{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T20:38:28Z","timestamp":1774384708440,"version":"3.50.1"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s00521-025-11804-3","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T08:30:48Z","timestamp":1772699448000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Casenet: a novel HIV forecasting model using attention-based spatio-temporal graph network leveraging city-wide infection correlations"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8381-2954","authenticated-orcid":false,"given":"Dat Thanh","family":"Pham","sequence":"first","affiliation":[]},{"given":"Duong","family":"Van Nguyen","sequence":"additional","affiliation":[]},{"given":"Thanh Tan","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Viet Anh","family":"Nguyen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"11804_CR1","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1186\/s12879-024-09481-z","volume":"24","author":"N Admasu","year":"2024","unstructured":"Admasu N, Lomboro A, Kebede E et al (2024) Recent hiv infection and associated factors among newly diagnosed hiv cases in the southwest ethiopia regional state: Hiv case-based surveillance analysis (2019\u20132022). BMC Infect Dis 24:609. https:\/\/doi.org\/10.1186\/s12879-024-09481-z","journal-title":"BMC Infect Dis"},{"issue":"3","key":"11804_CR2","doi-asserted-by":"publisher","first-page":"44","DOI":"10.2196\/publichealth.7610","volume":"3","author":"R Harklerode","year":"2017","unstructured":"Harklerode R, Schwarcz S, Hargreaves J, Boulle A, Todd J, Xueref S, Rice B (2017) Feasibility of establishing hiv case-based surveillance to measure progress along the health sector cascade: situational assessments in Tanzania, South Africa, and Kenya. JMIR Public Health Surveill 3(3):44. https:\/\/doi.org\/10.2196\/publichealth.7610","journal-title":"JMIR Public Health Surveill"},{"issue":"2","key":"11804_CR3","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1177\/0033354918754541","volume":"133","author":"M Padilla","year":"2018","unstructured":"Padilla M, Mattson CL, Scheer S, Udeagu CN, Buskin SE, Hughes AJ, Jaenicke T, Wohl AR, Prejean J, Wei SC (2018) Locating people diagnosed with hiv for public health action: utility of hiv case surveillance and other data sources. Public Health Rep 133(2):147\u2013154. https:\/\/doi.org\/10.1177\/0033354918754541. (Epub 2018 Feb 27)","journal-title":"Public Health Rep"},{"issue":"9","key":"11804_CR4","doi-asserted-by":"publisher","first-page":"2774","DOI":"10.1007\/s10461-016-1656-7","volume":"21","author":"H Clark","year":"2017","unstructured":"Clark H, Babu AS, Wiewel EW, Opoku J, Crepaz N (2017) Diagnosed hiv infection in transgender adults and adolescents: results from the national hiv surveillance system, 2009\u20132014. AIDS Behav 21(9):2774\u20132783. https:\/\/doi.org\/10.1007\/s10461-016-1656-7","journal-title":"AIDS Behav"},{"issue":"3","key":"11804_CR5","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1097\/QAI.0000000000000302","volume":"67","author":"JC Dombrowski","year":"2014","unstructured":"Dombrowski JC, Buskin SE, Bennett A, Thiede H, Golden MR (2014) Use of multiple data sources and individual case investigation to refine surveillance-based estimates of the hiv care continuum. J Acquir Immune Defic Syndr 67(3):323\u2013330. https:\/\/doi.org\/10.1097\/QAI.0000000000000302","journal-title":"J Acquir Immune Defic Syndr"},{"issue":"2","key":"11804_CR6","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1097\/QAI.0000000000003479","volume":"97","author":"SA McDonald","year":"2024","unstructured":"McDonald SA, Yeung A, Nandwani R, Clutterbuck D, Wallace LA, Cullen BL, Shepherd SJ, Roy K, Marsh K, Gunson R, Hutchinson SJ (2024) A statistical model for inference of recent and incident hiv infection using surveillance data on individuals newly diagnosed with hiv infection in scotland. J Acquir Immune Defic Syndr 97(2):117\u2013124. https:\/\/doi.org\/10.1097\/QAI.0000000000003479","journal-title":"J