Loading [a11y]/accessibility-menu.js
ESID: Exploring the Design and Development of a Visual Analytics Tool for Epidemiological Emergencies | IEEE Conference Publication | IEEE Xplore

ESID: Exploring the Design and Development of a Visual Analytics Tool for Epidemiological Emergencies


Abstract:

Visual analytics tools can help illustrate the spread of infectious diseases and enable informed decisions on epidemiological and public health issues. To create visualis...Show More

Abstract:

Visual analytics tools can help illustrate the spread of infectious diseases and enable informed decisions on epidemiological and public health issues. To create visualisation tools that are intuitive, easy to use, and effective in communicating information, continued research and development focusing on user-centric and methodological design models is extremely important. As a contribution to this topic, this paper presents the design and development process of the visual analytics application ESID (Epidemiological Scenarios for Infectious Diseases ). ESID is a visual analytics tool aimed at projecting the future developments of infectious disease spread using reported and simulated data based on sound mathematical-epidemiological models. The development process involved a collaborative and participatory design approach with project partners from diverse scientific fields. The findings from these studies, along with the guidelines derived from them, played a pivotal role in shaping the visualisation tool.
Date of Conference: 22-23 October 2023
Date Added to IEEE Xplore: 12 December 2023
ISBN Information:
Conference Location: Melbourne, Australia

Funding Agency:


1 Introduction

Visual analytics (VA) tools are increasingly used in the field of epidemiology and public health [6 , 20] , particularly, for the surveillance and management of infectious diseases with the potential of emerging pandemics. Scientists need to analyse a large amount of data at different scales for predictions with mathematical models. This is where VA tools help in gaining insights from the large and complex data sets. Based on these insights, scientists can advise decision makers involved in the control of infection events to make informed decisions.

Contact IEEE to Subscribe

References

References is not available for this document.