<p>The survey questions on COVID-19 infection.</p>

The apparent model performance and discrimination were presented using the calibration slope and the Area Under the Curve (AUC).</p> <p>Results</p><p>Of 2,886 participants (median [IQR] age: 56 [52 –62]; 52.9% males), 283 individuals (9.8%) reported a COVID-19 infection at least once. Six predictors of at least one COVID-19 infection were identified: living outside informal tented settlements, having elementary and preparatory education or above, having chronic conditions, not receiving cash assistance, being water insecure and having unmet waste management needs.

The apparent model had modest discrimination (AUC = 0.621 [95%CI: 0.587 to 0.655]) and good calibration (c-Slope = 1.004 [95%CI: 0.704 to 1.304]). Nearly half of the cases were diagnosed through testing. The main reasons for not testing were perception that the tests were unnecessary (n = 91 [63.6%]) or inability to afford them (n = 46 [32.2%]).</p> <p>Conclusions</p><p>The identified predictors provide insight into characteristics associated with higher predicted risk of COVID-19 infection among Syrian refugees.

However, given the model’s modest predictive performance and reliance on lasso tuning, further validation and refinement in other populations and settings are needed before implementation. Awareness campaigns, screening measures, and cash assistance may be associated with reduced risk of infection in future pandemics.</p></div>