TY - GEN

T1 - Visual Analytics based Search-Analyze-Forecast Framework for Epidemiological Time-series Data

AU - Gönen, Tuna

AU - Xing, Yiwen

AU - Turkay, Cagatay

AU - Abdul-Rahman, Alfie

AU - Jianu, Radu

AU - Fang, Hui

AU - Freeman, Euan

AU - Vidal, Franck

AU - Chen, Min

PY - 2023/10

Y1 - 2023/10

N2 - The COVID-19 pandemic has been a period where time-series of disease statistics, such as the number of cases or vaccinations, have been intensively used by public health professionals to estimate how their region compares to others and estimate what future could look like at home. Conventional visualizations are often limited in terms of advanced comparative features and in supporting forecasting systematically. This paper presents a visual analytics approach to support data-driven prediction based on a search-analyze-predict process comprising a multi-metric, multi-criteria time-series search method and a data-driven prediction technique. These are supported by a visualization framework for the comprehensive comparison of multiple time-series. We inform the design of our approach by getting iterative feedback from public health experts globally, and evaluate it both quantitatively and qualitatively.

AB - The COVID-19 pandemic has been a period where time-series of disease statistics, such as the number of cases or vaccinations, have been intensively used by public health professionals to estimate how their region compares to others and estimate what future could look like at home. Conventional visualizations are often limited in terms of advanced comparative features and in supporting forecasting systematically. This paper presents a visual analytics approach to support data-driven prediction based on a search-analyze-predict process comprising a multi-metric, multi-criteria time-series search method and a data-driven prediction technique. These are supported by a visualization framework for the comprehensive comparison of multiple time-series. We inform the design of our approach by getting iterative feedback from public health experts globally, and evaluate it both quantitatively and qualitatively.

KW - Human-centered computing

KW - Visualization

KW - Visualization techniques

KW - Treemaps

KW - Visualization design and evaluation methods

UR - https://vis4pandemres.github.io/papers/

M3 - Conference contribution

BT - IEEE VIS Workshop on Visualization for Pandemic and Emergency Responses 2023 (Vis4PandEmRes)

PB - IEEE

T2 - IEEE VIS: Visualization & Visual Analytics

Y2 - 22 October 2023 through 27 October 2023

ER -