{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:01:07Z","timestamp":1774026067831,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["883441"],"award-info":[{"award-number":["883441"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models.<\/jats:p>","DOI":"10.3390\/s22103658","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"3658","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0448-9459","authenticated-orcid":false,"given":"Ioannis","family":"Kavouras","sequence":"first","affiliation":[{"name":"School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5944-0455","authenticated-orcid":false,"given":"Maria","family":"Kaselimi","sequence":"additional","affiliation":[{"name":"School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3876-0024","authenticated-orcid":false,"given":"Eftychios","family":"Protopapadakis","sequence":"additional","affiliation":[{"name":"School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3106-4758","authenticated-orcid":false,"given":"Nikolaos","family":"Bakalos","sequence":"additional","affiliation":[{"name":"School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4064-8990","authenticated-orcid":false,"given":"Nikolaos","family":"Doulamis","sequence":"additional","affiliation":[{"name":"School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0612-5889","authenticated-orcid":false,"given":"Anastasios","family":"Doulamis","sequence":"additional","affiliation":[{"name":"School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15772 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1159\/000507423","article-title":"COVID-19 Outbreak: An Overview","volume":"64","author":"Ciotti","year":"2019","journal-title":"Chemotherapy"},{"key":"ref_2","unstructured":"WHO (2022, March 22). Events as They Happen, Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/events-as-they-happen."},{"key":"ref_3","first-page":"157","article-title":"WHO Declares COVID-19 a Pandemic","volume":"91","author":"Cucinotta","year":"2022","journal-title":"Atenei Parm."},{"key":"ref_4","unstructured":"WHO (2022, March 22). Timeline: WHO\u2019s COVID-19 Response, Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/interactive-timeline."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"58","DOI":"10.15585\/mmwr.mm7002e4","article-title":"Mitigation Policies and COVID-19-Associated Mortality-37 European Countries, 23 January\u201330 June 2020","volume":"70","author":"Fuller","year":"2021","journal-title":"Morb. Mortal. Wkly. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"AlZu\u2019bi, S., Aqel, D., and Mughaid, A. (2021, January 14\u201315). Recent intelligent Approaches for Managing and Optimizing smart Blood Donation process. Proceedings of the 2021 International Conference on Information Technology (ICIT), Amman, Jordan.","DOI":"10.1109\/ICIT52682.2021.9491125"},{"key":"ref_7","unstructured":"Bakalos, N., Kaselimi, M., Doulamis, A., and Doulamis, N. (July, January 29). Bioinformatics Systems for Monitoring and Mitigating Epidemics: The STAMINA Paradigm. Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference, Virtual Event."},{"key":"ref_8","unstructured":"Voulodimos, A., Protopapadakis, E., Katsamenis, I., Doulamis, A., and Doulamis, N. (July, January 29). Deep learning models for COVID-19 infected area segmentation in CT images. Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference, Virtual Event."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100815","DOI":"10.1016\/j.imu.2021.100815","article-title":"Forecasting the spread of the third wave of COVID-19 pandemic using time series analysis in Bangladesh","volume":"28","author":"Kibria","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Maaliw, R.R., Ballera, M.A., Mabunga, Z.P., Mahusay, A.T., Dejelo, D.A., and Se\u00f1o, M.P. (2021, January 27\u201330). An Ensemble Machine Learning Approach For Time Series Forecasting of COVID-19 Cases. Proceedings of the 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON53756.2021.9623074"},{"key":"ref_11","unstructured":"Tandon, H., Ranjan, P., Chakraborty, T., and Suhag, V. (2020). Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.procs.2021.01.036","article-title":"Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET","volume":"179","author":"Satrio","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jain, A., Sukhdeve, T., Gadia, H., Sahu, S.P., and Verma, S. (2021, January 25\u201327). COVID-19 Prediction using Time Series Analysis. Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), online.","DOI":"10.1109\/ICAIS50930.2021.9395877"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"116611","DOI":"10.1016\/j.eswa.2022.116611","article-title":"Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India","volume":"195","author":"Verma","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110227","DOI":"10.1016\/j.chaos.2020.110227","article-title":"Time series forecasting of COVID-19 using deep learning models: India-USA comparative case study","volume":"140","author":"Shastri","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110121","DOI":"10.1016\/j.chaos.2020.110121","article-title":"Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study","volume":"140","author":"Zeroual","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6686745","DOI":"10.1155\/2021\/6686745","article-title":"Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia","volume":"2021","author":"Omran","year":"2021","journal-title":"Complexity"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109309","DOI":"10.1016\/j.petrol.2021.109309","article-title":"Time-series production forecasting method based on the integration of Bidirectional Gated Recurrent Unit (Bi-GRU) network and Sparrow Search Algorithm (SSA)","volume":"208","author":"Li","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_19","unstructured":"Barman, A. (2020). Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and ARIMA Models. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109864","DOI":"10.1016\/j.chaos.2020.109864","article-title":"Time series forecasting of COVID-19 transmission in Canada using LSTM networks","volume":"135","author":"Chimmula","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1007\/s00521-021-06548-9","article-title":"A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting","volume":"34","author":"Abbasimehr","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.irbm.2020.05.003","article-title":"Deep Transfer Learning Based Classification Model for COVID-19 Disease","volume":"43","author":"Pathak","year":"2020","journal-title":"IRBM"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00500-021-06490-x","article-title":"India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability","volume":"26","author":"Ketu","year":"2022","journal-title":"Soft Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rauf, H.T., Lali, M.I.U., Khan, M.A., Kadry, S., Alolaiyan, H., Razaq, A., and Irfan, R. (2021). Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks. Pers. Ubiquitous Comput., 1\u201318.","DOI":"10.1007\/s00779-020-01494-0"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101574","DOI":"10.1016\/j.asej.2021.08.016","article-title":"Performance evaluation of regression models for COVID-19: A statistical and predictive perspective","volume":"13","author":"Khan","year":"2022","journal-title":"Ain Shams Eng. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"110214","DOI":"10.1016\/j.chaos.2020.110214","article-title":"Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran","volume":"140","author":"Wang","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_27","unstructured":"Yudistira, N. (2020). COVID-19 growth prediction using multivariate long short term memory. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Carpenter, M., Luo, C., and Wang, X.S. (2021). The effects of regularisation on RNN models for time series forecasting: COVID-19 as an example. arXiv.","DOI":"10.1109\/IUCC-CIT-DSCI-SmartCNS55181.2021.00054"},{"key":"ref_29","unstructured":"Bernardini, A., and De Fina, S. (1991, January 14\u201317). A neural network to approximate nonlinear functions. Proceedings of the 34th Midwest Symposium on Circuits and Systems, Monterey, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, M.X., Firat, O., Bapna, A., Johnson, M., Macherey, W., Foster, G., Jones, L., Parmar, N., Schuster, M., and Chen, Z. (2018). The best of both worlds: Combining recent advances in neural machine translation. arXiv.","DOI":"10.18653\/v1\/P18-1008"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.1109\/TSG.2020.2974347","article-title":"Context aware energy disaggregation using adaptive bidirectional LSTM models","volume":"11","author":"Kaselimi","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_33","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","unstructured":"Ritchie, H., Lucas Rod\u00e9s-Guirao, E.M., Charlie Giattino, C.A., Ortiz-Ospina, E., Hasell, J., Macdonald, B., Beltekian, D., and Roser, M. (2020). Coronavirus Pandemic (COVID-19). Our World in Data, Available online: https:\/\/ourworldindata.org\/coronavirus."},{"key":"ref_35","unstructured":"OurWorldInData (2022, March 20). Research and Data to Make Progress Against the World\u2019s Largest Problems. Available online: https:\/\/ourworldindata.org\/coronavirus#explore-the-global-situation."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3658\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:09:12Z","timestamp":1760137752000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3658"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,11]]},"references-count":35,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103658"],"URL":"https:\/\/doi.org\/10.3390\/s22103658","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,11]]}}}