Isolation/*statistics & numerical data[MESH] |Pneumonia, Viral/*diagnosis/epidemiology/*prevention & control/transmission[MESH]Intervention effects in the transmission of COVID-19 depending on the (epmc).pdf DeepDyve Pubget Overpricing