{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T17:49:07Z","timestamp":1777916947343,"version":"3.51.4"},"reference-count":56,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T00:00:00Z","timestamp":1625702400000},"content-version":"vor","delay-in-days":372,"URL":"http:\/\/www.sagepub.com\/licence-information-for-chorus"}],"funder":[{"DOI":"10.13039\/100000738","name":"U.S. Department of Veterans Affairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000738","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005492","name":"Stanford University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005492","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000026","name":"National Institute on Drug Abuse","doi-asserted-by":"publisher","award":["2R37DA01561216"],"award-info":[{"award-number":["2R37DA01561216"]}],"id":[{"id":"10.13039\/100000026","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DGE-1656518"],"award-info":[{"award-number":["DGE-1656518"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Med Decis Making"],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>Background. Published data on a disease do not always correspond directly to the parameters needed to simulate natural history. Several calibration methods have been applied to computer-based disease models to extract needed parameters that make a model\u2019s output consistent with available data. Objective. To assess 3 calibration methods and evaluate their performance in a real-world application. Methods. We calibrated a model of cholera natural history in Bangladesh, where a lack of active surveillance biases available data. We built a cohort state-transition cholera natural history model that includes case hospitalization to reflect the passive surveillance data-generating process. We applied 3 calibration techniques: incremental mixture importance sampling, sampling importance resampling, and random search with rejection sampling. We adapted these techniques to the context of wide prior uncertainty and many degrees of freedom. We evaluated the resulting posterior parameter distributions using a range of metrics and compared predicted cholera burden estimates. Results. All 3 calibration techniques produced posterior distributions with a higher likelihood and better fit to calibration targets as compared with prior distributions. Incremental mixture importance sampling resulted in the highest likelihood and largest number of unique parameter sets to better inform joint parameter uncertainty. Compared with na\u00efve uncalibrated parameter sets, calibrated models of cholera in Bangladesh project substantially more cases, many of which are not detected by passive surveillance, and fewer deaths. Limitations. Calibration cannot completely overcome poor data quality, which can leave some parameters less well informed than others. Calibration techniques may perform differently under different circumstances. Conclusions. Incremental mixture importance sampling, when adapted to the context of high uncertainty, performs well. By accounting for biases in data, calibration can improve model projections of disease burden.<\/jats:p>","DOI":"10.1177\/0272989x20938683","type":"journal-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T13:03:16Z","timestamp":1594213396000},"page":"693-709","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Methods for Model Calibration under High Uncertainty: Modeling Cholera in Bangladesh"],"prefix":"10.1177","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3214-3228","authenticated-orcid":false,"given":"Theresa","family":"Ryckman","sequence":"first","affiliation":[{"name":"Center for Health Policy and Center for Primary Care & Outcomes Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen","family":"Luby","sequence":"additional","affiliation":[{"name":"Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Douglas K.","family":"Owens","sequence":"additional","affiliation":[{"name":"VA Palo Alto Healthcare System, Palo Alto, CA, USA"},{"name":"Center for Health Policy and Center for Primary Care & Outcomes Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eran","family":"Bendavid","sequence":"additional","affiliation":[{"name":"Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"},{"name":"Center for Health Policy and Center for Primary Care & Outcomes Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremy D.","family":"Goldhaber-Fiebert","sequence":"additional","affiliation":[{"name":"Center for Health Policy and Center for Primary Care & Outcomes Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"bibr1-0272989X20938683","doi-asserted-by":"publisher","DOI":"10.2165\/11584600-000000000-00000"},{"key":"bibr2-0272989X20938683","doi-asserted-by":"publisher","DOI":"10.1007\/s40273-017-0494-4"},{"key":"bibr3-0272989X20938683","unstructured":"GBD Compare. IHME Viz Hub. Available from: http:\/\/vizhub.healthdata.org\/gbd-compare. Accessed March 13, 2018."},{"key":"bibr4-0272989X20938683","unstructured":"World Health Organization. The global burden of cholera. Available from: http:\/\/www.who.int\/bulletin\/volumes\/90\/3\/11-093427\/en\/. Accessed March 14, 2018."},{"key":"bibr5-0272989X20938683","unstructured":"International Vaccine Institute. Country investment case study on cholera vaccination in Bangladesh. Available from: https:\/\/issuu.com\/internationalvaccineinstitute\/docs\/ivi_country_investment_case_study_o\/1. Accessed March 13, 2018."},{"key":"bibr6-0272989X20938683","doi-asserted-by":"publisher","DOI":"10.4269\/ajtmh.2011.11-0181"},{"key":"bibr7-0272989X20938683","doi-asserted-by":"crossref","unstructured":"Ali M, Nelson AR, Lopez AL, Sack DA. Updated global burden of cholera in endemic countries. PLoS Negl Trop Dis. 2015;9(6). 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