Acquir Immune Defic Syndr"},{"issue":"1S","key":"11804_CR7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1097\/QAI.0000000000003319","volume":"95","author":"T Brown","year":"2024","unstructured":"Brown T, Peerapatanapokin W, Siripong N, Puckett R (2024) The aids epidemic model 2023 for estimating hiv trends and transmission dynamics in asian epidemic settings. J Acquir Immune Defic Syndr 95(1S):13\u201323. https:\/\/doi.org\/10.1097\/QAI.0000000000003319","journal-title":"J Acquir Immune Defic Syndr"},{"key":"11804_CR8","doi-asserted-by":"publisher","first-page":"494","DOI":"10.4236\/ojs.2020.103030","volume":"10","author":"N Umunna","year":"2020","unstructured":"Umunna N, Samuel O (2020) Forecasting the monthly reported cases of human immunodeficiency virus (hiv) at minna niger state, nigeria. Open J Stat 10:494\u2013515. https:\/\/doi.org\/10.4236\/ojs.2020.103030","journal-title":"Open J Stat"},{"key":"11804_CR9","doi-asserted-by":"publisher","first-page":"144","DOI":"10.46338\/ijetae0922_15","volume":"12","author":"S Aribe Jr","year":"2022","unstructured":"Aribe S Jr, Gerardo B, Medina R (2022) Time series forecasting of hiv\/aids in the philippines using deep learning: does covid-19 epidemic matter? Int J Emerg Technol Adv Eng 12:144\u2013157. https:\/\/doi.org\/10.46338\/ijetae0922_15","journal-title":"Int J Emerg Technol Adv Eng"},{"key":"11804_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110861","volume":"146","author":"KE ArunKumar","year":"2021","unstructured":"ArunKumar KE, Kalaga DV, Kumar CMS, Kawaji M, Brenza TM (2021) Forecasting of covid-19 using deep layer recurrent neural networks (rnns) with gated recurrent units (grus) and long short-term memory (lstm) cells. Chaos Solitons Fractals 146:110861. https:\/\/doi.org\/10.1016\/j.chaos.2021.110861","journal-title":"Chaos Solitons Fractals"},{"issue":"10","key":"11804_CR11","doi-asserted-by":"publisher","first-page":"5388","DOI":"10.1109\/TKDE.2023.3333824","volume":"36","author":"G Jin","year":"2024","unstructured":"Jin G, Liang Y, Fang Y, Shao Z, Huang J, Zhang J, Zheng Y (2024) Spatio-temporal graph neural networks for predictive learning in urban computing: a survey. IEEE Trans Knowl Data Eng 36(10):5388\u20135408. https:\/\/doi.org\/10.1109\/TKDE.2023.3333824","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11804_CR12","doi-asserted-by":"publisher","unstructured":"Panagopoulos G, Nikolentzos G, Vazirgiannis M (2021) Transfer graph neural networks for pandemic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp 4838\u20134845 . https:\/\/doi.org\/10.1609\/aaai.v35i6.16616","DOI":"10.1609\/aaai.v35i6.16616"},{"issue":"2","key":"11804_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3579815","volume":"14","author":"S Yu","year":"2023","unstructured":"Yu S, Xia F, Li S, Hou M, Sheng QZ (2023) Spatio-temporal graph learning for epidemic prediction. ACM Trans Intell Syst Technol 14(2):1\u201325. https:\/\/doi.org\/10.1145\/3579815","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"6","key":"11804_CR14","doi-asserted-by":"publisher","first-page":"1740","DOI":"10.1111\/tgis.12675","volume":"24","author":"S Zhong","year":"2020","unstructured":"Zhong S, Bian L (2020) What drives disease flows between locations? Trans GIS 24(6):1740\u20131755. https:\/\/doi.org\/10.1111\/tgis.12675. (Epub 2020 Aug 5)","journal-title":"Trans GIS"},{"issue":"1","key":"11804_CR15","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1186\/s12879-022-07306-5","volume":"22","author":"AB Sandie","year":"2022","unstructured":"Sandie AB, Tchatchueng Mbougua JB, Nlend AEN, Thiam S, Nono BF, Fall NA, Senghor DB, Sylla EHM, Faye CM (2022) Hot-spots of hiv infection in cameroon: a spatial analysis based on demographic and health surveys data. BMC Infect Dis 22(1):334. https:\/\/doi.org\/10.1186\/s12879-022-07306-5","journal-title":"BMC Infect Dis"},{"issue":"1","key":"11804_CR16","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1186\/s12879-021-06033-7","volume":"21","author":"W