{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T18:04:06Z","timestamp":1780682646968,"version":"3.54.1"},"reference-count":56,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Nontyphoidal\n                      <jats:italic>Salmonella enterica<\/jats:italic>\n                      (NTS) is a major public\u2011health threat in the United States of America (U.S.). Evaluating associations between serovars, exposure sources, and settings in multistate outbreaks can reveal the drivers of NTS transmission and guide prioritization of targeted prevention and control strategies.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed multistate animal\u2011contact-related NTS outbreaks reported to the CDC National Outbreak Reporting System during 2009\u20132022. We calculated incidence rates (IR) per 10 million population-years (MPY) and assessed temporal trends in IRs using Joinpoint regression. We constructed interstate co-occurrence networks linking serovars, exposure sources, settings, and states, and applied a random forest classifier to identify variables most useful for distinguishing outbreak profiles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We identified 177 multistate outbreaks (0.06 per 10 MPY) involving 40 serovars. Incidence significantly declined from 2009 to 2013 and remained stable thereafter. Random forest rankings identified birds and reptiles as the most influential exposure sources and agricultural feed stores and residential homes as the most influential exposure settings in distinguishing outbreak profiles. Co-occurrence network analysis revealed two major communities. The first included outbreaks involving serovars Enteritidis and Infantis, bird exposure source, and agricultural feed stores or farms as exposure settings, with co-occurrence hubs across the Midwest, Northeast, and Southern regions. The second community involved outbreaks linked with reptiles and mammals as exposure sources, residential homes and farms as exposure settings, and serovars Hadar, Typhimurium, and Braenderup, which were co-occurring in the Western and Southern regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Multistate animal-contact NTS outbreaks clustered into distinct serovar-exposure, source, setting, and region patterns, suggesting different NTS outbreak transmission pathways. The persistence of NTS serovars across states, diverse animal-contact sources, and exposure settings underscores the ongoing zoonotic transmission risk at the human-animal and environmental interfaces. A region-specific One Health approach to prevent and control NTS outbreaks is suggested to reduce the health burden.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0344889","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T17:33:16Z","timestamp":1780680796000},"page":"e0344889","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multistate animal-contact-related nontyphoidal Salmonella enterica outbreaks in the United States, 2009\u20132022: Network and machine learning analyses of exposure sources, settings, and serovars"],"prefix":"10.1371","volume":"21","author":[{"given":"Hammad Ur Rehman","family":"Bajwa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4506-8343","authenticated-orcid":true,"given":"Suman","family":"Bhowmick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2751-3677","authenticated-orcid":true,"given":"Csaba","family":"Varga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"issue":"6","key":"pone.0344889.ref001","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1086\/650733","article-title":"The global burden of nontyphoidal Salmonella gastroenteritis","volume":"50","author":"SE Majowicz","year":"2010","journal-title":"Clin Infect 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