Variable selection for prediction models of Omicron infection: Insights from two population-based cohort studies.

BACKGROUND

During the early stages of the COVID-19 pandemic, prediction modelling was widely used to forecast infection rates while only few studies developed models to predict individual risk of infection for Omicron and subsequent variants. For such prediction models to perform well, it is important to carefully select predictors from a comprehensive set of potential factors, including prior infection and vaccination history, individual behaviors, and immunological markers.

METHODS

This exploratory analysis aimed to develop and compare prediction models for Omicron infection to provide an evidence base for identifying key predictors of SARS-CoV-2 infections which can be used to inform future derivation and validation of predictive models. We used data from 710 participants from two ongoing, prospective population-based cohorts: the Zurich SARS-CoV-2 Cohort and the Zurich SARS-CoV-2 Vaccine Cohort (Switzerland). Participants were recruited between 2020 and 2021 and provided demographic data, vaccination history, self-reported infections, and longitudinal serological data (anti-S IgG, anti-S IgA, anti-N IgG antibodies) collected at 6-month intervals. Our main outcome was SARS-CoV-2 infection during the first Omicron wave (01.01.2022-31.03.2022) based on self-reported positive tests or a doubling in any of anti-S IgG, anti-S IgA or anti-N IgG. We used logistic regression models with backward stepwise selection based on the Akaike Information Criterion (AIC) to evaluate predictors and identify the best-fitting models.

RESULTS

Only 17.3% of participants reported a positive SARS-CoV-2 test result during the Omicron wave. However, when including serological testing, 37.2% of participants had evidence of infection, indicating substantial underdiagnosis. The best-performing model had an AUC of 0.69 (95%CI 0.66, 0.73) and included the following predictors: age, sex, compliance with COVID-19 prevention guidelines, smoking status, comorbidities, prior anti-N IgG antibody levels, and the sequence of previous infections and vaccinations. We found that older age (≥ 65 years) was associated with a 50-60% lower odds of Omicron infection across all our models, while having fewer prior exposures (through infections or vaccinations) increased the odds of infection.

CONCLUSION

This explorative study highlights the importance of integrating comprehensive immunological, clinical and behavioral data to predict SARS-CoV-2 infection risk. Our study lays the foundation to develop and validate future prediction models that identify individuals at risk, particularly through the novel use of infection and prior vaccination sequence as an important predictor.