Chen","year":"2021","unstructured":"Chen W, Yang J, Jiang J, He L, Xu Y, Zheng J, Jiang J, Pan X (2021) A spatial analysis of the epidemiology of hiv-infected students in zhejiang province, china. BMC Infect Dis 21(1):430. https:\/\/doi.org\/10.1186\/s12879-021-06033-7","journal-title":"BMC Infect Dis"},{"key":"11804_CR17","doi-asserted-by":"publisher","unstructured":"Vaughan JP, Victora C, R Chowdhury AM (2021) Outbreaks and epidemics. In: Practical Epidemiology: Using Epidemiology to Support Primary Health Care. Oxford University Press, Oxford, UK . https:\/\/doi.org\/10.1093\/med\/9780192848741.003.0006","DOI":"10.1093\/med\/9780192848741.003.0006"},{"key":"11804_CR18","doi-asserted-by":"publisher","unstructured":"Daw M, Daw A, Sifennasr N, Draha A, Daw A, Ahmed M, Mokhtar E, El-Bouzedi A, Daw I, Adam S (2019) Hepatitis &\u00a0HIV, S.W.I.: spatiotemporal analysis and epidemiological characterization of the human immunodeficiency virus (hiv) in libya within a twenty-five-year period: 1993-2017. AIDS Res Ther 16(1), 14 https:\/\/doi.org\/10.1186\/s12981-019-0228-0","DOI":"10.1186\/s12981-019-0228-0"},{"issue":"1","key":"11804_CR19","doi-asserted-by":"publisher","first-page":"0260935","DOI":"10.1371\/journal.pone.0260935","volume":"17","author":"R Kteily-Hawa","year":"2022","unstructured":"Kteily-Hawa R, Hawa AC, Gogolishvili D, Al Akel M, Andruszkiewicz N, Vijayanathan H, Loutfy M (2022) Understanding the epidemiological hiv risk factors and underlying risk context for youth residing in or originating from the middle east and north africa (mena) region: a scoping review of the literature. PLoS ONE 17(1):0260935. https:\/\/doi.org\/10.1371\/journal.pone.0260935","journal-title":"PLoS ONE"},{"issue":"1","key":"11804_CR20","doi-asserted-by":"publisher","first-page":"0231","DOI":"10.1590\/0037-8682-0231-2021","volume":"55","author":"CL Szwarcwald","year":"2022","unstructured":"Szwarcwald CL, Souza J\u00fanior PRB, Pascom ARP, Coelho RA, Ribeiro RA, Damacena GN, Malta DC, Pimenta MC, Pereira GFM (2022) Hiv incidence estimates by sex and age group in the population aged 15 years or over, brazil, 1986\u20132018. Revista da Sociedade Brasileira de Medicina Tropical 55(1):0231. https:\/\/doi.org\/10.1590\/0037-8682-0231-2021","journal-title":"Revista da Sociedade Brasileira de Medicina Tropical"},{"key":"11804_CR21","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1086\/502178","volume":"24","author":"A Do","year":"2003","unstructured":"Do A, Ciesielski C, Metler R, Hammett T, Li J, Fleming P (2003) Occupationally acquired human immunodeficiency virus (hiv) infection: national case surveillance data during 20 years of the hiv epidemic in the united states, Infect Control Hosp Epidemiol Off J Soc Hosp Epidemiol Am 24:86\u201396. https:\/\/doi.org\/10.1086\/502178","journal-title":"Infect Control Hosp Epidemiol Off J Soc Hosp Epidemiol Am"},{"key":"11804_CR22","doi-asserted-by":"publisher","first-page":"6304820","DOI":"10.1155\/2016\/6304820","volume":"2016","author":"P Ngangue","year":"2016","unstructured":"Ngangue P, Bedard E, Zomahoun HT, Payne-Gagnon J, Fournier C, Afounde J, Gagnon M-P (2016) Returning for hiv test results: a systematic review of barriers and facilitators. Int Sch Res Notices 2016:6304820. https:\/\/doi.org\/10.1155\/2016\/6304820","journal-title":"Int Sch Res Notices"},{"issue":"5","key":"11804_CR23","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1093\/cid\/cit281","volume":"57","author":"JK Cornett","year":"2013","unstructured":"Cornett JK, Kirn TJ (2013) Laboratory diagnosis of hiv in adults: a review of current methods. Clin Infect Dis 57(5):712\u2013718. https:\/\/doi.org\/10.1093\/cid\/cit281","journal-title":"Clin Infect Dis"},{"key":"11804_CR24","doi-asserted-by":"crossref","unstructured":"Pal Y (2023) A study to assess the role of community-based organizations in supporting ARV and PrEP before and during COVID-19 in Ho Chi Minh City and Dong Nai Provinces in Vietnam. ASEAN