A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping

Keyphrases

• Afghanistan 33%
• Areal Data 66%
• Areal Unit 100%
• Bayesian Modeling 33%
• Cold Spots 66%
• Conditional Autoregressive 33%
• Coverage Mapping 100%
• Data Effects 33%
• Demographic Survey 33%
• Detailed Data 33%
• Development Indicators 33%
• Diphtheria-tetanus-pertussis 33%
• Disaggregation 100%
• Fine-scale Heterogeneity 33%
• Gaussian Spatial Process 33%
• Grid Level 33%
• Health Indicators 33%
• Health Survey Data 33%
• High-resolution Grid 100%
• High-resolution Mapping 33%
• Leave No One behind 33%
• Level Statistics 33%
• Linear Predictor 33%
• Logit Link 33%
• Low Coverage 33%
• Measles 33%
• Meld 33%
• Pakistan 33%
• Performance Prediction 33%
• Pertussis Vaccination 33%
• Precise Intervention 33%
• Prediction Map 33%
• Predictive Ability 33%
• Proposed Methodology 66%
• Random Effects 33%
• Random Effects Modelling 33%
• Simulated Dataset 33%
• Small Area 33%
• Spatial Autocorrelation 33%
• Spatial Regression Model 100%
• SPDE Approach 33%
• Subnational 33%
• Survey Data 33%
• Two-level 33%
• Unit-based 100%
• United Nations Sustainable Development Goals (UN SDGs) 33%
• Vaccination 33%
• Vaccination Coverage 100%

Mathematics

• Bayesian Model 50%
• Conditionals 50%
• Covariate 50%
• Gaussian Distribution 50%
• Grid Point 50%
• Linear Predictor 50%
• Predicted Value 50%
• Predictive Ability 50%
• Predictive Performance 50%
• Random Effect 50%
• Random Effect Modeling 50%
• Regression Model 100%
• Simulated Data Set 50%
• Spatial Autocorrelation 50%
• Survey Data 100%