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Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast
Jue Tao Lim1, Kelvin Bryan Tan2,3, John Abisheganaden1,4
1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
Plos Computational Biology
|February 7, 2023
Summary
Forecasting upper respiratory tract infections (URTIs) using environmental and disease data improves public health planning. Combining multiple models provides accurate predictions for better healthcare resource allocation.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Upper respiratory tract infections (URTIs) significantly strain primary healthcare resources.
- Effective public health strategies require accurate forecasting of URTI burden and transmission dynamics.
- Understanding factors influencing URTI transmission is crucial for resource planning.
Purpose of the Study:
- To develop and validate a novel forecasting approach for URTIs.
- To enhance national public health resource planning capabilities.
- To identify key environmental and epidemiological drivers of URTI transmission.
Main Methods:
- Utilized high-dimensional environmental and disease data (>1000 features).
- Developed optimized sub-models balancing explainability, fit, and accuracy.
- Employed forecast combinations of multiple models for prediction.
- Conducted out-of-sample forecast assessment across stable and dynamic transmission periods (2012-2022).
- Applied post-selection inference for epidemiological analysis.
Main Results:
- Forecast combinations demonstrated superior and consistent predictive performance compared to individual models.
- Identified significant associations between lower temperatures, increased relative and absolute humidity, and higher URTI attendance.
- Validated the model's effectiveness across periods with and without structural breaks in transmission.
Conclusions:
- The proposed forecasting methodology offers a robust tool for national public health resource planning.
- The approach supports outbreak preparedness and healthcare resource allocation.
- Effective forecasting is achievable even during periods of shifting transmission dynamics.
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