{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T09:37:42Z","timestamp":1792661862413,"version":"4.5.0"},"reference-count":25,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"unspecified","delay-in-days":204,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"tdm","delay-in-days":204,"URL":"https:\/\/www.cambridge.org\/tdm\/tdmrep-policy.json"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Epidemiol. Infect."],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Contact tracing is an effective public health policy to put the fast-spreading epidemic under control. The government tracks the contacts of confirmed SARS-CoV-2 cases, recommends testing, encourages self-quarantine, and monitors symptoms of contacts. In developing and less-developed countries with limited resources for widespread SARS-CoV-2 testing, it remains essential to identify and quarantine positive contacts to control outbreaks. Therefore, analysing recall and precision when implementing testing policies for these contacts is necessary. We analysed a contact tracing dataset from a cohort of 827 index patients infected with SARS-CoV-2 and their 14814 close contacts from Jan 2020 to July 2020 in a province in eastern China. We constructed a network from the data and used a Graph Convolutional Network to predict each contact\u2019s infection status. To the best of our knowledge, this is the first method to use population-based contact tracing data for predicting the infection status using graph neural networks. Despite limited information, our model achieves competitive Area Under the Receiver Operating Characteristic Curve (ROC AUC) compared to hospital-onset scenarios. Based on the risk scores, we propose several contact testing policy adaptations that balance resource efficiency and effective pandemic control.<\/jats:p>","DOI":"10.1017\/s0950268825100319","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T05:58:09Z","timestamp":1753336689000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":1,"title":["Personalized risk score prediction and testing policy adaptations of a COVID-19 population-based contact tracing network"],"prefix":"10.1017","volume":"153","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7594-0273","authenticated-orcid":false,"given":"Shushan","family":"Wu","sequence":"first","affiliation":[{"name":"University of Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6681-0266","authenticated-orcid":false,"given":"Yan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimin","family":"Cheng","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/05qwgg493","id-type":"ROR","asserted-by":"publisher"}],"name":"Boston University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/041pakw92","id-type":"ROR","asserted-by":"publisher"}],"name":"Renmin University of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxuan","family":"Zhong","sequence":"additional","affiliation":[{"name":"University of Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Shen","sequence":"additional","affiliation":[{"name":"University of Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"S0950268825100319_r17","unstructured":"[17] Kipf, TN (2016) Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"S0950268825100319_r19","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1002\/wics.182","volume":"3","author":"Bootstrap","year":"2011","journal-title":"WIREs Computational Statistics"},{"key":"S0950268825100319_r20","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"S0950268825100319_r1","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.2003.2554"},{"key":"S0950268825100319_r24","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2020.05.580"},{"key":"S0950268825100319_r14","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.106.682658"},{"key":"S0950268825100319_r8","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2020.2033"},{"key":"S0950268825100319_r2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0000012"},{"key":"S0950268825100319_r13","doi-asserted-by":"crossref","unstructured":"[13] Ho, TK (1995) Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition. vol. 1, pp. 278\u2013282.","DOI":"10.1109\/ICDAR.1995.598994"},{"key":"S0950268825100319_r9","doi-asserted-by":"publisher","DOI":"10.11622\/smedj.2021019"},{"key":"S0950268825100319_r21","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2016.03.35"},{"key":"S0950268825100319_r12","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-32542-3"},{"key":"S0950268825100319_r7","doi-asserted-by":"publisher","DOI":"10.1101\/2021.07.15.21260590"},{"key":"S0950268825100319_r15","doi-asserted-by":"publisher","DOI":"10.1016\/S2666-5247(20)30172-5"},{"key":"S0950268825100319_r23","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0869-5"},{"key":"S0950268825100319_r22","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2011802117"},{"key":"S0950268825100319_r16","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2021.4686"},{"key":"S0950268825100319_r11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0243414"},{"key":"S0950268825100319_r5","doi-asserted-by":"publisher","DOI":"10.1016\/j.worlddev.2020.105105"},{"key":"S0950268825100319_r6","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00372-6"},{"key":"S0950268825100319_r25","unstructured":"[25] Veli\u010dkovi\u0107, P , Cucurull, G , Casanova, A , Romero, A , Li\u00f2, P and Bengio, Y (2018) Graph Attention Networks. In International Conference on Learning Representations."},{"key":"S0950268825100319_r3","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2021.100483"},{"key":"S0950268825100319_r18","doi-asserted-by":"publisher","DOI":"10.1007\/s13312-011-0055-4"},{"key":"S0950268825100319_r4","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2020.07.005"},{"key":"S0950268825100319_r10","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(22)00093-0"}],"updated-by":[{"DOI":"10.1017\/s0950268826101976","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000}}],"container-title":["Epidemiology and Infection"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0950268825100319","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0950268825100319","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T08:40:10Z","timestamp":1792658410000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0950268825100319\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":25,"alternative-id":["S0950268825100319"],"URL":"https:\/\/doi.org\/10.1017\/s0950268825100319","relation":{},"ISSN":["0950-2688","1469-4409"],"issn-type":[{"value":"0950-2688","type":"print"},{"value":"1469-4409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"\u00a9 The Author(s), 2025. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:\/\/creativecommons.org\/licenses\/by\/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"e90"}}