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10.1371/journal.pone.0260973 http://scihub22266oqcxt.onion/10.1371/journal.pone.0260973 34855929!8639064!34855929     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34855929&cmd=llinks): Failed to open stream: HTTP request failed! 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Effect of homophily and correlation of beliefs on COVID-19 and general infectious disease outbreaks |pdf=|usr=}}{{34855929}} gab.com Text Effect of homophily and correlation of beliefs on COVID-19 and general infectious disease outbreaks JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34855929 #FOAvip Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious disease outbreaks despite high population-wide vaccination rates. The epidemiological consequences of homophily regarding other beliefs as well as correlations among beliefs or circumstances are poorly understood, however. Here, we use a simple compartmental disease model as well as a more complex COVID-19 model to study how homophily and correlation of beliefs and circumstances in a social interaction network affect the probability of disease outbreak and COVID-19-related mortality. We find that the current social context, characterized by the presence of homophily and correlations between who vaccinates, who engages in risk reduction, and individual risk status, corresponds to a situation with substantially worse disease burden than in the absence of heterogeneities. In the presence of an effective vaccine, the effects of homophily and correlation of beliefs and circumstances become stronger. Further, the optimal vaccination strategy depends on the degree of homophily regarding vaccination status as well as the relative level of risk mitigation high- and low-risk individuals practice. The developed methods are broadly applicable to any investigation in which node attributes in a graph might reasonably be expected to cluster or exhibit correlations. Twit Text FOAVip Effect of homophily and correlation of beliefs on COVID-19 and general infectious disease outbreaks ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34855929 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34855929 #FOAvip English Wikipedia <ref>{{Cite pmid|34855929}}</ref> Effect of homophily and correlation of beliefs on COVID-19 and general infectious disease outbreaks #MMPMID34855929 Kadelka C; McCombs A PLoS One 2021[]; 16 (12): e0260973 PMID34855929show ga Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious disease outbreaks despite high population-wide vaccination rates. The epidemiological consequences of homophily regarding other beliefs as well as correlations among beliefs or circumstances are poorly understood, however. Here, we use a simple compartmental disease model as well as a more complex COVID-19 model to study how homophily and correlation of beliefs and circumstances in a social interaction network affect the probability of disease outbreak and COVID-19-related mortality. We find that the current social context, characterized by the presence of homophily and correlations between who vaccinates, who engages in risk reduction, and individual risk status, corresponds to a situation with substantially worse disease burden than in the absence of heterogeneities. In the presence of an effective vaccine, the effects of homophily and correlation of beliefs and circumstances become stronger. Further, the optimal vaccination strategy depends on the degree of homophily regarding vaccination status as well as the relative level of risk mitigation high- and low-risk individuals practice. The developed methods are broadly applicable to any investigation in which node attributes in a graph might reasonably be expected to cluster or exhibit correlations. |*Attitude to Health[MESH] |*Disease Outbreaks[MESH] |*Social Interaction[MESH] |Adult[MESH] |Age Factors[MESH] |Aged[MESH] |COVID-19 Vaccines/therapeutic use[MESH] |COVID-19/epidemiology/prevention & control/*psychology[MESH] |Humans[MESH] |Middle Aged[MESH] |Social Identification[MESH] |Social Networking[MESH]Effect of homophily and correlation of beliefs on COVID-19 and general(epmc).pdf DeepDyve Pubget Overpricing