J Psychiatr 24(3)","DOI":"10.54615\/2231-7805.47300"},{"key":"11804_CR25","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s10461-015-1065-2","volume":"20","author":"P Nadol","year":"2016","unstructured":"Nadol P, Tran HV, Hammett TM, Phan SH, Nguyen DD, Kaldor JM, Law M (2016) High HIV prevalence and associated risk factors among female sexual partners of male injection drug users (MWID) in Ho Chi Minh City. Vietnam AIDS Behav 20:395\u2013404. https:\/\/doi.org\/10.1007\/s10461-015-1065-2","journal-title":"Vietnam AIDS Behav"},{"issue":"13","key":"11804_CR26","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1177\/0956462420947555","volume":"31","author":"AT Nguyen","year":"2020","unstructured":"Nguyen AT, Johnston LG, Thanh DC, Le L-VN, Hoang TV, Quang TD, Quoc NC, Nadol P, Hien NT, Abdul-Quader A (2020) Increasing HIV prevalence and injection drug use among men who have sex with men in Ho Chi Minh City. Vietnam Int J STD AIDS 31(13):1247\u20131254. https:\/\/doi.org\/10.1177\/0956462420947555","journal-title":"Vietnam Int J STD AIDS"},{"issue":"2","key":"11804_CR27","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TAC.1972.1099963","volume":"17","author":"P Young","year":"1972","unstructured":"Young P, Shellswell S (1972) Time series analysis, forecasting and control. IEEE Trans Autom Control 17(2):281\u2013283. https:\/\/doi.org\/10.1109\/TAC.1972.1099963","journal-title":"IEEE Trans Autom Control"},{"issue":"2","key":"11804_CR28","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1007\/s40808-020-00890-y","volume":"7","author":"S Roy","year":"2021","unstructured":"Roy S, Bhunia GS, Shit PK (2021) Spatial prediction of covid-19 epidemic using arima techniques in India. Model Earth Syst Environ 7(2):1385\u20131391. https:\/\/doi.org\/10.1007\/s40808-020-00890-y. (Epub 2020 Jul 16)","journal-title":"Model Earth Syst Environ"},{"key":"11804_CR29","doi-asserted-by":"publisher","unstructured":"Dehesh T, Mardani-Fard HA, Dehesh P (2020) Forecasting of covid-19 confirmed cases in different countries with arima models. medRxiv https:\/\/doi.org\/10.1101\/2020.03.13.20035345","DOI":"10.1101\/2020.03.13.20035345"},{"issue":"1","key":"11804_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21010\/Ajidv17i1.1","volume":"17","author":"S Claris","year":"2022","unstructured":"Claris S, Peter N (2022) Arima model in predicting of covid-19 epidemic for the southern africa region. African J Infect Dis 17(1):1\u20139. https:\/\/doi.org\/10.21010\/Ajidv17i1.1","journal-title":"African J Infect Dis"},{"key":"11804_CR31","doi-asserted-by":"publisher","unstructured":"Dey R, Salem FM (2017) Gate-variants of gated recurrent unit (gru) neural networks. In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), pp 1597\u20131600. IEEE . https:\/\/doi.org\/10.1109\/MWSCAS.2017.8053243","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"11804_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D Nonlinear Phenomena 404:132306. https:\/\/doi.org\/10.1016\/j.physd.2019.132306","journal-title":"Physica D Nonlinear Phenomena"},{"key":"11804_CR33","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1017\/S095026881900075X","volume":"147","author":"G Wang","year":"2019","unstructured":"Wang G, Wei W, Jiang J, Ning C, Chen H, Huang J, Liang B, Zang N, Liao Y, Chen R, Lai J, Zhou O, Han J, Liang H, Ye L (2019) Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting hiv incidence in guangxi, china. Epidemiol Infect 147:194. https:\/\/doi.org\/10.1017\/S095026881900075X","journal-title":"Epidemiol Infect"},{"issue":"4","key":"11804_CR34","doi-asserted-by":"publisher","DOI":"10.1099\/jgv.0.001839","volume":"104","author":"W Wei","year":"2023","unstructured":"Wei W, Wang G, Tao X, Luo Q, Chen L, Bao X, Liu Y, Jiang J, Liang H, Ye L (2023) Time series prediction for the epidemic trends of monkeypox using the arima, exponential smoothing, gm (1, 1) and lstm deep learning methods. J Gen Virol 104(4):001839. https:\/\/doi.org\/10.1099\/jgv.0.001839","journal-title":"J