{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T03:22:12Z","timestamp":1778124132094,"version":"3.51.4"},"reference-count":25,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>The analysis of COVID\u201019\u00a0infection data through the eye of Physics\u2010inspired Artificial Intelligence leads to a clearer understanding of the infection dynamics and assists in predicting future evolution. The spreading of the pandemic during the first half of 2020 was curtailed to a larger or lesser extent through social distancing measures imposed by most countries. In the context of the standard Susceptible\u2010Infected\u2010Recovered (SIR) model, changes in social distancing enter through time\u2010dependent infection rates.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this work we use machine learning and the infection dynamical equations of SIR to extract from the infection data the degree of social distancing and, through it, assess the effectiveness of the imposed measures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Quantitative machine learning analysis is applied to eight countries with infection data from the first viral wave. We find as two extremes Greece and USA where the measures were successful and unsuccessful, respectively, in limiting spreading. This physics\u2010based neural network approach is employed to the second wave of the infection, and by training the network with the new data, we extract the time\u2010dependent infection rate and make short\u2010term predictions with a week\u2010long or even longer horizon. This algorithmic approach is applied to all eight countries with good short\u2010term results. The data for Greece is analyzed in more detail from August to December 2020.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The model captures the essential spreading dynamics and gives useful projections for the spreading, both in the short\u2010term but also for a more intermediate horizon, based on specific social distancing measures that are extracted directly from the data.<\/jats:p><\/jats:sec>","DOI":"10.15302\/j-qb-022-0281","type":"journal-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T08:38:12Z","timestamp":1657269492000},"page":"139-149","source":"Crossref","is-referenced-by-count":12,"title":["Physics\u2010informed machine learning for the COVID\u201019 pandemic: Adherence to social distancing and short\u2010term predictions for eight countries"],"prefix":"10.1002","volume":"10","author":[{"given":"Georgios D.","family":"Barmparis","sequence":"first","affiliation":[{"name":"Institute of Theoretical and Computational Physics and Department of Physics University of Crete Heraklion 71003 Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giorgos P.","family":"Tsironis","sequence":"additional","affiliation":[{"name":"Institute of Theoretical and Computational Physics and Department of Physics University of Crete Heraklion 71003 Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,6]]},"reference":[{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0308344101"},{"key":"e_1_2_7_3_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.abc2535"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00148\u2010020\u201000778\u20102"},{"key":"e_1_2_7_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/a13100249"},{"key":"e_1_2_7_6_2","doi-asserted-by":"crossref","unstructured":"Ardabili S. Mosavi A. Band S. S. Varkonyi\u2010Koczy A. R. (2020)Coronavirus disease (COVID\u201019) global prediction using hybrid artificial intelligence method of ANN trained with grey wolf optimizer. In:2020 IEEE 3rd International Conference and Workshop in \u00d3buda on Electrical and Power Engineering (CANDO\u2010EPE) pp.000251\u2013000254","DOI":"10.1109\/CANDO-EPE51100.2020.9337757"},{"key":"e_1_2_7_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/math8060890"},{"key":"e_1_2_7_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110027"},{"key":"e_1_2_7_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40484\u2010020\u20100199\u20100"},{"key":"e_1_2_7_10_2","doi-asserted-by":"crossref","unstructured":"Albani V. V. L. Velho R. M. Zubelli J. P. (2021)Estimating monitoring and forecasting the COVID\u201019 epidemics: A spatiotemporal approach applied to NYC data. Sci. 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