{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:57:00Z","timestamp":1760147820216,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EPSRC","award":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"],"award-info":[{"award-number":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"]}]},{"DOI":"10.13039\/501100000266","name":"ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems Environments","doi-asserted-by":"publisher","award":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"],"award-info":[{"award-number":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"AWE","doi-asserted-by":"publisher","award":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"],"award-info":[{"award-number":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"EPSRC Centre for Doctoral Training in Distributed Algorithms","doi-asserted-by":"publisher","award":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"],"award-info":[{"award-number":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"EPSRC through the Big Hypotheses","doi-asserted-by":"publisher","award":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"],"award-info":[{"award-number":["EP\/L015927\/1","EP\/R512011\/1","EP\/S023445\/1","EP\/R018537\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin\/destination (O\/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease.<\/jats:p>","DOI":"10.3390\/info14030170","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T03:11:39Z","timestamp":1678158699000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin\/Destination Matrices to Inform Disease Models"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8394-7344","authenticated-orcid":false,"given":"Conor","family":"Rosato","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert E.","family":"Moore","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0368-7042","authenticated-orcid":false,"given":"Matthew","family":"Carter","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Heap","sequence":"additional","affiliation":[{"name":"Computational Biology Facility, University of Liverpool, Liverpool L69 3GJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9606-9480","authenticated-orcid":false,"given":"John","family":"Harris","sequence":"additional","affiliation":[{"name":"Public Health England, London NW9 5EQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0559-5176","authenticated-orcid":false,"given":"Jose","family":"Storopoli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universidade Nove de Julho\u2014UNINOVE, Sao Paulo 03155-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1917-2913","authenticated-orcid":false,"given":"Simon","family":"Maskell","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"ref_1","unstructured":"(2023, March 03). Coronavirus Disease 2019. Available online: https:\/\/www.google.com\/search?q=covid-19+cases+worldwide&rlz=1C1CHBF_enGB763GB763&sxsrf=AJOqlzVAHRTMaItK2GPe9r5WtVyiju1d9g%3A1677849490518&ei=kvMBZO6lH4SW8gL377G4Dg&ved=0ahUKEwjutvm27L_9AhUEi1wKHfd3DOcQ4dUDCA8&uact=5&oq=covid-19+cases+worldwide&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCAAQgAQyBQgAEIAEMgYIABAWEB4yBggAEBYQHjIGCAAQFhAeMgYIABAWEB4yBggAEBYQHjIGCAAQFhAeMgYIABAWEB4yBggAEBYQHjoKCAAQRxDWBBCwAzoECAAQQ0oECEEYAFDLBFjOEWCFEmgBcAB4AIABWIgB8QSSAQE5mAEAoAEByAEIwAEB&sclient=gws-wiz-serpt."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","article-title":"An interactive web-based dashboard to track COVID-19 in real time","volume":"20","author":"Dong","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_3","first-page":"700","article-title":"A contribution to the mathematical theory of epidemics","volume":"115","author":"Kermack","year":"1927","journal-title":"Proc. R. Soc. London. Ser. A Contain Pap. Math. Phys. Charact."},{"key":"ref_4","unstructured":"(2021, October 01). Reproduction Number (R) and Growth Rate: Methodology, Available online: https:\/\/www.gov.uk\/government\/publications\/reproduction-number-r-and-growth-rate-methodology\/reproduction-number-r-and-growth-rate-methodology."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20200279","DOI":"10.1098\/rstb.2020.0279","article-title":"Real-time nowcasting and forecasting of COVID-19 dynamics in England: The first wave","volume":"376","author":"Birrell","year":"2021","journal-title":"Philos. Trans. R. Soc. B"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"34053254","DOI":"10.1038\/s41598-021-86266-3","article-title":"Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England","volume":"11","author":"Leclerc","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_7","first-page":"09622802211070257","article-title":"Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number","volume":"2022","author":"Keeling","year":"2022","journal-title":"Stat. Methods Med. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"20210305","DOI":"10.1098\/rsta.2021.0305","article-title":"Refining epidemiological forecasts with simple scoring rules","volume":"380","author":"Moore","year":"2022","journal-title":"Philos. Trans. R. Soc. A"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Funk, S., Abbott, S., Atkins, B.D., Baguelin, M., Baillie, J.K., Birrell, P., Blake, J., Bosse, N.I., Burton, J., and Carruthers, J. (2020). Short-term forecasts to inform the response to the Covid-19 epidemic in the UK. MedRxiv.","DOI":"10.1101\/2020.11.11.20220962"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Overton, C.E., Pellis, L., Stage, H.B., Scarabel, F., Burton, J., Fraser, C., Hall, I., House, T.A., Jewell, C., and Nurtay, A. (2022). EpiBeds: Data informed modelling of the COVID-19 hospital burden in England. PLoS Comput. Biol., 18.","DOI":"10.1371\/journal.pcbi.1010406"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1111\/j.1541-0420.2009.01191.x","article-title":"Predictive model assessment for count data","volume":"65","author":"Czado","year":"2009","journal-title":"Biometrics"},{"key":"ref_12","unstructured":"Aramaki, E., Maskawa, S., and Morita, M. (2011, January 27\u201331). Twitter catches the flu: Detecting influenza epidemics using Twitter. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e3532","DOI":"10.2196\/jmir.3532","article-title":"The reliability of tweets as a supplementary method of seasonal influenza surveillance","volume":"16","author":"Aslam","year":"2014","journal-title":"J. Med. Internet Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Broniatowski, D.A., Paul, M.J., and Dredze, M. (2013). National and local influenza surveillance through Twitter: An analysis of the 2012\u20132013 influenza epidemic. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0083672"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e1157","DOI":"10.2196\/jmir.1157","article-title":"Infodemiology and infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet","volume":"11","author":"Eysenbach","year":"2009","journal-title":"J. Med. Internet Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.H., and Liu, B. (2011, January 10\u201315). Predicting flu trends using twitter data. Proceedings of the 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Toronto, ON, Canada.","DOI":"10.1109\/INFCOMW.2011.5928903"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1016\/j.ipm.2018.04.011","article-title":"Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification","volume":"56","author":"Thapen","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2200177","DOI":"10.2807\/1560-7917.ES.2022.27.39.2200177","article-title":"Epitweetr: Early warning of public health threats using Twitter data","volume":"27","author":"Espinosa","year":"2022","journal-title":"Eurosurveillance"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109603","DOI":"10.1016\/j.asoc.2022.109603","article-title":"Twitter conversations predict the daily confirmed COVID-19 cases","volume":"129","author":"Lamsal","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Thakur, N. (2022). A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave. Data, 7.","DOI":"10.36227\/techrxiv.20363742.v1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.3390\/covid2080076","article-title":"An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection","volume":"2","author":"Thakur","year":"2022","journal-title":"COVID"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"ofaa258","DOI":"10.1093\/ofid\/ofaa258","article-title":"An \u201cinfodemic\u201d: Leveraging high-volume Twitter data to understand early public sentiment for the coronavirus disease 2019 outbreak","volume":"Volume 7","author":"Medford","year":"2020","journal-title":"Proceedings of the Open Forum Infectious Diseases"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e26769","DOI":"10.2196\/26769","article-title":"Monitoring depression trends on twitter during the COVID-19 pandemic: Observational study","volume":"1","author":"Zhang","year":"2021","journal-title":"JMIR Infodemiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e19447","DOI":"10.2196\/19447","article-title":"Global sentiments surrounding the COVID-19 pandemic on Twitter: Analysis of Twitter trends","volume":"6","author":"Lwin","year":"2020","journal-title":"JMIR Public Health Surveill."},{"key":"ref_25","unstructured":"Sharma, K., Seo, S., Meng, C., Rambhatla, S., and Liu, Y. (2020). COVID-19 on social media: Analyzing misinformation in twitter conversations. arXiv."},{"key":"ref_26","unstructured":"Al-Garadi, M.A., Yang, Y.C., Lakamana, S., and Sarker, A. (2023, March 06). A Text Classification Approach for the Automatic Detection of Twitter Posts Containing Self-Reported COVID-19 Symptoms. Available online: https:\/\/openreview.net\/forum?id=xyGSIttHYO."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1093\/jamia\/ocaa116","article-title":"Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource","volume":"27","author":"Sarker","year":"2020","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107057","DOI":"10.1016\/j.asoc.2020.107057","article-title":"Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA","volume":"101","author":"Garcia","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kar, D., Bhardwaj, M., Samanta, S., and Azad, A.P. (February, January 18). No rumours please! A multi-indic-lingual approach for COVID fake-tweet detection. Proceedings of the 2021 Grace Hopper Celebration India (GHCI), Bangalore, India.","DOI":"10.1109\/GHCI50508.2021.9514012"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/S1473-3099(20)30553-3","article-title":"Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study","volume":"20","author":"Badr","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Goel, R., and Sharma, R. (2020, January 7\u201310). Mobility based sir model for pandemics-with case study of covid-19. Proceedings of the 2020 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), The Hague, The Netherlands.","DOI":"10.1109\/ASONAM49781.2020.9381457"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.cities.2019.03.006","article-title":"Social media and urban mobility: Using twitter to calculate home-work travel matrices","volume":"89","year":"2019","journal-title":"Cities"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, Z., Jiang, Y., Li, X., and Porter, D. (2020). Twitter reveals human mobility dynamics during the COVID-19 pandemic. