About Hanan Alahmadi Hanan Alahmadi Ph.D. Student, Statistics Bayesian computational statistics statistics geospatial statistics health surveillance spatio-temporal disease data Events Presented Events May 25 - May 31, 2025 Advanced Spatial Methods for Health Surveillance in Saudi Arabia: Data Integration and Cluster Detection Hanan Alahmadi, Ph.D. Student, Statistics May 29, 15:00 - 16:00 Building 5, Seaside, Level 5, Room 5209 Under the framework of Saudi Arabia’s Vision 2030, the Health Sector Transformation Program (HSTP) aims to revolutionize the healthcare sector by enhancing access to services, increasing their value, and bolstering preventive measures against health threats. This thesis presents innovative and efficient spatial modeling approaches tailored for public health surveillance in Saudi Arabia, including the modeling of hepatitis B and hepatitis C, leading causes of hepatocellular carcinoma and severe liver diseases which place a significant burden on Saudi Arabia’s healthcare system. Reducing the prevalence of these diseases is critical to achieving the health goals of Vision 2030, emphasizing health as a cornerstone of a vibrant society. Nov 7 - Nov 13, 2021 Joint Quantile Disease Mapping for Areal Data Hanan Alahmadi, Ph.D. Student, Statistics Nov 7, 15:00 - 16:00 B1 L4 R4102 The statistical analysis based on the quantile method is more comprehensive, flexible, and not sensitive against outliers compared to the mean methods. The study of the joint disease mapping focuses on the mean regression. This means they study the correlation or the dependence between the means of the diseases by using standard regression. However, sometimes one disease limits the occurrence of another disease. In this case, the dependence between the two diseases will not be in the means but in the different quantiles; thus, the analyzes will consider a joint disease mapping of high quantile for one disease with low quantile of the other disease.
Advanced Spatial Methods for Health Surveillance in Saudi Arabia: Data Integration and Cluster Detection Hanan Alahmadi, Ph.D. Student, Statistics May 29, 15:00 - 16:00 Building 5, Seaside, Level 5, Room 5209 Under the framework of Saudi Arabia’s Vision 2030, the Health Sector Transformation Program (HSTP) aims to revolutionize the healthcare sector by enhancing access to services, increasing their value, and bolstering preventive measures against health threats. This thesis presents innovative and efficient spatial modeling approaches tailored for public health surveillance in Saudi Arabia, including the modeling of hepatitis B and hepatitis C, leading causes of hepatocellular carcinoma and severe liver diseases which place a significant burden on Saudi Arabia’s healthcare system. Reducing the prevalence of these diseases is critical to achieving the health goals of Vision 2030, emphasizing health as a cornerstone of a vibrant society.
Joint Quantile Disease Mapping for Areal Data Hanan Alahmadi, Ph.D. Student, Statistics Nov 7, 15:00 - 16:00 B1 L4 R4102 The statistical analysis based on the quantile method is more comprehensive, flexible, and not sensitive against outliers compared to the mean methods. The study of the joint disease mapping focuses on the mean regression. This means they study the correlation or the dependence between the means of the diseases by using standard regression. However, sometimes one disease limits the occurrence of another disease. In this case, the dependence between the two diseases will not be in the means but in the different quantiles; thus, the analyzes will consider a joint disease mapping of high quantile for one disease with low quantile of the other disease.
Related Sites Geospatial Statistics and Health Surveillance (GeoHealth) Bayesian Computational Statistics and Modeling (BAYESCOMP) Statistics (STAT) Related Content Events 2
