{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:58:08Z","timestamp":1775069888021,"version":"3.50.1"},"reference-count":35,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Section for Global Health, Department of Population Health, NYU Grossman School of Medicine"},{"name":"Division of Comparative Effectiveness & Decision Science, Department of Population Health, NYU Grossman School of Medicine"},{"name":"Division of Comparative Effectiveness & Decision Science, Department of Population Health, NYU Grossman School of Medicine"},{"name":"Division of Comparative Effectiveness & Decision Science, Department of Population Health, NYU Grossman School of Medicine"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Background<\/jats:title>\n Mathematical models guide tuberculosis (TB) target-setting, yet most assume homogeneous \u201call-to-all\u201d mixing. We compared projected intervention impacts between an all-to-all compartmental model and a Barab\u00e1si\u2013Albert (BA) scale\u2011free social network model under otherwise identical disease assumptions.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We calibrated transmission parameters so both models produced similar baseline trends, then introduced vaccination (coverage 30\u201370%; efficacy 80\u201395%) and treatment (20\u201350% increases in recovery) after a 400\u2011day burn\u2011in. Outcomes were assessed 300 days post\u2011intervention.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Under 60% coverage, increasing vaccine efficacy from 80% to 95% yielded smaller projected reductions in active TB with the network model than with all\u2011to\u2011all mixing. Treatment improvements showed the same pattern: lower reductions under the network than the all\u2011to\u2011all model at modest efficacy, converging at high efficacy\/coverage. Findings were robust across baseline prevalence scenarios.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Accounting for social networks can attenuate projected impacts for sub\u2011optimal TB interventions. Forecasts and target\u2011setting should include sensitivity to social network structure.<\/jats:p>\n <\/jats:sec>","DOI":"10.1371\/journal.pone.0343421","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:44:16Z","timestamp":1775065456000},"page":"e0343421","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["How do social network models compare to all-to-all models for forecasting tuberculosis epidemics? A mathematical modeling study"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6527-1088","authenticated-orcid":true,"given":"Masabho P.","family":"Milali","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hae-Young","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0864-3142","authenticated-orcid":true,"given":"George F.","family":"Corliss","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Bershteyn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"issue":"3","key":"pone.0343421.ref001","article-title":"Time for high-burden countries to lead the tuberculosis research agenda","volume":"15","author":"M Pai","year":"2018","journal-title":"PLoS Med"},{"issue":"5","key":"pone.0343421.ref002","doi-asserted-by":"crossref","DOI":"10.1016\/S2214-109X(24)00083-4","article-title":"A global tuberculosis dictionary: unified terms and definitions for the field of tuberculosis","volume":"12","author":"AL Garcia-Basteiro","year":"2024","journal-title":"Lancet Glob Health"},{"key":"pone.0343421.ref003","article-title":"Tuberculosis Overview","volume-title":"StatPearls","author":"EH Tobin","year":"2025"},{"issue":"9","key":"pone.0343421.ref004","doi-asserted-by":"crossref","DOI":"10.1101\/cshperspect.a017863","article-title":"Multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis","volume":"5","author":"KJ Seung","year":"2015","journal-title":"Cold Spring Harb Perspect Med"},{"issue":"1","key":"pone.0343421.ref005","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1038\/s41426-018-0106-1","article-title":"Risk factors for extrapulmonary dissemination of tuberculosis and associated mortality during treatment for extrapulmonary tuberculosis","volume":"7","author":"X Qian","year":"2018","journal-title":"Emerg Microbes Infect"},{"key":"pone.0343421.ref006","doi-asserted-by":"crossref","first-page":"640555","DOI":"10.3389\/fphar.2021.640555","article-title":"Treatment outcomes of extensively drug-resistant tuberculosis in pakistan: a countrywide retrospective record review","volume":"12","author":"M Abubakar","year":"2021","journal-title":"Front Pharmacol"},{"issue":"9979","key":"pone.0343421.ref007","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1016\/S0140-6736(15)60570-0","article-title":"WHO\u2019s new end TB strategy","volume":"385","author":"M Uplekar","year":"2015","journal-title":"The Lancet"},{"key":"pone.0343421.ref008","unstructured":"Global tuberculosis report. 2020 [cited 2023 Feb 15]. 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