{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:28:29Z","timestamp":1787099309288,"version":"build-2736575974"},"reference-count":59,"publisher":"The Royal Society","issue":"8","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mynoon Doro and Stephen Doro MD"},{"name":"PhD Family Private Foundation Fund"},{"name":"A*STAR National Science Scholarship, Singapore"},{"name":"Seesel Postdoctoral Fellowship, Yale University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Mathematical models are central to understanding disease transmission for host\u2013pathogen systems across the biosphere. However, most existing frameworks do not explicitly treat host population heterogeneity as a formal driver of variation in transmission dynamics. This gap is most visible in human disease, where social inequalities are well known to structure infectious disease risk, but applies broadly: ecological, behavioural and demographic structure shapes transmission in non-human host populations as well. Here, we introduce a new metric, structural causal influence, which uses causal analysis to quantify how subpopulations contribute to overall transmission through structural differences in exposure, susceptibility or recovery. Using a multi-population model, we show that even a population whose isolated reproduction number lies below one can be drawn into a sustained epidemic through minimal contact with a more affected group.<\/jats:p>","DOI":"10.1098\/rsbl.2025.0655","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:12:53Z","timestamp":1787098373000},"source":"Crossref","is-referenced-by-count":0,"title":["Structural causal influence captures the forces of social inequality in models of infectious disease"],"prefix":"10.1098","volume":"22","author":[{"given":"Sudam","family":"Surasinghe","sequence":"first","affiliation":[{"name":"Department of Ecology & Evolutionary Biology, Yale University 1 , New Haven, CT, USA"},{"name":"Public Health Modeling Unit, Yale School of Public Health 2 , New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swathi Nachiar","family":"Manivannan","sequence":"additional","affiliation":[{"name":"Department of Ecology & Evolutionary Biology, Yale University 1 , New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5716-2770","authenticated-orcid":false,"given":"Samuel V.","family":"Scarpino","sequence":"additional","affiliation":[{"name":"Institute for Experiential AI, Northeastern University 4 , Boston, MA, USA"},{"name":"Network Science Institute, Northeastern University 5 , Boston, MA, USA"},{"name":"Santa Fe Institute 6 , Santa Fe, NM, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0178-8242","authenticated-orcid":false,"given":"Lorin","family":"Crawford","sequence":"additional","affiliation":[{"name":"Microsoft Research New England 7 , Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1581-8345","authenticated-orcid":false,"given":"C. 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