Gen Virol"},{"key":"11804_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110227","volume":"140","author":"S Shastri","year":"2020","unstructured":"Shastri S, Singh K, Kumar S, Kour P, Mansotra V (2020) Time series forecasting of covid-19 using deep learning models: India-usa comparative case study. Chaos Solitons Fractals 140:110227. https:\/\/doi.org\/10.1016\/j.chaos.2020.110227","journal-title":"Chaos Solitons Fractals"},{"key":"11804_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110212","volume":"140","author":"F Shahid","year":"2020","unstructured":"Shahid F, Zameer A, Muneeb M (2020) Predictions for covid-19 with deep learning models of lstm, gru and bi-lstm. Chaos Solitons Fractals 140:110212. https:\/\/doi.org\/10.1016\/j.chaos.2020.110212","journal-title":"Chaos Solitons Fractals"},{"key":"11804_CR37","doi-asserted-by":"publisher","unstructured":"Maaliw RR, Mabunga ZP, Villa FT (2021) Time-series forecasting of covid-19 cases using stacked long short-term memory networks. In: 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp 435\u2013441 . https:\/\/doi.org\/10.1109\/3ICT53449.2021.9581688","DOI":"10.1109\/3ICT53449.2021.9581688"},{"key":"11804_CR38","doi-asserted-by":"publisher","unstructured":"Guo X, Yang Y, Tseng S-P, Yin Z, Wang C (2022) Research on hiv\/aids epidemic trend prediction model based on arima-lstm. In: 2022 10th International Conference on Orange Technology (ICOT), pp 1\u20134 . https:\/\/doi.org\/10.1109\/ICOT56925.2022.10008185","DOI":"10.1109\/ICOT56925.2022.10008185"},{"issue":"1","key":"11804_CR39","doi-asserted-by":"publisher","first-page":"17421","DOI":"10.1038\/s41598-021-97037-5","volume":"11","author":"R Ma","year":"2021","unstructured":"Ma R, Zheng X, Wang P, Liu H, Zhang C (2021) The prediction and analysis of covid-19 epidemic trend by combining lstm and markov method. Sci Rep 11(1):17421. https:\/\/doi.org\/10.1038\/s41598-021-97037-5","journal-title":"Sci Rep"},{"issue":"2","key":"11804_CR40","doi-asserted-by":"publisher","first-page":"359","DOI":"10.4314\/tjs.v50i2.15","volume":"50","author":"Z Kaijage","year":"2024","unstructured":"Kaijage Z, Kimaro H, Maziku H (2024) Performance comparison of deep learning models in predicting hiv incidences in tanzania. Tanzania J Sci 50(2):359\u2013375. https:\/\/doi.org\/10.4314\/tjs.v50i2.15","journal-title":"Tanzania J Sci"},{"issue":"1","key":"11804_CR41","doi-asserted-by":"publisher","first-page":"21982","DOI":"10.1186\/s12889-025-21982-3","volume":"25","author":"D Tang","year":"2025","unstructured":"Tang D, Zhang Y, Hu W, Wei K, Zhou Y, Chen S, Zuo Y, Wang C (2025) A study on the performance of predicting monthly aids incidence in xinjiang based on time series and deep learning models. BMC Public Health 25(1):21982. https:\/\/doi.org\/10.1186\/s12889-025-21982-3","journal-title":"BMC Public Health"},{"issue":"1","key":"11804_CR42","doi-asserted-by":"publisher","first-page":"18991","DOI":"10.1038\/s41598-024-69580-4","volume":"14","author":"A Abade","year":"2024","unstructured":"Abade A, Porto LF, Scholze AR, Kuntath D, Silva Barros N, Berra TZ, Ramos ACV, Arc\u00eancio RA, Alves JD (2024) A comparative analysis of classical and machine learning methods for forecasting tb\/hiv co-infection. Sci Rep 14(1):18991. https:\/\/doi.org\/10.1038\/s41598-024-69580-4","journal-title":"Sci Rep"},{"issue":"4","key":"11804_CR43","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/s42979-023-01834-w","volume":"4","author":"M Diqi","year":"2023","unstructured":"Diqi M, Mulyani SH, Pradila R (2023) Deepcov: effective prediction model of covid-19 using cnn algorithm. SN Comput Sci 4(4):396. https:\/\/doi.org\/10.1007\/s42979-023-01834-w","journal-title":"SN Comput Sci"},{"key":"11804_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/8785636","volume":"2021","author":"Z Muhammad Zain","year":"2021","unstructured":"Muhammad Zain Z, Alturki N (2021) Covid-19 pandemic forecasting using cnn-lstm: a hybrid approach. J