WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic
The outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people’s daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of mult...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-11-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2023.2182236 |
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| author | Siqi Wang Xiaoxiao Zhao Jingyu Qiu Haofen Wang Chuang Tao |
| author_facet | Siqi Wang Xiaoxiao Zhao Jingyu Qiu Haofen Wang Chuang Tao |
| author_sort | Siqi Wang |
| collection | DOAJ |
| description | The outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people’s daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of multi-source heterogeneous data fusion, deep mining, and comprehensive applications. The Spatio-Temporal Artificial Intelligence (STAI) technology, which focuses on integrating spatial related time-series data, artificial intelligence models, and digital tools to provide intelligent computing platforms and applications, opens up new opportunities for scientific epidemic control. To this end, we leverage STAI and long-term experience in location-based intelligent services in the work. Specifically, we devise and develop a STAI-driven digital infrastructure, namely, WAYZ Disease Control Intelligent Platform (WDCIP), which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection, processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios. According to the platform implementation logic, our work can be performed and summarized from three aspects: (1) a STAI-driven integrated system; (2) a hybrid GNN-based approach for hierarchical risk assessment (as the core algorithm of WDCIP); and (3) comprehensive applications for social epidemic containment. This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources, where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic. So far, WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic. |
| format | Article |
| id | doaj-art-7453ae8fe71c409e964ebdea15a208b7 |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-7453ae8fe71c409e964ebdea15a208b72024-12-11T11:57:33ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532024-11-012762023204710.1080/10095020.2023.2182236WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemicSiqi Wang0Xiaoxiao Zhao1Jingyu Qiu2Haofen Wang3Chuang Tao4College of Design and Innovation, Tongji University, Shanghai, ChinaProduct Development Department, Wayz AI Technology Company Limited, Shanghai, ChinaProduct Development Department, Wayz AI Technology Company Limited, Shanghai, ChinaCollege of Design and Innovation, Tongji University, Shanghai, ChinaProduct Development Department, Wayz AI Technology Company Limited, Shanghai, ChinaThe outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people’s daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of multi-source heterogeneous data fusion, deep mining, and comprehensive applications. The Spatio-Temporal Artificial Intelligence (STAI) technology, which focuses on integrating spatial related time-series data, artificial intelligence models, and digital tools to provide intelligent computing platforms and applications, opens up new opportunities for scientific epidemic control. To this end, we leverage STAI and long-term experience in location-based intelligent services in the work. Specifically, we devise and develop a STAI-driven digital infrastructure, namely, WAYZ Disease Control Intelligent Platform (WDCIP), which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection, processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios. According to the platform implementation logic, our work can be performed and summarized from three aspects: (1) a STAI-driven integrated system; (2) a hybrid GNN-based approach for hierarchical risk assessment (as the core algorithm of WDCIP); and (3) comprehensive applications for social epidemic containment. This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources, where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic. So far, WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic.https://www.tandfonline.com/doi/10.1080/10095020.2023.2182236COVID-19spatio-temporal artificial intelligenceepidemic prevention and control platformrisk assessmentSIRgraph autoencoder |
| spellingShingle | Siqi Wang Xiaoxiao Zhao Jingyu Qiu Haofen Wang Chuang Tao WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic Geo-spatial Information Science COVID-19 spatio-temporal artificial intelligence epidemic prevention and control platform risk assessment SIR graph autoencoder |
| title | WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic |
| title_full | WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic |
| title_fullStr | WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic |
| title_full_unstemmed | WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic |
| title_short | WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic |
| title_sort | wdcip spatio temporal ai driven disease control intelligent platform for combating covid 19 pandemic |
| topic | COVID-19 spatio-temporal artificial intelligence epidemic prevention and control platform risk assessment SIR graph autoencoder |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2023.2182236 |
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