Análise de impacto dos fatores associados ao combate e contenção do SARS-CoV-2 utilizando redes bayesianas
The Covid-19 pandemic is caused by the severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2. Alerted by the Chinese government in a press release of 2020, the virus which was first identified in Wuhan, China, has caused global panic as well as a race against time to find plausible methods...
| Principais autores: | Ferreira, Caio, Soares, Vinicios Roberto |
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| Formato: | Trabalho de Conclusão de Curso (Graduação) |
| Idioma: | Português |
| Publicado em: |
Universidade Tecnológica Federal do Paraná
2022
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| Assuntos: | |
| Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/29085 |
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| Resumo: |
The Covid-19 pandemic is caused by the severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2. Alerted by the Chinese government in a press release of 2020, the virus which was first identified in Wuhan, China, has caused global panic as well as a race against time to find plausible methods to curb the spread of the disease. On March, 2020, based on these data, WHO, World Health Organization, officially stated that the virus was elevated to pandemic level. The main objective of this present work is to enumerate these methods in data and, via a Bayesian network, which uses the relationship between events prior to an already known subsequent event - which in this case will be the mortality that the disease causes as an end-event - to identify which methods have a direct relationship with the effectiveness in minimizing deaths. The utilized data will be used in relation to the countries of Brazil, Chile, Germany, Finland and the United States of America. As results, it was found that in the absence of a specific vaccine, the most effective policy is based on lockdown. Even with the vaccine available, giving up other public health policies is not the optimal action to prevent a new outbreak. |
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