Control Sci Eng 2021:1\u201323. https:\/\/doi.org\/10.1155\/2021\/8785636","journal-title":"J Control Sci Eng"},{"key":"11804_CR45","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57\u201381. https:\/\/doi.org\/10.1016\/j.aiopen.2021.01.001","journal-title":"AI Open"},{"issue":"1","key":"11804_CR46","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s40537-023-00876-4","volume":"11","author":"B Khemani","year":"2024","unstructured":"Khemani B, Patil S, Kotecha K et al (2024) A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions. J Big Data 11(1):18. https:\/\/doi.org\/10.1186\/s40537-023-00876-4","journal-title":"J Big Data"},{"key":"11804_CR47","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: 6th International Conference on Learning Representations (ICLR) . arXiv:abs\/1707.01926"},{"key":"11804_CR48","doi-asserted-by":"publisher","unstructured":"Wang L, Adiga A, Chen J, Sadilek A, Venkatramanan S, Marathe M (2022) Causalgnn: Causal-based graph neural networks for spatio-temporal epidemic forecasting, vol. 36, pp 12191\u201312199 . https:\/\/doi.org\/10.1609\/aaai.v36i11.21479","DOI":"10.1609\/aaai.v36i11.21479"},{"key":"11804_CR49","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1972\/1\/012107","volume":"1972","author":"Z Wu","year":"2021","unstructured":"Wu Z, Huang M, Zhao A, lan Z (2021) Traffic prediction based on gcn-lstm model. J Phys Conf Ser 1972:012107. https:\/\/doi.org\/10.1088\/1742-6596\/1972\/1\/012107","journal-title":"J Phys Conf Ser"},{"key":"11804_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11277-020-08045-z","volume":"118","author":"N Li","year":"2021","unstructured":"Li N, Hu L, Deng Z-L, Su T, Liu J-W (2021) Research on gru neural network satellite traffic prediction based on transfer learning. Wirel Pers Commun 118:1\u201313. https:\/\/doi.org\/10.1007\/s11277-020-08045-z","journal-title":"Wirel Pers Commun"},{"key":"11804_CR51","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . arXiv:abs\/1609.02907"},{"key":"11804_CR52","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3390\/math12020255","volume":"12","author":"T Zhu","year":"2024","unstructured":"Zhu T, Boada M, Boada MJ (2024) Adaptive graph attention and long short-term memory-based networks for traffic prediction. Mathematics 12:255. https:\/\/doi.org\/10.3390\/math12020255","journal-title":"Mathematics"},{"key":"11804_CR53","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. ICML\u201917, pp 1263\u20131272. JMLR.org. arXiv:abs\/1704.01212"},{"key":"11804_CR54","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2017) Graph attention networks. 6th International Conference on Learning Representations"},{"key":"11804_CR55","doi-asserted-by":"publisher","unstructured":"Reid S, Nicolis O, Peralta B (2021) Predicting the covid-19 in the metropolitan region (chile) using a gcn-lstm neural network. In: 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), pp 1\u20136 https:\/\/doi.org\/10.1109\/CHILECON54041.2021.9702969","DOI":"10.1109\/CHILECON54041.2021.9702969"},{"key":"11804_CR56","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.fmre.2021.07.007","volume":"2","author":"Y Wang","year":"2021","unstructured":"Wang Y, Zeng D, Zhang Q, Zhao AP, Wang X, Wang Q, Yin L, Cao Z (2021) Adaptively temporal graph convolution model for epidemic prediction of multiple age groups. Fundam Res 2:311\u2013320. https:\/\/doi.org\/10.1016\/j.fmre.2021.07.007","journal-title":"Fundam Res"},{"key":"11804_CR57","doi-asserted-by":"publisher","unstructured":"Bai S, Kolter J, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling https:\/\/doi.org\/10.48550\/arXiv.1803.01271","DOI":"10.48550\/arXiv.1803.01271"},{"issue":"4","key":"11804_CR58","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1093\/jamia\/ocaa322","volume":"28","author":"J Gao","year":"2021","unstructured":"Gao J, Sharma R, Qian C, Glass LM, Spaeder J, Romberg J, Sun J, Xiao C (2021) Stan: spatio-temporal attention network for pandemic prediction using real-world evidence. J Am Med Inform Assoc 28(4):733\u2013743. https:\/\/doi.org\/10.1093\/jamia\/ocaa322","journal-title":"J Am Med Inform Assoc"},{"key":"11804_CR59","doi-asserted-by":"publisher","unstructured":"Zheng S, Gao Z, Cao W, Bian J, Liu T-Y (2021) Hierst: A unified hierarchical spatial-temporal framework for covid-19 trend forecasting. