Publication
SARS-CoV-2: sir model limitations and predictive constraints
| dc.contributor.author | Telles, Charles Roberto | |
| dc.contributor.author | Lopes, Henrique | |
| dc.contributor.author | Franco, Diogo | |
| dc.date.accessioned | 2021-04-28T13:59:54Z | |
| dc.date.available | 2021-04-28T13:59:54Z | |
| dc.date.issued | 2021-04 | |
| dc.description.abstract | Background: The main purpose of this research is to describe the mathematical asymmetric patterns of susceptible, infectious, or recovered (SIR) model equation application in the light of coronavirus disease 2019 (COVID-19) skewness patterns worldwide. Methods: The research mod-eled severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spreading and dissemination patterns sensitivity by redesigning time series data extraction of daily new cases in terms of deviation consistency concerning variables that sustain COVID-19 transmission. The approach opened a new scenario where seasonality forcing behavior was introduced to understand SARS-COV-2 non-linear dynamics due to heterogeneity and confounding epidemics scenarios. Results: The main research results are the elucidation of three birth-and death-forced seasonality persistence phases that can explain COVID-19 skew patterns worldwide. They are presented in the following order: (1) the environmental variables (Earth seasons and atmospheric conditions); (2) health policies and adult learning education (HPALE) interventions; (3) urban spaces (local indoor and outdoor spaces for transit and social-cultural interactions, public or private, with natural physical features (river, lake, terrain). Conclusions: Three forced seasonality phases (positive to negative skew) phases were pointed out as a theoretical framework to explain uncertainty found in the predictive SIR model equations that might diverge in outcomes expected to express the disease’s behaviour. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/sym13040676 | pt_PT |
| dc.identifier.eid | 85104131241 | |
| dc.identifier.issn | 2073-8994 | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/32799 | |
| dc.identifier.wos | 000643660600001 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Confounding variables | pt_PT |
| dc.subject | COVID-19 seasonality | pt_PT |
| dc.subject | Forced seasonality | pt_PT |
| dc.subject | Mathematical modeling | pt_PT |
| dc.subject | S.I.R. models | pt_PT |
| dc.subject | Uncertainty | pt_PT |
| dc.title | SARS-CoV-2: sir model limitations and predictive constraints | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 4 | pt_PT |
| oaire.citation.title | Symmetry | pt_PT |
| oaire.citation.volume | 13 | pt_PT |
| person.familyName | ROBERTO TELLES | |
| person.givenName | CHARLES | |
| person.identifier.orcid | 0000-0001-9624-2423 | |
| person.identifier.rid | B-7046-2018 | |
| person.identifier.scopus-author-id | 57204906343 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 1887a21f-42a5-40cb-95aa-e50142c2f375 | |
| relation.isAuthorOfPublication.latestForDiscovery | 1887a21f-42a5-40cb-95aa-e50142c2f375 |
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