Seasonal influence on respiratory tract infection severity including COVID-19 quantified through Markov Chain modelling.

Respiratory tract infections (RTI) are a burden to global health, but their characterisation is complicated by the influence of seasonality on incidence and severity. The Re-BCG-CoV-19 trial (NCT04379336) assessed BCG-(re)vaccination for protection from COVID-19 and recorded 958 RTIs in 574 individuals followed over one year. We characterized the probability of RTI occurrence and severity using a Markov model with health scores for four states of symptom severity. Covariate analysis on the transition probability between health scores explored the influence of demographics, medical history, SARS-CoV-2 or influenza vaccinations which became available during the trial, SARS-CoV-2 serology, and epidemiology-informed seasonal influence of infection pressure represented as regional COVID-19 pandemic waves, as well as BCG (re)vaccination. The infection pressure reflecting the pandemic waves increased the risk of RTI symptom development, while the presence of SARS-CoV-2 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-CoV-2 or influenza vaccination reduced the probability of transitioning from mild to healthy symptoms. Model diagnostics over calendar-time indicated that COVID-19 cases were underreported during the first wave by an estimated 2.76-fold. This trial was performed during the initial phase of the COVID-19 pandemic in South Africa and the results reflect that situation. Using this unique clinical dataset of prospectively studied RTIs over the course of one year, our Markov Chain model was able to capture risk factors for RTI development and severity, including epidemiology-informed infection pressure.