Publicação
Predicting the evolution and control of the COVID-19 pandemic in Portugal
| datacite.subject.fos | Ciências Médicas | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Pais, Ricardo J. | |
| dc.contributor.author | Taveira, Nuno | |
| dc.date.accessioned | 2025-07-30T14:36:09Z | |
| dc.date.available | 2025-07-30T14:36:09Z | |
| dc.date.issued | 2020-09 | |
| dc.description.abstract | Coronavirus disease 2019 (COVID-19) is a worldwide pandemic that has been affecting Portugal since 2 March 2020. The Portuguese government has been making efforts to contradict the exponential growth through lockdown, social distancing and the usage of masks. However, these measures have been implemented without controlling the compliance degree and how much is necessary to achieve an effective control. To address this issue, we developed a mathematical model to estimate the strength of Government-Imposed Measures (GIM) and predict the impact of the degree of compliance on the number of infected cases and peak of infection. We estimate the peak to be around 650 thousand infected cases with 53 thousand requiring hospital care by the beginning of May if no measures were taken. The model shows that the population compliance of the GIM was gradual between 30% to 75%, contributing to a significant reduction on the infection peak and mortality. Importantly, our simulations show that the infection burden could have been further reduced if the population followed the GIM immediately after their release on 18 March. | eng |
| dc.identifier.citation | Pais RJ and Taveira N. Predicting the evolution and control of the COVID-19 pandemic in Portugal [version 2; peer review: 2 approved]. F1000Research 2020, 9:283 (https://doi.org/10.12688/f1000research.23401.2) | |
| dc.identifier.doi | 10.12688/f1000research.23401.2 | |
| dc.identifier.issn | 2046-1402 | |
| dc.identifier.uri | http://hdl.handle.net/10400.26/58287 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | F1000 Research | |
| dc.relation.hasversion | https://doi.org/10.12688/f1000research.23401.2 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | COVID-19 | |
| dc.subject | Pandemic Control | |
| dc.subject | Predictive modeling | |
| dc.subject | Simulation | |
| dc.subject | Social isolation | |
| dc.subject | Mathematical model | |
| dc.title | Predicting the evolution and control of the COVID-19 pandemic in Portugal | eng |
| dc.type | contribution to journal | |
| dspace.entity.type | Publication | |
| oaire.citation.startPage | 283 | |
| oaire.citation.title | F1000 Research | |
| oaire.citation.volume | 9 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |