{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:31:03Z","timestamp":1759336263469,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031610332"},{"type":"electronic","value":"9783031610349"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-61034-9_3","type":"book-chapter","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T16:02:20Z","timestamp":1715616140000},"page":"30-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Active Sensing for\u00a0Epidemic State Estimation Using ABM-Guided Machine Learning"],"prefix":"10.1007","author":[{"given":"Sami","family":"Saliba","sequence":"first","affiliation":[]},{"given":"Faraz","family":"Dadgostari","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Hoops","sequence":"additional","affiliation":[]},{"given":"Henning S.","family":"Mortveit","sequence":"additional","affiliation":[]},{"given":"Samarth","family":"Swarup","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,14]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Ahmed, N., et al.: A survey of COVID-19 contact tracing apps. IEEE Access 8, 134577\u2013134601 (2020)","key":"3_CR1","DOI":"10.1109\/ACCESS.2020.3010226"},{"unstructured":"Angione, C., Silverman, E., Yaneske, E.: Using machine learning to emulate agent-based simulations. arXiv:2005.02077 [cs.MA] (2020)","key":"3_CR2"},{"issue":"7883","key":"3_CR3","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/s41586-021-04014-z","volume":"599","author":"H Bastani","year":"2021","unstructured":"Bastani, H., et al.: Efficient and targeted COVID-19 border testing via reinforcement learning. Nature 599(7883), 108\u2013113 (2021)","journal-title":"Nature"},{"doi-asserted-by":"crossref","unstructured":"Boers, Y., Driessen, H., Bagchi, A., Mandal, P.: Particle filter based entropy. In: Proceedings of the 13th International Conference on Information Fusion (2010)","key":"3_CR4","DOI":"10.1109\/ICIF.2010.5712013"},{"key":"3_CR5","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47845-2","volume-title":"An Introduction to Sequential Monte Carlo","author":"N Chopin","year":"2020","unstructured":"Chopin, N., Papaspiliopoulos, O.: An Introduction to Sequential Monte Carlo. SSS, Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47845-2"},{"doi-asserted-by":"publisher","unstructured":"Cramer, E.Y., et al.: US COVID-19 forecast hub consortium: the United States COVID-19 forecast hub dataset. medRxiv: https:\/\/doi.org\/10.1101\/2021.11.04.21265886v1 (2021)","key":"3_CR6","DOI":"10.1101\/2021.11.04.21265886v1"},{"doi-asserted-by":"crossref","unstructured":"Franceschi, V.B., et al.: Population-based prevalence surveys during the Covid-19 pandemic: a systematic review. Rev. Med. Virol. 31(4) (2021)","key":"3_CR7","DOI":"10.1002\/rmv.2200"},{"doi-asserted-by":"crossref","unstructured":"Hoops, S., et al.: High performance agent-based modeling to study realistic contact tracing protocols. In: Proceedings of the Winter Simulation Conference (2021)","key":"3_CR8","DOI":"10.1109\/WSC52266.2021.9715382"},{"doi-asserted-by":"crossref","unstructured":"Lueck, J., Rife, J.H., Swarup, S., Uddin, N.: Who goes there? Using an agent-based simulation for tracking population movement. In: Mustafee, N., et al. (eds.) Proceedings of the Winter Simulation Conference (WSC). National Harbor, MD, USA (2019)","key":"3_CR9","DOI":"10.1109\/WSC40007.2019.9004861"},{"doi-asserted-by":"crossref","unstructured":"Ma, Q., et al.: Global percentage of asymptomatic SARS-CoV-2 infections among the tested population and individuals with confirmed COVID-19 diagnosis. JAMA Netw. Open 4(12), e2137257 (2021)","key":"3_CR10","DOI":"10.1001\/jamanetworkopen.2021.37257"},{"doi-asserted-by":"crossref","unstructured":"Ma, X., Karkus, P., Hsu, D., Lee, W.S.: Particle filter recurrent neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 5101\u20135108 (2020)","key":"3_CR11","DOI":"10.1609\/aaai.v34i04.5952"},{"doi-asserted-by":"crossref","unstructured":"Malleson, N., Minors, K., Kieu, L.M., Ward, J.A., West, A., Heppenstall, A.: Simulating crowds in real time with agent-based modelling and a particle filter. J. Artif. Soc. Soc. Simul. 23(3) (2020)","key":"3_CR12","DOI":"10.18564\/jasss.4266"},{"doi-asserted-by":"crossref","unstructured":"de\u00a0Mooij, J., Dell\u2019Anna, D., Bhattacharya, P., Dastani, M., Logan, B., Swarup, S.: Quantifying the effects of norms on COVID-19 cases using an agent-based simulation. In: Van\u00a0Dam, K.H., Verstaevel, N. (eds.) Multi-Agent-Based Simulation XXII, pp. 99\u2013112 (2022)","key":"3_CR13","DOI":"10.1007\/978-3-030-94548-0_8"},{"issue":"6","key":"3_CR14","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/S2352-4642(21)00060-2","volume":"5","author":"L Rennert","year":"2021","unstructured":"Rennert, L., et al.: Surveillance-based informative testing for detection and containment of SARS-CoV-2 outbreaks on a public university campus: an observational and modelling study. Lancet Child Adolesc. Health 5(6), 428\u2013436 (2021)","journal-title":"Lancet Child Adolesc. Health"},{"unstructured":"Thorve, S., et al.: An active learning method for the comparison of agent-based models. In: Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (2020)","key":"3_CR15"},{"key":"3_CR16","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.cobeha.2016.06.009","volume":"11","author":"SCH Yang","year":"2016","unstructured":"Yang, S.C.H., Wolpert, D.M., Lengyel, M.: Theoretical perspectives on active sensing. Curr. Opin. Behav. Sci. 11, 100\u2013108 (2016)","journal-title":"Curr. Opin. Behav. Sci."}],"container-title":["Lecture Notes in Computer Science","Multi-Agent-Based Simulation XXIV"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-61034-9_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T16:02:59Z","timestamp":1715616179000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-61034-9_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031610332","9783031610349"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-61034-9_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"14 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MABS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Multi-Agent Systems and Agent-Based Simulation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mabs2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mabsworkshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}