A Collaborative Platform Supporting Distributed Teams in Visualization and Analysis of Infectious Disease Data
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85139012846
Journal Title
Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
Start Page
226
End Page
232
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022 (2022) , 226-232
Suggested Citation
Vogtle F., Haddawy P., Yin M.S., Barkowsky T., Bicout D., Prachyabrued M., Lawpoolsri S. A Collaborative Platform Supporting Distributed Teams in Visualization and Analysis of Infectious Disease Data. Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022 (2022) , 226-232. 232. doi:10.1109/ICHI54592.2022.00042 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84362
Title
A Collaborative Platform Supporting Distributed Teams in Visualization and Analysis of Infectious Disease Data
Author's Affiliation
Other Contributor(s)
Abstract
Control of infectious diseases requires insight into transmission dynamics and their relation to relevant spatiotemporal factors. Due to the geographically distributed nature of disease outbreaks, as well as the multidisciplinary teams needed to analyze disease data, the experts needed for analysis and modeling may not all be located in the same place at the same time. There is thus need for an analysis and visualization tool to support distributed teams in upstream and downstream disease modeling tasks. In this paper we present a collaborative platform for visualization and analysis of spatiotemporal data concerning disease incidence and related factors. The platform supports integration of data in a variety of formats and resolutions and creation of derived attributes on the fly. Data can be visualized in terms of 3D choropleth maps, as well as scatter plots which include statistical correlations. Multiple visualizations can be simultaneously displayed and manipulated by all session users. We demonstrate the use of the system with the analysis and modeling of data on dengue incidence and related factors in Thailand. The data includes counts of potential mosquito vector breeding sites extracted from street view images using convolutional neural nets. We show how the visualization supports exploratory data analysis that drives machine learning model development and then show how it helps to understand the model output, which provides insight into how and where the models may be best used.