{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:17:28Z","timestamp":1760149048303,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Training Program (RTP) for higher degree research by the University of Queensland"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pathogens"],"abstract":"<jats:p>Continued surveillance of antimicrobial resistance is critical as a feedback mechanism for the generation of concerted public health action. A characteristic of importance in evaluating disease surveillance systems is representativeness. Scenario tree modelling offers an approach to quantify system representativeness. This paper utilises the modelling approach to assess the Australian Gonococcal Surveillance Programme\u2019s representativeness as a case study. The model was built by identifying the sequence of events necessary for surveillance output generation through expert consultation and literature review. A scenario tree model was developed encompassing 16 dichotomous branches representing individual system sub-components. Key classifications included biological sex, clinical symptom status, and location of healthcare service access. The expected sensitivities for gonococcal detection and antibiotic status ascertainment were 0.624 (95% CI; 0.524, 0.736) and 0.144 (95% CI; 0.106, 0.189), respectively. Detection capacity of the system was observed to be high overall. The stochastic modelling approach has highlighted the need to consider differential risk factors such as sex, health-seeking behaviours, and clinical behaviour in sample generation. Actionable points generated by this study include modification of clinician behaviour and supplementary systems to achieve a greater contextual understanding of the surveillance data generation process.<\/jats:p>","DOI":"10.3390\/pathogens12070907","type":"journal-article","created":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:53:04Z","timestamp":1688518384000},"page":"907","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Insights into Australia\u2019s Gonococcal Surveillance Programme through Stochastic Modelling"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4470-5419","authenticated-orcid":false,"given":"Phu Cong","family":"Do","sequence":"first","affiliation":[{"name":"School of Public Health, Faculty of Medicine, University of Queensland, Herston, QLD 4006, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibeltal Assefa","family":"Alemu","sequence":"additional","affiliation":[{"name":"School of Public Health, Faculty of Medicine, University of Queensland, Herston, QLD 4006, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon Andrew","family":"Reid","sequence":"additional","affiliation":[{"name":"School of Public Health, Faculty of Medicine, University of Queensland, Herston, QLD 4006, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1179\/2047773215Y.0000000030","article-title":"Antimicrobial resistance: A global multifaceted phenomenon","volume":"109","author":"Prestinaci","year":"2015","journal-title":"Pathog. 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