{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:25:53Z","timestamp":1768555553173,"version":"3.49.0"},"posted":{"date-parts":[[2022,9,17]]},"group-title":"Epidemiology","reference-count":28,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2022,9,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Recent studies applying chaos theory methods have found the existence of chaotic markers in SARS-CoV-2\u2019s epidemiological data, evidence that has implications on the prediction, modeling and epidemiological analysis of the SARS-CoV-2\/COVID-19 pandemic with implications for healthcare management.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Aim and Methods<\/jats:title>\n                  <jats:p>We study the aggregate data for the new cases per million and the new deaths per million from COVID-19 in Africa, Asia, Europe, North and South America and Oceania, applying chaos theory\u2019s empirical methods including embedding dimension estimation, Lyapunov spectra estimation, spectral analysis and state-of-the-art topological data analysis methods combining persistent homology, recurrence analysis and machine learning with the aim of characterizing the nature of the dynamics and its predictability.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The results show that for all regions except Oceania there is evidence of low dimensional noisy chaotic attractors that are near the onset of chaos, with a recurrence structure that can be used by adaptive artificial intelligence solutions equipped with nearest neighbors\u2019 machine learning modules to predict with a very high performance the future values of the two target series for each region. The persistent homology analysis uncovers a division into two groups, the first group comprised of Africa and Asia and the second of Europe, North and South America. For Oceania, we found evidence of the occurrence of a bifurcation which we characterize in detail applying a combination of machine learning and topological analysis methods, we find that the bifurcation in the region is related to the emergence of new variants.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2022.09.16.22280044","type":"posted-content","created":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T14:45:25Z","timestamp":1663425925000},"source":"Crossref","is-referenced-by-count":1,"title":["Low Dimensional Chaotic Attractors in SARS-CoV-2\u2019s Regional Epidemiological Data"],"prefix":"10.64898","author":[{"given":"Carlos Pedro","family":"Gon\u00e7alves","sequence":"first","affiliation":[]}],"member":"54368","reference":[{"key":"2022092006550521000_2022.09.16.22280044v1.1","doi-asserted-by":"publisher","DOI":"10.1017\/S0950268820000990"},{"key":"2022092006550521000_2022.09.16.22280044v1.2","first-page":"138","article-title":"COVID-19 pandemic and chaos theory. 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