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0241957"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lombardi, A., Amoroso, N., Monaco, A., Tangaro, S., and Bellotti, R. (2021). Complex Network Modelling of Origin\u2013Destination Commuting Flows for the COVID-19 Epidemic Spread Analysis in Italian Lombardy Region. Appl. Sci., 11.","DOI":"10.3390\/app11104381"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.1038\/s41598-019-38722-4","article-title":"Impact of origin-destination information in epidemic spreading","volume":"9","author":"Meloni","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"18951","DOI":"10.1038\/s41598-021-97170-1","article-title":"Simulating the impacts of interregional mobility restriction on the spatial spread of COVID-19 in Japan","volume":"11","author":"Kondo","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/s41586-020-2405-7","article-title":"Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe","volume":"584","author":"Flaxman","year":"2020","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100457","DOI":"10.1016\/j.eclinm.2020.100457","article-title":"Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking","volume":"25","author":"Vinceti","year":"2020","journal-title":"EClinicalMedicine"},{"key":"ref_39","unstructured":"CoDatMo (2021, October 01). 2021 Welcome to the CoDatMo Site. Available online: https:\/\/codatmo.github.io."},{"key":"ref_40","unstructured":"UK Government (2021, October 01). 2021 Coronavirus (COVID-19) in the UK, Available online: https:\/\/coronavirus.data.gov.uk\/details\/deaths."},{"key":"ref_41","unstructured":"UK Government (2021, October 01). 2021 Coronavirus (COVID-19) in the UK, Available online: https:\/\/coronavirus.data.gov.uk\/details\/healthcare."},{"key":"ref_42","unstructured":"(2021, October 01). Zoe App: COVID-Public-Data. Available online: https:\/\/console.cloud.google.com\/storage\/browser\/covid-public-data;tab=objects?prefix=&forceOnObjectsSortingFiltering=false."},{"key":"ref_43","unstructured":"(2021, October 01). Potential Coronavirus (COVID-19) Symptoms Reported through NHS Pathways and 111 Online. Available online: https:\/\/digital.nhs.uk\/data-and-information\/publications\/statistical\/mi-potential-covid-19-symptoms-reported-through-nhs-pathways-and-111-online\/latest."},{"key":"ref_44","unstructured":"Roesslein, J. (2012, May 08). Tweepy Documentation. 2009, Volume 5, p. 724. Available online: http:\/\/tweepy.readthedocs.io\/en\/v3."},{"key":"ref_45","unstructured":"(2021, October 01). COVID-19 Terms and MedDRA. Available online: https:\/\/www.meddra.org\/COVID-19-terms-and-MedDRA."},{"key":"ref_46","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Leetaru, K., Wang, S., Cao, G., Padmanabhan, A., and Shook, E. (2013). Mapping the global Twitter heartbeat: The geography of Twitter. First Monday, Available online: https:\/\/journals.uic.edu\/ojs\/index.php\/fm\/article\/view\/4366.","DOI":"10.5210\/fm.v18i5.4366"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1430202","DOI":"10.18637\/jss.v076.i01","article-title":"Stan: A probabilistic programming language","volume":"76","author":"Carpenter","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_49","first-page":"1593","article-title":"The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo","volume":"15","author":"Hoffman","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, Z., Heckman, C., Julier, S., and Ahmed, N. (2018, January 10\u201313). Weak in the NEES?: Auto-tuning Kalman filters with Bayesian optimization. Proceedings of the 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK.","DOI":"10.23919\/ICIF.2018.8454982"},{"key":"ref_51","unstructured":"(2022, October 24). Modelling the Coronavirus Epidemic in a City with Python. Available online: https:\/\/towardsdatascience.com\/modelling-the-coronavirus-epidemic-spreading-in-a-city-with-python-babd14d82fa2."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.1038\/s41467-017-02064-4","article-title":"Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics","volume":"8","author":"Wesolowski","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3389\/fdata.2019.00005","article-title":"Location Prediction for Tweets","volume":"2","author":"Huang","year":"2019","journal-title":"Front. Big Data"},{"key":"ref_54","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/j.1467-9868.2006.00553.x","article-title":"Sequential monte carlo samplers","volume":"68","author":"Doucet","year":"2006","journal-title":"J. R. Stat. Soc. Ser. (Statist. Methodol.)"},{"key":"ref_56","unstructured":"Devlin, L., Horridge, P., Green, P.L., and Maskell, S. (2021). The No-U-Turn sampler as a proposal distribution in a sequential Monte Carlo sampler with a near-optimal L-kernel. arXiv."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/3\/170\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:49:39Z","timestamp":1760122179000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/3\/170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,7]]},"references-count":56,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["info14030170"],"URL":"https:\/\/doi.org\/10.3390\/info14030170","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2023,3,7]]}}}