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

Tuna Gönen; Yiwen Xing; Cagatay Turkay; Alfie Abdul-Rahman; Radu Jianu; Hui Fang; Euan Freeman; Franck Vidal; Min Chen

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

Tuna Gönen, Yiwen Xing, Cagatay Turkay, Alfie Abdul-Rahman, Radu Jianu, Hui Fang, Euan Freeman, Franck Vidal, Min Chen

Ysgol Cyfrifiadureg a Pheirianneg

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i Gynhadledd › adolygiad gan gymheiriaid

Crynodeb

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.

Iaith wreiddiol	Saesneg
Teitl	IEEE VIS Workshop on Visualization for Pandemic and Emergency Responses 2023 (Vis4PandEmRes)
Cyhoeddwr	IEEE
Statws	Cyhoeddwyd - Hyd 2023
Digwyddiad	IEEE VIS: Visualization & Visual Analytics: IEEE VIS 2023 - Melbourne Convention and Exhibition Centre, Melbourne, Awstralia Hyd: 22 Hyd 2023 → 27 Hyd 2023 https://ieeevis.org/year/2023/welcome

Cynhadledd

Cynhadledd	IEEE VIS: Visualization & Visual Analytics
Teitl cryno	IEEE VIS
Gwlad/Tiriogaeth	Awstralia
Dinas	Melbourne
Cyfnod	22/10/23 → 27/10/23
Cyfeiriad rhyngrwyd	https://ieeevis.org/year/2023/welcome

NDC y CU

Mae’r allbwn hwn yn cyfrannu at y Nod(au) Datblygu Cynaliadwy canlynol

Ffeiliau eraill a chysylltiadau

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

1 Wedi Gorffen

RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19
Vidal, F. (PY)
1/02/21 → 1/08/22
Project: Ymchwil

1 Erthygl

RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses
Chen, M., Abdul-Rahman, A., Archambault, D., Dykes, J., Slingsby, A., Ritsos, P. D., Torsney-Weir, T., Turkay, C., Bach, B., Borgo, R., Brett, A., Fang, H., Jianu, R., Khan, S., Laramee, R. S., Nguyen, P. H., Reeve, R., Roberts, J. C., Vidal, F. & Wang, Q. & 2 eraill, Wood, J. & Xu, K., Meh 2022, Yn: Epidemics. 39, 15 t., 100569.
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

Mynediad agored
Ffeil
242 Wedi eu Llwytho i Lawr (Pure)

Dyfynnu hyn

@inproceedings{4ce57cea4bcd4218b9f7048574a9daac,

title = "Visual Analytics based Search-Analyze-Forecast Framework for Epidemiological Time-series Data",

abstract = "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.",

keywords = "Human-centered computing, Visualization, Visualization techniques, Treemaps, Visualization design and evaluation methods",

author = "Tuna G{\"o}nen and Yiwen Xing and Cagatay Turkay and Alfie Abdul-Rahman and Radu Jianu and Hui Fang and Euan Freeman and Franck Vidal and Min Chen",

year = "2023",

month = oct,

language = "English",

booktitle = "IEEE VIS Workshop on Visualization for Pandemic and Emergency Responses 2023 (Vis4PandEmRes)",

publisher = "IEEE",

note = "IEEE VIS: Visualization \& Visual Analytics : IEEE VIS 2023, IEEE VIS ; Conference date: 22-10-2023 Through 27-10-2023",

url = "https://ieeevis.org/year/2023/welcome",

}

Gönen, T, Xing, Y, Turkay, C, Abdul-Rahman, A, Jianu, R, Fang, H, Freeman, E, Vidal, F & Chen, M 2023, Visual Analytics based Search-Analyze-Forecast Framework for Epidemiological Time-series Data. yn IEEE VIS Workshop on Visualization for Pandemic and Emergency Responses 2023 (Vis4PandEmRes). IEEE, IEEE VIS: Visualization & Visual Analytics, Melbourne, Awstralia, 22/10/23.

Visual Analytics based Search-Analyze-Forecast Framework for Epidemiological Time-series Data. / Gönen, Tuna; Xing, Yiwen; Turkay, Cagatay et al.
IEEE VIS Workshop on Visualization for Pandemic and Emergency Responses 2023 (Vis4PandEmRes). IEEE, 2023.

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i Gynhadledd › adolygiad gan gymheiriaid

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 -

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

Crynodeb

Cynhadledd

NDC y CU

Ffeiliau eraill a chysylltiadau

Ôl bys

Prosiectau

RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19

Allbwn Ymchwil

RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses

Dyfynnu hyn