{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T09:50:44Z","timestamp":1769248244858,"version":"3.49.0"},"posted":{"date-parts":[[2022,12,5]]},"group-title":"Epidemiology","reference-count":14,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2022,12,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Epidemiological application of chaos theory methods have uncovered the existence of chaotic markers in SARS-CoV-2\u2019s 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\u2019s 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%\n                  <jats:italic>R<\/jats:italic>\n                  <jats:sup>2<\/jats:sup>\n                  scores for up to 42 days ahead prediction. The evidence is favorable to the USA\u2019s 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.\n                <\/jats:p>","DOI":"10.1101\/2022.12.04.22283069","type":"posted-content","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T13:10:16Z","timestamp":1670245816000},"source":"Crossref","is-referenced-by-count":1,"title":["Low Dimensional Chaotic Attractors in Daily Hospital Occupancy from COVID-19 in the USA and Canada"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0298-3974","authenticated-orcid":false,"given":"Carlos Pedro","family":"Gon\u00e7alves","sequence":"first","affiliation":[]}],"member":"54368","reference":[{"key":"2022120701200679000_2022.12.04.22283069v1.1","doi-asserted-by":"publisher","DOI":"10.1017\/S0950268820000990"},{"key":"2022120701200679000_2022.12.04.22283069v1.2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.matcom.2020.09.029","article-title":"COVID-19 pandemic and chaos theory","volume":"181","year":"2021","journal-title":"Math. and Comp. in Sim"},{"issue":"1","key":"2022120701200679000_2022.12.04.22283069v1.3","first-page":"50","article-title":"Complexity of COVID-19 Dynamics","volume":"24","year":"2022","journal-title":"Entr"},{"key":"2022120701200679000_2022.12.04.22283069v1.4","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1007\/s11071-021-07196-3","article-title":"Is fractional-order chaos theory the new tool to model chaotic pandemics as Covid-19?","volume":"109","year":"2022","journal-title":"Non. Dyn"},{"issue":"9","key":"2022120701200679000_2022.12.04.22283069v1.5","first-page":"1000271","article-title":"Low Dimensional Chaotic Attractors in SARS-CoV-2\u2019s Regional Epidemiological Data","volume":"11","year":"2022","journal-title":"Int J Swarm Evol Comput"},{"issue":"9","key":"2022120701200679000_2022.12.04.22283069v1.6","first-page":"1000271","article-title":"Coupled Stochastic Chaos and Multifractal Turbulence in an Artificial Financial Market","volume":"11","year":"2022","journal-title":"Int J Swarm Evol Comput"},{"issue":"2","key":"2022120701200679000_2022.12.04.22283069v1.7","first-page":"87","article-title":"A Random Walk or Color Chaos on the Stock Market? Time-Frequency Analysis of S&P Indexes","volume":"1","year":"1996","journal-title":"Stud. Nonlinear Dyn Econom"},{"issue":"4","key":"2022120701200679000_2022.12.04.22283069v1.8","first-page":"1000246","article-title":"Quantum Neural Networks, Computational Field Theory and Dynamics","volume":"11","year":"2022","journal-title":"Int J Swarm Evol Comput"},{"key":"2022120701200679000_2022.12.04.22283069v1.9","doi-asserted-by":"crossref","unstructured":"Kaplan D , Glass L. Understanding Nonlinear Dynamics. Springer-Verlag, 1995.","DOI":"10.1007\/978-1-4612-0823-5"},{"key":"2022120701200679000_2022.12.04.22283069v1.10","doi-asserted-by":"publisher","DOI":"10.1007\/bfb0091924"},{"key":"2022120701200679000_2022.12.04.22283069v1.11","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1186\/1471-2105-15-276","article-title":"Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks","volume":"15","year":"2014","journal-title":"BMC Bioinf"},{"key":"2022120701200679000_2022.12.04.22283069v1.12","doi-asserted-by":"crossref","unstructured":"Hudoba de Badyn M , Chapman A , Mesbahi M. Network entropy: A system-theoretic perspective. 54th IEEE Conference on Decision and Control (CDC). IEE. 2015;5512\u20135517.","DOI":"10.1109\/CDC.2015.7403083"},{"key":"2022120701200679000_2022.12.04.22283069v1.13","doi-asserted-by":"crossref","first-page":"022314","DOI":"10.1103\/PhysRevE.100.022314","article-title":"Persistent homology of complex networks for dynamic state detection","volume":"100","year":"2019","journal-title":"Phys. Rev. E"},{"issue":"1","key":"2022120701200679000_2022.12.04.22283069v1.14","first-page":"19","article-title":"Using persistent homology and dynamical distances to analyze protein binding","volume":"15","year":"2016","journal-title":"Stat Appl Genet Mol Biol"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2022.12.04.22283069","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T19:23:29Z","timestamp":1768505009000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2022.12.04.22283069"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":14,"URL":"https:\/\/doi.org\/10.1101\/2022.12.04.22283069","relation":{},"subject":[],"published":{"date-parts":[[2022,12,5]]},"subtype":"preprint"}}