Leveraging Visual Analytics and Diverse Datasets for Proactive Pandemic Surveillance: A One Health Approach

Abstract

The primary objectives of this project are to use a variety of datasets and visual analytics methods to gain insights into and deal with any prospective pandemic. Our focus is on early detection and prompt public health actions, achieved by utilizing "One Health" data, comprising human, animal, and weather information, including geospatial data and Google Trends data. In this work, we created a Predictive Intelligence for Pandemic Prevention (PIPP) dashboard that contains interactive visualization techniques to simplify the information presented, making it easier for people to understand. Our main objectives include exploring the relationship among different types of One Health datasets, developing user-friendly interactive predictive/forecasting models coupled with visualization techniques, highlighting the significance of visual analytics techniques, and advocating for timely identification of potential pandemic threats. This integrated approach offers in-depth insights and supports informed decision-making in public health. We have also integrated several forecasting approaches to predict the trends of COVID-19 as a case study.