Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.

BACKGROUND

Viral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromic surveillance tools, including the WHO Severe Acute Respiratory Infection definition, demonstrate limited diagnostic accuracy in children whose symptom profiles vary widely. This study aimed to develop a machine learning (ML) model to predict microbiologically confirmed vRTIs in hospitalised children and to evaluate performance across age groups and viral pathogens.

METHODS

We conducted a retrospective cross-sectional study of 2050 paediatric patients (<18 years) admitted with acute respiratory infections to two tertiary paediatric hospitals in Canada. Predictors included age, sex, hospital transfer status, chronic comorbidity status and 22 presenting symptoms. The primary outcome was microbiologically confirmed vRTI, determined by multiplex PCR or rapid antigen testing. Six ML algorithms were trained and the best-performing model, identified by area under the receiver operating characteristic curve (auROC), was tested on age subgroups, viral pathogens and sites.

RESULTS

Among 2050 patients (median (IQR) age 2.4 (0.8-5.2) years), 1831 (89.3%) tested positive, most commonly for respiratory syncytial virus (RSV) (38.7%) and enterovirus/rhinovirus (32.8%). Logistic regression with L2 regularisation demonstrated the best performance (auROC, 0.754; 95% CI 0.697 to 0.808; sensitivity, 69.2%; specificity, 69.9%), with greatest performance among children <1 year (auROC, 0.763) and RSV cases (auROC, 0.727).

CONCLUSIONS

An ML-based logistic regression model using admission data accurately predicted paediatric vRTIs, outperforming traditional syndromic surveillance definitions, especially among infants <1 year. By integrating ML models into hospital electronic medical records, healthcare systems can achieve enhanced respiratory virus surveillance, faster outbreak detection, greater diagnostic efficiency and improved pandemic preparedness.