{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T23:23:47Z","timestamp":1787873027924,"version":"build-2784847793"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,4]]},"DOI":"10.1109\/icassp49357.2023.10095012","type":"proceedings-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T13:28:30Z","timestamp":1683293310000},"page":"1-5","source":"Crossref","is-referenced-by-count":3,"title":["Higher-Order Spatio-Temporal Neural Networks for Covid-19 Forecasting"],"prefix":"10.1109","author":[{"given":"Yuzhou","family":"Chen","sequence":"first","affiliation":[{"name":"Temple University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sotiris","family":"Batsakis","sequence":"additional","affiliation":[{"name":"University of Huddersfield"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. Vincent","family":"Poor","sequence":"additional","affiliation":[{"name":"Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-24658-7"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref15","article-title":"GC-LSTM: Graph convolution embedded LSTM for dynamic link prediction","author":"chen","year":"2018"},{"key":"ref14","article-title":"Theory solving made easy with clingo 5","author":"gebser","year":"2016","journal-title":"Technical Communications of the International Conference on Logic Programming"},{"key":"ref20","first-page":"1684","article-title":"Z-GCNETs: Time zigzags at graph convolutional networks for time series forecasting","author":"chen","year":"2021","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"ref11","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"li","year":"2018","journal-title":"Proceedings of the International Conference on Learning Representations"},{"key":"ref10","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume":"27","author":"sutskever","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/328"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-021-00099-3"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2109228119"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330847"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref19","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume":"33","author":"bai","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref18","article-title":"Examining COVID-19 forecasting using spatio-temporal graph neural networks","author":"kapoor","year":"2020"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01862-6"},{"key":"ref7","article-title":"Time-conditioned dances with simplicial complexes: Zigzag filtration curve based supra-Hodge convolution networks for time-series forecasting","author":"chen","year":"2022","journal-title":"Advances in neural information processing systems"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3115432"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2975067"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102784"},{"key":"ref5","article-title":"A county-level dataset for informing the United States’ response to COVID-19","author":"killeen","year":"2020"}],"event":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Rhodes Island, Greece","start":{"date-parts":[[2023,6,4]]},"end":{"date-parts":[[2023,6,10]]}},"container-title":["ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10094559\/10094560\/10095012.pdf?arnumber=10095012","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T13:59:46Z","timestamp":1699883986000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10095012\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,4]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/icassp49357.2023.10095012","relation":{},"subject":[],"published":{"date-parts":[[2023,6,4]]}}}