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. CIKM \u201921, pp 4383\u20134392. Association for Computing Machinery, New York, NY, USA . https:\/\/doi.org\/10.1145\/3459637.3481927","DOI":"10.1145\/3459637.3481927"},{"issue":"4","key":"11804_CR60","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1007\/s10618-024-01035-w","volume":"38","author":"M Qiu","year":"2024","unstructured":"Qiu M, Tan Z, Bao B-K (2024) MSGNN: a multi-scale spatio-temporal graph neural network for epidemic forecasting. Data Min Knowl Disc 38(4):2348\u20132376. https:\/\/doi.org\/10.1007\/s10618-024-01035-w","journal-title":"Data Min Knowl Disc"},{"key":"11804_CR61","doi-asserted-by":"publisher","unstructured":"Brockschmidt M (2020) Gnn-film: graph neural networks with feature-wise linear modulation. In: Proceedings of the 37th International Conference on Machine Learning. ICML\u201920. JMLR.org, Vienna, Austria . https:\/\/doi.org\/10.5555\/3524938.3525045","DOI":"10.5555\/3524938.3525045"},{"key":"11804_CR62","unstructured":"Yang Y, Li D (2020) Nenn: Incorporate node and edge features in graph neural networks. In: Pan, S.J., Sugiyama, M. (eds.) Proceedings of the 12th Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 129, pp 593\u2013608. PMLR, Virtual Conference . https:\/\/proceedings.mlr.press\/v129\/yang20a.html"},{"key":"11804_CR63","doi-asserted-by":"publisher","unstructured":"Gong L, Cheng Q (2019) Exploiting edge features for graph neural networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 9203\u20139211 . https:\/\/doi.org\/10.1109\/CVPR.2019.00943","DOI":"10.1109\/CVPR.2019.00943"},{"key":"11804_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-023-10577-2","volume":"56","author":"C Sun","year":"2023","unstructured":"Sun C, Li C, Lin X, Zheng T, Meng F, Rui X, Wang Z (2023) Attention-based graph neural networks: a survey. Artif Int Rev 56:1\u201348. https:\/\/doi.org\/10.1007\/s10462-023-10577-2","journal-title":"Artif Int Rev"},{"key":"11804_CR65","doi-asserted-by":"publisher","unstructured":"Brody S, Alon U, Yahav E (2021) How attentive are graph attention networks? arXiv preprint arXiv:2105.14491https:\/\/doi.org\/10.48550\/arXiv.2105.14491","DOI":"10.48550\/arXiv.2105.14491"},{"key":"11804_CR66","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS\u201917, pp 6000\u20136010. Curran Associates Inc., Red Hook, NY, USA"},{"key":"11804_CR67","doi-asserted-by":"publisher","unstructured":"Liu C, Zhan Y, Wu J, Li C, Du B, Hu W, Liu T, Tao D (2023) Graph pooling for graph neural networks: progress, challenges, and opportunities. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. IJCAI 23 . https:\/\/doi.org\/10.24963\/ijcai.2023\/752","DOI":"10.24963\/ijcai.2023\/752"},{"issue":"2","key":"11804_CR68","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1109\/TNNLS.2022.3190922","volume":"35","author":"D Grattarola","year":"2024","unstructured":"Grattarola D, Zambon D, Bianchi FM, Alippi C (2024) Understanding pooling in graph neural networks. IEEE Trans Neural Netw Learn Syst 35(2):2708\u20132718. https:\/\/doi.org\/10.1109\/TNNLS.2022.3190922","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11804_CR69","unstructured":"Ying R, You J, Morris C, Ren X, Hamilton WL, Leskovec J (2018) Hierarchical graph representation learning with differentiable pooling. