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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7533| Computational modelling of COVID-19: A study of compliance and superspreaders | |
| Faith Lee María Pérez-Ortiz John Shawe_Taylor | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2021.05.12.21257079 | |
| https://www.medrxiv.org/content/10.1101/2021.05.12.21257079v1 | |
| Background The success of social distancing implementations of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) depends heavily on population compliance. Mathematical modelling has been used extensively to assess the rate of viral transmission from behavioural responses. Previous epidemics of SARS-Cov-2 have been characterised by superspreaders, a small number of individuals who transmit a disease to a large group of individuals, who contribute to the stochasticity (or randomness) of transmission compared to other pathogens such as Influenza. This growing evidence proves an urgent matter to understand transmission routes in order to target and combat outbreaks. Objective To investigate the role of superspreaders in the rate of viral transmission with various levels of compliance. | |
| medRxiv and bioRxiv | |
| 15-05-2021 | |
| Preimpreso | |
| www.medrxiv.org | |
| Inglés | |
| Epidemia COVID-19 | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Computational modelling of COVID 19_ A study of compliance and superspreaders.pdf | 1.27 MB | Adobe PDF | Visualizar/Abrir |