Geospatial and Spatio-Temporal Models

• Main Authors: Benito, Daniel Joseph, Yao, Lean Franzl, Uyheng, Joshua, De Lara-Tuprio, Elvira, Pulmano, Christian E, Estuar, Ma. Regina Justina
• 格式: text
• 出版: Archīum Ateneo 2023
• 主題: Geospatial; Spatio-temporal; Epidemiology; Spatial autocorrelation; Integrated nested laplace approximation; Analytical, Diagnostic and Therapeutic Techniques and Equipment; Data Science; Medicine and Health Sciences; Physical Sciences and Mathematics; Public Health
• 在線閱讀: https://archium.ateneo.edu/discs-faculty-pubs/414; https://doi.org/10.1007/978-981-99-3153-8_7

One of the main functions of the FASSSTER platform is to provide Spatio-Temporal approach in understanding disease spread. In fact; the name of the platform when spelled out explicitly indicates Spatio-Temporal analysis. Specifically; the original version of FASSSTER made use of Spatio-Temporal Epidemiological Modeler (STEM) to visualize the spread of dengue; measles; and typhoid that is derived from the SEIR projections. Given the limitations in using STEM for spatial and spatio-temporal analysis for COVID-19 cases in the Philippines; more appropriate models were developed to accommodate the need for better visualization as well as a better prediction on the spread of the disease.