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. NIPS\u201918, pp 4805\u20134815. Curran Associates Inc., Red Hook, NY, USA"},{"key":"11804_CR70","doi-asserted-by":"publisher","unstructured":"Bianchi FM, Grattarola D, Alippi C (2020) Mincut pooling in graph neural networks. In: International Conference on Learning Representations (ICLR) https:\/\/doi.org\/10.48550\/arXiv.1907.00481 . https:\/\/arxiv.org\/abs\/1907.00481","DOI":"10.48550\/arXiv.1907.00481"},{"key":"11804_CR71","unstructured":"Yuan H, Ji S (2020) Structpool: Structured graph pooling via conditional random fields. In: International Conference on Learning Representations"},{"key":"11804_CR72","doi-asserted-by":"publisher","unstructured":"Ahmadi A, Moradi P (2020) Memory-based graph networks https:\/\/doi.org\/10.48550\/arXiv.2002.09518","DOI":"10.48550\/arXiv.2002.09518"},{"issue":"9","key":"11804_CR73","doi-asserted-by":"publisher","first-page":"4948","DOI":"10.1109\/TPAMI.2021.3081010","volume":"44","author":"H Gao","year":"2022","unstructured":"Gao H, Ji S (2022) Graph u-nets. IEEE Trans Pattern Anal Mach Intell 44(9):4948\u20134960. https:\/\/doi.org\/10.1109\/TPAMI.2021.3081010","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11804_CR74","unstructured":"Lee J, Lee I, Kang J (2019) Self-attention graph pooling. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp 3734\u20133743. PMLR, Long Beach, California, USA .https:\/\/proceedings.mlr.press\/v97\/lee19c.html"},{"key":"11804_CR75","doi-asserted-by":"publisher","unstructured":"Zhang L, Wang X, Li H, Zhu G, Shen P, Li P, Lu X, Shah SAA, Bennamoun M (2020) Structure-feature based graph self-adaptive pooling. In: Proceedings of The Web Conference 2020. WWW \u201920, pp 3098\u20133104. Association for Computing Machinery, New York, NY, USA . https:\/\/doi.org\/10.1145\/3366423.3380083","DOI":"10.1145\/3366423.3380083"},{"issue":"9","key":"11804_CR76","doi-asserted-by":"publisher","first-page":"5032","DOI":"10.1109\/TNNLS.2021.3067441","volume":"33","author":"X Gao","year":"2021","unstructured":"Gao X, Dai W, Li C, Xiong H, Frossard P (2021) ipool-information-based pooling in hierarchical graph neural networks. IEEE Trans Neural Netw Learn Syst 33(9):5032\u20135044. https:\/\/doi.org\/10.1109\/TNNLS.2021.3067441","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11804_CR77","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2019) How powerful are graph neural networks? In: International Conference on Learning Representations (ICLR)"},{"key":"11804_CR78","unstructured":"Bruna J, Zaremba W, Szlam A, Lecun Y (2013) Spectral networks and locally connected networks on graphs. In: CoRR Abs\/1312.6203"},{"key":"11804_CR79","doi-asserted-by":"publisher","unstructured":"Pan Y, Zhang M, Chen Z, Zhou M, Zhang Z (2016) An arima based model for forecasting the patient number of epidemic disease. In: 2016 13th International Conference on Service Systems and Service Management (ICSSSM), pp 1\u20134 . https:\/\/doi.org\/10.1109\/ICSSSM.2016.7538560","DOI":"10.1109\/ICSSSM.2016.7538560"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11804-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11804-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11804-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T17:08:03Z","timestamp":1774372083000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11804-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":79,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["11804"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11804-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"11 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Ethical approval was not considered necessary for this study. The data set used in this investigation is fully encoded and contains no personal information, such as names, addresses, or personal identification numbers. All information is encoded as numerical values represented in the dataset, and the original values cannot be retrieved from these encoded values. This study uses retrospective data previously recorded to ensure compliance with ethical guidelines and information security. The research is not conducted on human subjects but only aims to forecast the number of new infections in regions.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"119"}}