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Updated: Jun 19, 2026

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

Comparing conditional autoregressive models for Bayesian spatial mapping of dengue cases in Indonesia.

Ferra Yanuar¹, Yudiantri Asdi¹, Aidinil Zetra²

¹Department of Mathematics and Data Science, Universitas Andalas.

Geospatial Health

|June 18, 2026

Summary

Dengue Haemorrhagic Fever (DHF) in Indonesia is linked to environmental and workforce factors. Higher temperatures showed a trend towards lower DHF risk, while more health workers correlated with higher reported cases, suggesting potential reporting biases.

Keywords:

Bayesian Spatial Conditional Autoregression Besag-York-Mollié Dengue Indonesia Leroux spatial

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Area of Science:

Epidemiology
Spatial Analysis
Public Health

Background:

Dengue Haemorrhagic Fever (DHF) presents a significant public health challenge in Indonesia, characterized by notable provincial disparities.
Understanding the spatial distribution and risk factors of DHF is crucial for effective public health interventions.

Purpose of the Study:

To model province-level DHF counts in Indonesia for 2023.
To investigate the association of average annual temperature and public health workforce density with DHF risk.
To identify geographical areas with elevated DHF burden.

Main Methods:

Bayesian spatial conditional autoregressive Poisson models with population offsets were employed.
Besag-York-Mollié (BYM) and Leroux priors were utilized with Markov chain Monte Carlo methods.
Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) were used for model comparison.

Main Results:

Spatial dependence in DHF distribution was statistically significant (Moran's I=0.4689, p=0.021).
Average annual temperature showed a non-significant association with lower DHF risk (RR=0.90; 95% CrI: 0.76 to 1.07).
Public health workforce density was associated with higher reported DHF risk (RR=1.05; 95% CrI: 1.03 to 1.07), potentially reflecting reporting capacity.

Conclusions:

Elevated DHF risk was mapped in parts of Kalimantan and eastern Indonesia.
Findings suggest the need for geographically targeted surveillance and vector control strategies.
The association between workforce density and DHF risk requires cautious interpretation, possibly indicating reporting or deployment factors rather than direct causality.