Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model
Tuana Kant1,2, Rohini R Datta3, Daniel S Farrar2,3
1Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Insights
A machine learning model accurately predicts viral respiratory tract infections (vRTIs) in hospitalized children, outperforming traditional methods. This advance improves early detection and pandemic preparedness, especially for infants.
Area of Science:
- Pediatric Infectious Diseases
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Viral respiratory tract infections (vRTIs) are a major cause of pediatric hospitalizations.
- Current surveillance methods lack diagnostic accuracy in children due to varied symptoms.
- Need for improved diagnostic tools for vRTIs in pediatric populations.
Purpose of the Study:
- To develop and evaluate a machine learning (ML) model for predicting microbiologically confirmed vRTIs in hospitalized children.
- To assess the model's performance across different age groups and viral pathogens.
- To compare ML model accuracy against traditional syndromic surveillance definitions.
Main Methods:
- Retrospective study of 2050 pediatric patients with acute respiratory infections.
- Utilized admission data including symptoms, age, and comorbidities.
- Trained and tested six ML algorithms, selecting the best-performing logistic regression model based on auROC.
Main Results:
- The ML logistic regression model achieved an auROC of 0.754, outperforming traditional methods.
- Model demonstrated higher accuracy in infants (<1 year) and for respiratory syncytial virus (RSV) cases.
- High prevalence of RSV (38.7%) and enterovirus/rhinovirus (32.8%) detected among positive cases.
Conclusions:
- ML-based prediction models accurately identify pediatric vRTIs using readily available admission data.
- These models offer superior performance compared to syndromic surveillance, particularly for infants.
- Integration into electronic medical records can enhance surveillance, outbreak detection, and pandemic preparedness.
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.
