| dc.creator | Nieto-Chaupis, Huber | |
| dc.date.accessioned | 2022-02-25T01:14:21Z | |
| dc.date.accessioned | 2023-05-30T23:11:38Z | |
| dc.date.available | 2022-02-25T01:14:21Z | |
| dc.date.available | 2023-05-30T23:11:38Z | |
| dc.date.created | 2022-02-25T01:14:21Z | |
| dc.date.issued | 2021-08-19 | |
| dc.identifier | Nieto-Chaupis, H. (2021, July). Anticipating Subsequent Waves from First Wave Parameters in the Ongoing Covid-19 Pandemic. In 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4) (pp. 260-265). IEEE. | |
| dc.identifier | 978-1-6654-0096-1 | |
| dc.identifier | https://hdl.handle.net/20.500.13067/1664 | |
| dc.identifier | 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4) | |
| dc.identifier | https://doi.org/10.1109/WorldS451998.2021.9514033 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/6473097 | |
| dc.description.abstract | This paper focuses on the mathematical construction of a model that describes the statistical properties of a second wave of infections by Corona Virus Disease 2019 (Covid-19 in short) from the information of a first one. Basically this study is done having as grounds a topological model based at rectangles. Thus, perimeters and distances between rectangles might be encompassed to a real data through valid approximations. A full trapezoid model is also proposed. The two-rectangles model appears that fits well to the Philippines covid-19 data. It is seen that while both rectangles are pretty separated, the the peak of second wave turns out to be high. From this an exponential formulation is derived, and fits well the exponential morphology as seen in Covid-19 data France. | |
| dc.language | eng | |
| dc.publisher | Institute of Electrical and Electronics Engineers | |
| dc.publisher | PE | |
| dc.relation | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114477026&doi=10.1109%2fWorldS451998.2021.9514033&partnerID= | |
| dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | |
| dc.source | AUTONOMA | |
| dc.source | 260 | |
| dc.source | 265 | |
| dc.subject | COVID-19 | |
| dc.subject | Surveillance | |
| dc.subject | Stochastic processes | |
| dc.subject | Predictive models | |
| dc.subject | Probabilistic logic | |
| dc.subject | Data models | |
| dc.subject | Vaccines | |
| dc.title | Anticipating Subsequent Waves from First Wave Parameters in the Ongoing Covid-19 Pandemic | |
| dc.type | info:eu-repo/semantics/article | |
