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Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models.

Loshini Thiruchelvam1, Sarat Chandra Dass2, Vijanth Sagayan Asirvadam3

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Dengue cases in Selangor are influenced by neighboring districts. Ensemble Autoregressive Integrated Moving Average (ARIMA) models better predict dengue patterns by including spatial effects, outperforming traditional ARIMA models.

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Area of Science:

  • Epidemiology
  • Spatial Analysis
  • Time Series Modeling

Background:

  • Selangor, Malaysia, experiences high annual dengue cases due to its climate and urban centers.
  • Understanding spatial influences on dengue transmission is crucial for public health interventions.

Purpose of the Study:

  • To investigate the spatial influence of districts on dengue case patterns within Selangor.
  • To compare the performance of traditional Autoregressive Integrated Moving Average (ARIMA) models with Ensemble ARIMA models incorporating spatial data.

Main Methods:

  • Developed a traditional ARIMA model using historical dengue case data.
  • Constructed Ensemble ARIMA models by integrating dengue data from neighboring districts as exogenous variables.
  • Utilized Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to evaluate model performance.

Main Results:

  • Ensemble ARIMA models demonstrated a superior fit compared to traditional ARIMA models.
  • Smaller AIC and BIC values were observed for Ensemble ARIMA models, indicating better predictive accuracy.
  • The study confirmed that dengue case patterns are significantly affected by spatial effects from surrounding districts.

Conclusions:

  • Dengue case distribution in Selangor is influenced by the number of cases in adjacent districts.
  • Ensemble ARIMA models provide a more accurate approach to modeling dengue epidemiology by accounting for spatial dependencies.