Infrastructure and methods for real-time predictions of the 2014 dengue fever season in Thailand

TL;DR

This paper presents a real-time dengue fever forecasting system for Thailand's provinces, demonstrating mixed accuracy but outperforming simple models in many regions, and discusses challenges like reporting delays.

Contribution

Developed an operational infrastructure for real-time multi-step dengue predictions across Thai provinces in 2014, highlighting implementation challenges and performance insights.

Findings

Predictions outperformed naive seasonal models in over half of provinces at 1.5-month horizon.

Reporting delays significantly impact long-range prediction accuracy.

The infrastructure provides valuable lessons for public health forecasting.

Abstract

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create accurate and actionable real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created an operational and computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing na\"ive seasonal models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making.