2022/06/13 by Saturnino Luz, Luz, Saturnino, Masood Masoodian +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Medicine · Social Sciences · #68U01 (secondary) #92D30 (primary) #COVID-19 epidemiological studies #Cartography #Computer science #Context (archaeology) #Data mining #Data science #Data visualization #Data-Driven Disease Surveillance #Disease surveillance #Epidemiology #FOS: Biological sciences #FOS: Computer and information sciences #Geographic information system #Geography #Geovisualization #H.4.3 #Human Mobility and Location-Based Analysis #Human-Computer Interaction (cs.HC) #Information visualization #J.3 #Medicine #Methodology (stat.ME) #Multidisciplinary approach #Populations and Evolution (q-bio.PE) #Public health #Remote sensing #Spatial analysis #Spatial epidemiology #Visualization #acm:68U01 #acm:92D30 #cs.HC #msc:68U01 #msc:92D30 #q-bio.PE #stat.ME
paper · pdf · doi:10.48550/arxiv.2206.06048
published in arXiv (Cornell University) (Cornell University) · Presented at the Map-based Interfaces and Interactions (MAPII) Workshop, at AVI'22
arxiv created 2022/06/13 · openalex publication_date 2022/06/13 · arxiv updated 2022/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Maps have played an important role in epidemiology and public health since the beginnings of these disciplines. With the advent of geographical information systems and advanced information visualization techniques, interactive maps have become essential tools for the analysis of geographical patterns of disease incidence and prevalence, as well as communication of public health knowledge, as dramatically illustrated by the proliferation of web-based maps and disease surveillance ``dashboards'' during the COVID-19 pandemic. While such interactive maps are usually effective in supporting static spatial analysis, support for spatial epidemiological visualization and modelling involving distributed and dynamic data sources, and support for analysis of temporal aspects of disease spread have proved more challenging. Combining these two aspects can be crucial in applications of interactive maps in epidemiology and public health work. In this paper, we discuss these issues in the context of support for disease surveillance in remote regions, including tools for distributed data collection, simulation and analysis, and enabling multidisciplinary collaboration.