{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T01:42:41Z","timestamp":1791337361695,"version":"4.3.1"},"reference-count":84,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"12","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.science.org\/content\/page\/science-licenses-journal-article-reuse"}],"funder":[{"DOI":"10.13039\/100000005","name":"U.S. Department of Defense","doi-asserted-by":"publisher","award":["National Defense Science and Engineering Graduate Fellowship"],"award-info":[{"award-number":["National Defense Science and Engineering Graduate Fellowship"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Schmidt Sciences, LLC","award":["Polymath Award"],"award-info":[{"award-number":["Polymath Award"]}]},{"name":"NSF Physics of Living Systems student network"},{"name":"MIT MathWorks Professorship Fund"}],"content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Adv."],"published-print":{"date-parts":[[2026,3,20]]},"abstract":"<jats:p>\n                    Mosquito-borne diseases cause several hundred thousand deaths worldwide every year. Deciphering mosquito host-seeking behavior is essential to prevent disease transmission through mosquito capture and surveillance. Despite recent substantial progress, we still lack a comprehensive quantitative understanding of how visual and other sensory cues guide mosquitoes to their targets. Here, we combined three-dimensional infrared tracking of\n                    <jats:italic toggle=\"yes\">Aedes aegypti<\/jats:italic>\n                    mosquitoes with Bayesian dynamical systems inference to learn a quantitative biophysical model of mosquito host-seeking behavior. Trained on more than 20 million data points, each corresponding to an instantaneous position and velocity in mosquito free-flight trajectories recorded in the presence of visual and carbon dioxide cues, the model accurately predicts how mosquitoes respond to human targets. Our results provide a quantitative foundation for optimizing mosquito capture and control strategies, a key step toward mitigating the impact of mosquito-borne diseases.\n                  <\/jats:p>","DOI":"10.1126\/sciadv.adz7063","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:59:43Z","timestamp":1773856783000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":5,"title":["Predicting mosquito flight behavior using Bayesian dynamical systems learning"],"prefix":"10.1126","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3019-0868","authenticated-orcid":true,"given":"Christopher","family":"Zuo","sequence":"first","affiliation":[{"name":"Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8287-4347","authenticated-orcid":true,"given":"Chenyi","family":"Fei","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5284-6775","authenticated-orcid":true,"given":"Alexander E.","family":"Cohen","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1083-238X","authenticated-orcid":true,"given":"Soohwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6659-302X","authenticated-orcid":true,"given":"Ring T.","family":"Card\u00e9","sequence":"additional","affiliation":[{"name":"Department of Entomology, University of California, Riverside, CA 92521, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8865-2369","authenticated-orcid":true,"given":"J\u00f6rn","family":"Dunkel","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0017-7303","authenticated-orcid":true,"given":"David L.","family":"Hu","sequence":"additional","affiliation":[{"name":"Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA."},{"name":"School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmedr.2018.10.018"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1115000"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature13964"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13071-022-05333-4"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","unstructured":"A. Hinze S. Hill R. Ignell \u201cChapter 9: Odour-mediated host selection and discrimination in mosquitoes \u201d in Sensory Ecology of Disease Vectors (Wageningen Academic Publishers 2022) pp. 253\u2013276.","DOI":"10.3920\/978-90-8686-932-9_9"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2015.06.046"},{"key":"e_1_3_2_8_2","unstructured":"T. D. De R. Dixit \u201cNeuro-olfactory regulation and salivary actions: A coordinated event for successful blood-feeding behavior of mosquitoes \u201d in Sino-Nasal and Olfactory System Disorders (IntechOpen 2020) pp. 73\u201395."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.180246"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2019.06.001"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-ento-120709-144852"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-28195-x"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0007485300051038"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/252388b0"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10905-022-09796-2"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2915.2006.00614.x"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2013.12.044"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1242\/jeb.01736"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsbl.2022.0270"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2022.12.025"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0050464"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1093\/jme\/tjz243"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2915.2006.00613.x"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2020.1030"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.170189"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","unstructured":"R. de Jong B. 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Ronceray Learning dynamical models from stochastic trajectories. arXiv:2406.02363 (2024)."},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2016602118"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1517384113"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2017.0009"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2018.0534"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1002\/wics.199"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1088\/1752-7163\/ac2589"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1093\/jmedent\/44.4.617"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.108.248101"},{"key":"e_1_3_2_70_2","unstructured":"Mosquito Simulation Portal (2025); https:\/\/github.com\/f-chenyi\/MosquitoProject."},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13071-016-1724-x"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.adf8141"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1002\/ece3.71164"},{"key":"e_1_3_2_74_2","unstructured":"C. Zuo C. Fei A. Cohen S. Kim R. Carde J. Dunkel D. Hu Original data of \u201cPredicting mosquito flight behavior using Bayesian dynamical systems learning \u201d Zenodo (2025); https:\/\/zenodo.org\/records\/15277051."},{"key":"e_1_3_2_75_2","unstructured":"C. Zuo C. Fei A. E. Cohen S. Kim R. T. Carde Code for \u201cPredicting mosquito flight behavior using Bayesian dynamical systems learning \u201d Zenodo (2025); https:\/\/zenodo.org\/records\/17956325."},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07848-5"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-3032.1995.tb00005.x"},{"key":"e_1_3_2_78_2","unstructured":"PFMD Key Specifications (2020); https:\/\/photonicsentry.com\/uploads\/PFMD_Sell_Sheet.pdf."},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1242\/jeb.202.20.2727"},{"key":"e_1_3_2_80_2","unstructured":"A. Krogh J. Hertz \u201cA simple weight decay can improve generalization \u201d in Advances in Neural Information Processing Systems J. Moody S. Hanson R. Lippmann Eds. (Morgan-Kaufmann Burlington MA 1991) vol. 4 pp. 950\u2013957."},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.3.448"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.3.415"},{"key":"e_1_3_2_83_2","unstructured":"J. K. Ghosh M. Delampady T. Samanta An Introduction to Bayesian Analysis: Theory and Methods (Springer 2006) vol. 725."},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.5334\/jors.151"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1093\/jee\/toae209"}],"container-title":["Science Advances"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/sciadv.adz7063","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/sciadv.adz7063","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T18:00:00Z","timestamp":1773856800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/sciadv.adz7063"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,20]]},"references-count":84,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2026,3,20]]}},"alternative-id":["10.1126\/sciadv.adz7063"],"URL":"https:\/\/doi.org\/10.1126\/sciadv.adz7063","relation":{},"ISSN":["2375-2548"],"issn-type":[{"value":"2375-2548","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,20]]},"assertion":[{"value":"2025-06-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"eadz7063"}}