{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T01:37:53Z","timestamp":1789177073259,"version":"build-2803163510"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T00:00:00Z","timestamp":1655164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Chronic wounds, such as venous leg ulcers, are difficult to treat and can reduce the quality of life for patients. Clinical trials have been conducted to identify the most effective venous leg ulcer treatments and the clinical factors that may indicate whether a wound will successfully heal. More recently, mathematical modelling has been used to gain insight into biological factors that may affect treatment success but are difficult to measure clinically, such as the rate of oxygen flow into wounded tissue. In this work, we calibrate an existing mathematical model using a Bayesian approach with clinical data for individual patients to explore which clinical factors may impact the rate of wound healing for individuals. Although the model describes group-level behaviour well, it is not able to capture individual-level responses in all cases. From the individual-level analysis, we propose distributions for coefficients of clinical factors in a linear regression model, but ultimately find that it is difficult to draw conclusions about which factors lead to faster wound healing based on the existing model and data. This work highlights the challenges of using Bayesian methods to calibrate partial differential equation models to individual patient clinical data. However, the methods used in this work may be modified and extended to calibrate spatiotemporal mathematical models to multiple data sets, such as clinical trials with several patients, to extract additional information from the model and answer outstanding biological questions.<\/jats:p>","DOI":"10.1093\/imammb\/dqac007","type":"journal-article","created":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T05:27:26Z","timestamp":1655184446000},"page":"313-331","source":"Crossref","is-referenced-by-count":4,"title":["How quickly does a wound heal? Bayesian calibration of a mathematical model of venous leg ulcer healing"],"prefix":"10.1093","volume":"39","author":[{"given":"Adriana","family":"Zanca","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics , University of Melbourne, Parkville, 3010, Victoria, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James M","family":"Osborne","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics , University of Melbourne, Parkville, 3010, Victoria, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie G","family":"Zaloumis","sequence":"additional","affiliation":[{"name":"School of Population and Global Health , University of Melbourne, Parkville, 3010, Victoria, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolina D","family":"Weller","sequence":"additional","affiliation":[{"name":"School of Nursing and Midwifery , Monash University, Clayton, 3800, Victoria, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer A","family":"Flegg","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics , University of Melbourne, Parkville, 3010, Victoria, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,6,14]]},"reference":[{"key":"2022120216033840700_ref1","doi-asserted-by":"crossref","DOI":"10.1016\/j.epidem.2020.100393","article-title":"Influencing public health policy with data-informed mathematical models of infectious diseases: recent developments and new challenges","volume":"32","author":"Alahmadi","year":"2020","journal-title":"Epidemics"},{"key":"2022120216033840700_ref2","doi-asserted-by":"crossref","DOI":"10.1098\/rsos.191315","article-title":"A comparison of approximate versus exact techniques for bayesian parameter inference in nonlinear ordinary differential equation models","volume":"7","author":"Alahmadi","year":"2020","journal-title":"Royal Soc. 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