{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data-driven-modeling-reveals-a-universal","title":"Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing","arxiv_id":"2004.10666","date":"2020-04-21","proceeding":null,"authors":[],"abstract":"We show that the COVID-19 pandemic under social distancing exhibits universal\ndynamics. The cumulative numbers of both infections and deaths quickly cross\nover from exponential growth at early times to a longer period of power law\ngrowth, before eventually slowing. In agreement with a recent statistical\nforecasting model by the IHME, we show that this dynamics is well described by\nthe erf function. Using this functional form, we perform a data collapse across\ncountries and US states with very different population characteristics and\nsocial distancing policies, confirming the universal behavior of the COVID-19\noutbreak. We show that the predictive power of statistical models is limited\nuntil a few days before curves flatten, forecast deaths and infections assuming\ncurrent policies continue and compare our predictions to the IHME models. We\npresent simulations showing this universal dynamics is consistent with disease\ntransmission on scale-free networks and random networks with non-Markovian\ntransmission dynamics.","url_abs":"http://arxiv.org/abs/2004.10666v1","url_pdf":"http://arxiv.org/pdf/2004.10666v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data-driven-modeling-reveals-a-universal","repo_url":"https://github.com/Emergent-Behaviors-in-Biology/covid19","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}