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10.1016/j.mbs.2020.108378 http://scihub22266oqcxt.onion/10.1016/j.mbs.2020.108378 32507746!7229764!32507746     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Math+Biosci 2020 ; 325 (ä): 108378 Nephropedia Template TP {{tp|p=32507746|t=2020. Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability |pdf=|usr=}}{{32507746}} gab.com Text Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability JO: Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=32507746 #FOAvip The emerging coronavirus SARS-CoV-2 has caused a COVID-19 pandemic. SARS-CoV-2 causes a generally mild, but sometimes severe and even life-threatening infection, known as COVID-19. Currently, there exist no effective vaccines or drugs and, as such, global public authorities have so far relied upon non pharmaceutical interventions (NPIs). Since COVID-19 symptoms are aspecific and may resemble a common cold, if it should come back with a seasonal pattern and coincide with the influenza season, this would be particularly challenging, overwhelming and straining the healthcare systems, particularly in resource-limited contexts, and would increase the likelihood of nosocomial transmission. In the present study, we devised a mathematical model focusing on the treatment of people complaining of influenza-like-illness (ILI) symptoms, potentially at risk of contracting COVID-19 or other emerging/re-emerging respiratory infectious agents during their admission at the health-care setting, who will occupy the detection kits causing a severe shortage of testing resources. The model is used to assess the effect of mass influenza vaccination on the spread of COVID-19 and other respiratory pathogens in the case of a coincidence of the outbreak with the influenza season. Here, we show that increasing influenza vaccine uptake or enhancing the public health interventions would facilitate the management of respiratory outbreaks coinciding with the peak flu season, especially, compensate the shortage of the detection resources. However, how to increase influenza vaccination coverage rate remains challenging. Public health decision- and policy-makers should adopt evidence-informed strategies to improve influenza vaccine uptake. Twit Text FOAVip Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability ????Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=32507746 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32507746 #FOAvip English Wikipedia <ref>{{Cite pmid|32507746}}</ref> Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability #MMPMID32507746 Li Q; Tang B; Bragazzi NL; Xiao Y; Wu J Math Biosci 2020[Jul]; 325 (ä): 108378 PMID32507746show ga The emerging coronavirus SARS-CoV-2 has caused a COVID-19 pandemic. SARS-CoV-2 causes a generally mild, but sometimes severe and even life-threatening infection, known as COVID-19. Currently, there exist no effective vaccines or drugs and, as such, global public authorities have so far relied upon non pharmaceutical interventions (NPIs). Since COVID-19 symptoms are aspecific and may resemble a common cold, if it should come back with a seasonal pattern and coincide with the influenza season, this would be particularly challenging, overwhelming and straining the healthcare systems, particularly in resource-limited contexts, and would increase the likelihood of nosocomial transmission. In the present study, we devised a mathematical model focusing on the treatment of people complaining of influenza-like-illness (ILI) symptoms, potentially at risk of contracting COVID-19 or other emerging/re-emerging respiratory infectious agents during their admission at the health-care setting, who will occupy the detection kits causing a severe shortage of testing resources. The model is used to assess the effect of mass influenza vaccination on the spread of COVID-19 and other respiratory pathogens in the case of a coincidence of the outbreak with the influenza season. Here, we show that increasing influenza vaccine uptake or enhancing the public health interventions would facilitate the management of respiratory outbreaks coinciding with the peak flu season, especially, compensate the shortage of the detection resources. However, how to increase influenza vaccination coverage rate remains challenging. Public health decision- and policy-makers should adopt evidence-informed strategies to improve influenza vaccine uptake. |*Coinfection/diagnosis/prevention & control[MESH] |*Communicable Disease Control/statistics & numerical data[MESH] |*Coronavirus Infections/diagnosis/prevention & control[MESH] |*Epidemics/prevention & control[MESH] |*Influenza Vaccines[MESH] |*Influenza, Human/diagnosis/prevention & control[MESH] |*Mass Vaccination/statistics & numerical data[MESH] |*Models, Theoretical[MESH] |*Pandemics/prevention & control[MESH] |*Pneumonia, Viral/diagnosis/prevention & control[MESH] |COVID-19[MESH]Modeling the impact of mass influenza vaccination and public health in(epmc).pdf DeepDyve Pubget Overpricing