{"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/heterogeneity-in-susceptibility-dictates-the","title":"Heterogeneity in susceptibility dictates the order of epidemiological models","arxiv_id":"2005.04704","date":"2020-05-10","proceeding":null,"authors":["Christopher Rose","Andrew J. Medford","C. Franklin Goldsmith","Tejs Vegge","Joshua S. Weitz","Andrew A. Peterson"],"abstract":"The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in disease susceptibility. We show that variation leads to the natural emergence of a power law in the force of infection ($\\beta I S^p$), where the order $p$ is a simple function of the distribution shape. $p$ is significantly greater than one for reasonable variances, suggesting that conventional epidemic models make extreme assumptions about the absence of variance in susceptibility. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak.","url_abs":"https://arxiv.org/abs/2005.04704v2","url_pdf":"https://arxiv.org/pdf/2005.04704v2.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":"heterogeneity-in-susceptibility-dictates-the","repo_url":"https://github.com/aapeterson/powerlaw-figures","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"epidemiology","task_name":"Epidemiology"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}