Fenni Kurnia Mutiya, Ferra Yanuar, Tessy Oktavia Mukhti, Fitri Mudia Sari, Hendry Frananda
Spatial regression analysis is a method employed for data exhibiting spatial dependencies. The Spatial Durbin Model (SDM) is one such method that accounts for spatial effects in both the dependent and independent variables. However, the presence of spatial outliers can compromise the accuracy of SDM predictions. To address this issue, a robust method is needed, namely the Robust Spatial Durbin Model (RSDM). This study applies the RSDM to model the factors influencing the spread of dengue haemorrhagic fever cases across Indonesia and to identify the superior modeling approach. The results indicate that the RSDM outperforms the standard SDM, evidenced by a higher Adjusted R² and a lower Mean Squared Error (MSE). Key factors identified as significantly influencing dengue haemorrhagic fever cases are population density, the number of doctors in healthcare facilities, and the percentage of the population covered by health insurance. © 2026 the author(s).
Department of Statistics, Department of Geography, Universitas Negeri Padang, Padang, Indonesia; Department of Mathematics and Data Science, Andalas University, Padang, Indonesia
Research at a Glance
Register to unlock
Topics & SDG Alignment
Register to unlock
Collaboration
Register to unlock
Author Profile (Selected)
Register to unlock
References Overview
Register to unlock
Journal & Source
Register to unlock
Metadata & Integrity
Register to unlock