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10.1016/j.scitotenv.2020.144114 http://scihub22266oqcxt.onion/10.1016/j.scitotenv.2020.144114 33360131!7834887!33360131     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 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       Sci+Total+Environ 2021 ; 761 (ä): 144114 Nephropedia Template TP {{tp|p=33360131|t=2021. How do social media and individual behaviors affect epidemic transmission and control?|pdf=|usr=}}{{33360131}} gab.com Text How do social media and individual behaviors affect epidemic transmission and control JO: Sci Total Environ http://www.kidney.de/pget.php?&tw=1&pmid=33360131 #FOAvip In the outbreak of infectious diseases such as COVID-19, social media channels are important tools for the public to obtain information and form their opinions on infection risk, which can affect their disease prevention behaviors and the consequent disease transmission processes. However, there has been a lack of theoretical investigation into how social media and human behaviors jointly affect the spread of infectious diseases. In this study, we develop an agent-based modeling framework that couples (1) a general opinion dynamics model that describes how individuals form their opinions on epidemic risk with various information sources, (2) a behavioral adoption model that simulates the adoption of disease prevention behaviors, and (3) an epidemiological SEIR model that simulates the spread of diseases in a host population. Through simulating the spread of a coronavirus-like disease in a hypothetical residential area, the modeling results show that social media can make a community more sensitive to external drivers. Social media can increase the public's awareness of infection risk, which is beneficial for epidemic containment, when high-quality epidemic information exists at the early stage of pandemics. However, fabricated and fake news on social media, after a "latent period", can lead to a significant increase in infection rate. The modeling results provide scientific evidence for the intricate interplay between social media and human behaviors in epidemic dynamics and control, and highlight the importance of public education to promote behavioral changes and the need to correct misinformation and fake news on social media in a timely manner. Twit Text FOAVip How do social media and individual behaviors affect epidemic transmission and control ????Sci Total Environ http://www.kidney.de/pget.php?&tw=1&pmid=33360131 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33360131 #FOAvip English Wikipedia <ref>{{Cite pmid|33360131}}</ref> How do social media and individual behaviors affect epidemic transmission and control? #MMPMID33360131 Du E; Chen E; Liu J; Zheng C Sci Total Environ 2021[Mar]; 761 (ä): 144114 PMID33360131show ga In the outbreak of infectious diseases such as COVID-19, social media channels are important tools for the public to obtain information and form their opinions on infection risk, which can affect their disease prevention behaviors and the consequent disease transmission processes. However, there has been a lack of theoretical investigation into how social media and human behaviors jointly affect the spread of infectious diseases. In this study, we develop an agent-based modeling framework that couples (1) a general opinion dynamics model that describes how individuals form their opinions on epidemic risk with various information sources, (2) a behavioral adoption model that simulates the adoption of disease prevention behaviors, and (3) an epidemiological SEIR model that simulates the spread of diseases in a host population. Through simulating the spread of a coronavirus-like disease in a hypothetical residential area, the modeling results show that social media can make a community more sensitive to external drivers. Social media can increase the public's awareness of infection risk, which is beneficial for epidemic containment, when high-quality epidemic information exists at the early stage of pandemics. However, fabricated and fake news on social media, after a "latent period", can lead to a significant increase in infection rate. The modeling results provide scientific evidence for the intricate interplay between social media and human behaviors in epidemic dynamics and control, and highlight the importance of public education to promote behavioral changes and the need to correct misinformation and fake news on social media in a timely manner. |*COVID-19[MESH] |*Coronavirus Infections/epidemiology[MESH] |*Social Media[MESH] |Humans[MESH] |Pandemics[MESH]How do social media and individual behaviors affect epidemic transmiss(epmc).pdf DeepDyve Pubget Overpricing