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Related Experiment Video

Updated: Dec 22, 2025

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
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Inference on dengue epidemics with Bayesian regime switching models.

Jue Tao Lim1, Borame Sue Dickens1, Sun Haoyang1

  • 1Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.

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|May 2, 2020
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Summary

Bayesian regime switching models effectively identify distinct epidemic and endemic dengue transmission patterns in Singapore. These models offer robust dengue forecasting, even with limited data, aiding public health interventions.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Dengue virus transmission poses a significant public health challenge in tropical regions like Singapore.
  • Frequent dengue outbreaks necessitate improved methods for predicting and understanding transmission dynamics.
  • Limited research exists on identifying critical turning points preceding surges in dengue case counts.

Purpose of the Study:

  • To apply Bayesian regime switching (BRS) models to differentiate between epidemic and endemic dengue transmission regimes.
  • To assess the accuracy and robustness of BRS models in forecasting dengue transmission.
  • To investigate the long-term influence of climatic factors on dengue transmission regimes.

Main Methods:

  • Bayesian regime switching (BRS) models were utilized to infer distinct epidemic and endemic transmission patterns.
  • A custom multi-move Gibbs sampling algorithm was developed for parameter estimation.
  • LASSO-based regression and cross-validation were employed to analyze climatic factors influencing transmission.
  • Posterior predictive checks and AUC-ROC analysis validated model performance.

Main Results:

  • BRS models accurately replicated temporal trends in dengue transmission, achieving robust nowcasting accuracy (AUC-ROC 0.935).
  • Identified epidemic and endemic regimes persisted for an average of 20 and 66 weeks, respectively.
  • Long-run climatic effects (up to 20 weeks prior) did not significantly distinguish between epidemic and endemic regimes.

Conclusions:

  • BRS models provide a reliable framework for understanding and forecasting dengue transmission dynamics in Singapore.
  • The models demonstrate strong predictive power, even with limited data, supporting public health surveillance.
  • The methodology is adaptable for analyzing other infectious diseases with time-series transmission data.