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The Re\u2010BCG\u2010CoV\u201019 trial (NCT04379336) assessed BCG (re)vaccination for protection from coronavirus disease 2019 (COVID\u201019) and recorded 958 RTIs in 574 individuals followed over 1 year. We characterized the probability of RTI occurrence and severity using a Markov model with health scores (HSs) for four states of symptom severity. Covariate analysis on the transition probability between HSs explored the influence of demographics, medical history, severe acute respiratory syndrome\u2010coronavirus 2 (SARS\u2010CoV\u20102), or influenza vaccinations, which became available during the trial, SARS\u2010CoV\u20102 serology, and epidemiology\u2010informed seasonal influence of infection pressure represented as regional COVID\u201019 pandemic waves, as well as BCG (re)vaccination. The infection pressure reflecting the pandemic waves increased the risk of RTI symptom development, whereas the presence of SARS\u2010CoV\u20102 antibodies protected against RTI symptom development and increased the probability of symptom relief. Higher probability of symptom relief was also found in participants with African ethnicity and with male biological gender. SARS\u2010CoV\u20102 or influenza vaccination reduced the probability of transitioning from mild to healthy symptoms. Model diagnostics over calendar\u2010time indicated that COVID\u201019 cases were under\u2010reported during the first wave by an estimated 2.76\u2010fold. This trial was performed during the initial phase of the COVID\u201019 pandemic in South Africa and the results reflect that situation. Using this unique clinical dataset of prospectively studied RTIs over the course of 1 year, our Markov Chain model was able to capture risk factors for RTI development and severity, including epidemiology\u2010informed infection pressure.<\/jats:p>","DOI":"10.1002\/psp4.13006","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T11:54:57Z","timestamp":1688471697000},"page":"1250-1261","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Seasonal influence on respiratory tract infection severity including COVID\u201019 quantified through Markov Chain modeling"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7247-1360","authenticated-orcid":false,"given":"Rob C.","family":"van Wijk","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Biosciences Uppsala University  Uppsala Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7008-4946","authenticated-orcid":false,"given":"Laurynas","family":"Mockeliunas","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Biosciences Uppsala University  Uppsala Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caryn M.","family":"Upton","sequence":"additional","affiliation":[{"name":"TASK  Cape Town South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Peter","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Cape Town Lung Institute and Division of Allergy and Clinical Immunology University of Cape Town  Cape Town South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas H.","family":"Diacon","sequence":"additional","affiliation":[{"name":"TASK  Cape Town South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3424-9686","authenticated-orcid":false,"given":"Ulrika S. 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