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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8051| Low Dimensional Chaotic Attractors in Daily Hospital Occupancy from COVID-19 in the USA and Canada | |
| Carlos Pedro dos Santos Goncalves | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2022.12.04.22283069 | |
| https://www.medrxiv.org/content/10.1101/2022.12.04.22283069v1 | |
| Abstract Epidemiological application of chaos theory methods have uncovered the existence of chaotic markers in SARS-CoV-2’s epidemiological data, including low dimensional attractors with positive Lyapunov exponents, and evidence markers of a dynamics that is close to the onset of chaos for different regions. We expand on these previous works, performing a comparative study of United States of America (USA) and Canada’s COVID-19 daily hospital occupancy cases, applying a combination of chaos theory, machine learning and topological data analysis methods. Both countries show markers of low dimensional chaos for the COVID-19 hospitalization data, with a high predictability for adaptive artificial intelligence systems exploiting the recurrence structure of these attractors, with more than 95% R2 scores for up to 42 days ahead prediction. The evidence is favorable to the USA’s hospitalizations being closer to the onset of chaos and more predictable than Canada, the reasons for this higher predictability are accounted for by using topological data analysis methods. | |
| bioRxiv | |
| 05-12-2022 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| Low Dimensional Chaotic Attractors in Daily Hospital Occupancy from COVID-19 in the USA and Canada.pdf | 604 kB | Adobe PDF | Visualizar/Abrir |