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Predicting mosquito-borne disease outbreaks using poisson and negative binomial models: A comparative study.

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Summary

Accurate dengue fever forecasting in Oman is improved by using hierarchical models that include delayed climate and mosquito data. The Negative Binomial model with lagged predictors best predicted future dengue outbreaks.

Keywords:
ClimateDengueHierarchical Bayesian modelsMosquito-borne diseasesNegative Binomial regressionOmanPoisson regressionVector surveillance

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Area of Science:

  • Epidemiology
  • Public Health
  • Mathematical Modeling

Background:

  • Dengue fever, a significant mosquito-borne disease (MBD), presents a growing global challenge.
  • Urbanization, climate change, and travel increase MBD transmission.
  • Accurate predictive models are vital for early dengue detection and outbreak control.

Purpose of the Study:

  • To develop and compare hierarchical Bayesian models for forecasting dengue cases in Oman.
  • To evaluate models with and without lagged environmental and entomological predictors.
  • To identify key predictors for dengue outbreak prediction.

Main Methods:

  • Retrospective analysis of weekly dengue data (2020-2024) from Omani districts.
  • Development of four hierarchical Bayesian models (Poisson and Negative Binomial, with and without lags).
  • Evaluation using convergence diagnostics, MSE, AUC, confusion matrices, and LOOIC.

Main Results:

  • The Negative Binomial model with lagged variables demonstrated superior performance (AUC=0.881, lowest LOOIC and MSE).
  • Mosquito trap positivity was the strongest predictor; wind speed had a positive effect, and temperature a delayed negative effect.
  • Forecasts for early 2025 accurately matched observed dengue case counts.

Conclusions:

  • Integrating lagged predictors into a Negative Binomial hierarchical model significantly improves dengue outbreak prediction in Oman.
  • Findings support using lagged variables and hierarchical modeling in MBD early warning systems.
  • This approach enhances public health interventions and outbreak preparedness for mosquito-borne diseases.