{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T21:52:32Z","timestamp":1793483552628,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2022A1515010124"],"award-info":[{"award-number":["2022A1515010124"]}]},{"name":"Ministry of Science and Technology of China","award":["2021ZD0112501, 2021ZD0112502"],"award-info":[{"award-number":["2021ZD0112501, 2021ZD0112502"]}]},{"name":"General Research Fund from the Research Grant Council of Hong Kong SAR","award":["RGC\/HKBU12201619, RGC\/HKBU12202220, RGC\/HKBU12203122"],"award-info":[{"award-number":["RGC\/HKBU12201619, RGC\/HKBU12202220, RGC\/HKBU12203122"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3615139","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"4084-4088","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Epidemiology-aware Deep Learning for Infectious Disease Dynamics Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4476-2149","authenticated-orcid":false,"given":"Mutong","family":"Liu","sequence":"first","affiliation":[{"name":"Hong Kong Baptist University, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0166-3944","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8669-9064","authenticated-orcid":false,"given":"Jiming","family":"Liu","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.idm.2021.12.005"},{"key":"e_1_3_2_2_2_1","first-page":"18807","article-title":"Interpretable sequence learning for COVID-19 forecasting","volume":"33","author":"Arik Sercan","year":"2020","unstructured":"Sercan Arik , Chun-Liang Li , Jinsung Yoon , Rajarishi Sinha , Arkady Epshteyn , Long Le , Vikas Menon , Shashank Singh , Leyou Zhang , Martin Nikoltchev , 2020 . Interpretable sequence learning for COVID-19 forecasting . Advances in Neural Information Processing Systems , Vol. 33 (2020), 18807 -- 18818 . Sercan Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, et al. 2020. Interpretable sequence learning for COVID-19 forecasting. Advances in Neural Information Processing Systems , Vol. 33 (2020), 18807--18818.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_3_1","unstructured":"Defu Cao Yujing Wang Juanyong Duan Ce Zhang Xia Zhu Congrui Huang Yunhai Tong Bixiong Xu Jing Bai Jie Tong etal 2020. Spectral temporal graph neural network for multivariate time-series forecasting. Advances in neural information processing systems Vol. 33 (2020) 17766--17778.  Defu Cao Yujing Wang Juanyong Duan Ce Zhang Xia Zhu Congrui Huang Yunhai Tong Bixiong Xu Jing Bai Jie Tong et al. 2020. Spectral temporal graph neural network for multivariate time-series forecasting. Advances in neural information processing systems Vol. 33 (2020) 17766--17778."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482356"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411975"},{"key":"e_1_3_2_2_7_1","unstructured":"R. Detels M. Gulliford K. Q. Abdool and C. C. Tan. 2017. Oxford Textbook of Global Public Health. Oxford University Press.  R. Detels M. Gulliford K. Q. Abdool and C. C. Tan. 2017. Oxford Textbook of Global Public Health. Oxford University Press."},{"key":"e_1_3_2_2_8_1","volume-title":"Applied time series analysis. ETH Scripts","author":"Dettling Marcel","year":"2013","unstructured":"Marcel Dettling . 2013. Applied time series analysis. ETH Scripts ( 2013 ). Marcel Dettling. 2013. Applied time series analysis. ETH Scripts (2013)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2009.0386"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa322"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrmicro1845"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0036144500371907"},{"key":"e_1_3_2_2_13_1","volume-title":"Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113","author":"Kapoor Amol","year":"2020","unstructured":"Amol Kapoor , Xue Ben , Luyang Liu , Bryan Perozzi , Matt Barnes , Martin Blais , and Shawn O'Banion . 2020. Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113 ( 2020 ). Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, and Shawn O'Banion. 2020. Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113 (2020)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i6.16615"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1927.0118"},{"key":"e_1_3_2_2_16_1","first-page":"1","article-title":"The predictive skill of convolutional neural networks models for disease forecasting","volume":"16","author":"Lee Kookjin","year":"2021","unstructured":"Kookjin Lee , Jaideep Ray , and Cosmin Safta . 2021 . The predictive skill of convolutional neural networks models for disease forecasting . PLOS ONE , Vol. 16 , 7 (2021), 1 -- 26 . Kookjin Lee, Jaideep Ray, and Cosmin Safta. 2021. The predictive skill of convolutional neural networks models for disease forecasting. PLOS ONE, Vol. 16, 7 (2021), 1--26.","journal-title":"PLOS ONE"},{"key":"e_1_3_2_2_17_1","volume-title":"Computational Epidemiology","author":"Liu Jiming","unstructured":"Jiming Liu and Shang Xia . 2020. Computational Epidemiology . Springer . Jiming Liu and Shang Xia. 2020. Computational Epidemiology. Springer."},{"key":"e_1_3_2_2_18_1","volume-title":"Applied time series econometrics","author":"L\u00fctkepohl Helmut","unstructured":"Helmut L\u00fctkepohl and Markus Kr\"atzig. 2004. Applied time series econometrics . Cambridge university press . Helmut L\u00fctkepohl and Markus Kr\"atzig. 2004. Applied time series econometrics. Cambridge university press."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i11.21479"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210077"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-26422-1_29"},{"key":"e_1_3_2_2_23_1","volume-title":"Inter-and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting. arXiv preprint arXiv:2208.11515","author":"Xie Feng","year":"2022","unstructured":"Feng Xie , Zhong Zhang , Xuechen Zhao , Bin Zhou , and Yusong Tan . 2022. Inter-and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting. arXiv preprint arXiv:2208.11515 ( 2022 ). Feng Xie, Zhong Zhang, Xuechen Zhao, Bin Zhou, and Yusong Tan. 2022. Inter-and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting. arXiv preprint arXiv:2208.11515 (2022)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.21037\/jtd.2020.02.64"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481927"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615139","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:42Z","timestamp":1750178202000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":25,"alternative-id":["10.1145\/3583780.3615139","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3615139","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}