{"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/sparse-hp-filter-finding-kinks-in-the-covid","title":"Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate","arxiv_id":"2006.10555","date":"2020-07-30","proceeding":null,"authors":[],"abstract":"In this paper, we estimate the time-varying COVID-19 contact rate of a\nSusceptible-Infected-Recovered (SIR) model. Our measurement of the contact rate\nis constructed using data on actively infected, recovered and deceased cases.\nWe propose a new trend filtering method that is a variant of the\nHodrick-Prescott (HP) filter, constrained by the number of possible kinks. We\nterm it the $\\textit{sparse HP filter}$ and apply it to daily data from five\ncountries: Canada, China, South Korea, the UK and the US. Our new method yields\nthe kinks that are well aligned with actual events in each country. We find\nthat the sparse HP filter provides a fewer kinks than the $\\ell_1$ trend\nfilter, while both methods fitting data equally well. Theoretically, we\nestablish risk consistency of both the sparse HP and $\\ell_1$ trend filters.\nUltimately, we propose to use time-varying $\\textit{contact growth rates}$ to\ndocument and monitor outbreaks of COVID-19.","url_abs":"http://arxiv.org/abs/2006.10555v2","url_pdf":"http://arxiv.org/pdf/2006.10555v2.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":"sparse-hp-filter-finding-kinks-in-the-covid","repo_url":"https://github.com/yshin12/sparseHP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}