{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T23:49:43Z","timestamp":1787701783620,"version":"build-2784847793"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science, Technology and Innovation of Argentina","award":["PICT2021-I130"],"award-info":[{"award-number":["PICT2021-I130"]}]},{"name":"Ministry of Science, Technology and Innovation of Argentina","award":["PICT2021-I130"],"award-info":[{"award-number":["PICT2021-I130"]}]},{"name":"General Secretariat for Science and Technology, Universidad Nacional del Nordeste","award":["CORR 01 COVID FEDERAL EX-2020-38902538"],"award-info":[{"award-number":["CORR 01 COVID FEDERAL EX-2020-38902538"]}]},{"name":"General Secretariat for Science and Technology, Universidad Nacional del Nordeste","award":["CORR 01 COVID FEDERAL EX-2020-38902538"],"award-info":[{"award-number":["CORR 01 COVID FEDERAL EX-2020-38902538"]}]},{"name":"General Secretariat for Science and Technology, Universidad Nacional del Nordeste","award":["CORR 01 COVID FEDERAL EX-2020-38902538"],"award-info":[{"award-number":["CORR 01 COVID FEDERAL EX-2020-38902538"]}]},{"DOI":"10.13039\/501100002923","name":"National Scientific and Technical Research Council","doi-asserted-by":"crossref","award":["PICT2020-SERIEA-I-A"],"award-info":[{"award-number":["PICT2020-SERIEA-I-A"]}],"id":[{"id":"10.13039\/501100002923","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The COVID-19 pandemic, with its multiple outbreaks, has posed significant challenges for governments worldwide. Much of the epidemiological modeling relied on pre-pandemic contact information of the population to model the virus transmission between population age groups. However, said interactions underwent drastic changes due to governmental health measures, referred to as non-pharmaceutical interventions. These interventions, from social distancing to complete lockdowns, aimed to reduce transmission of the virus. This work proposes taking into account the impact of non-pharmaceutical measures upon social interactions among different age groups by estimating the time dependence of these interactions in real time based on epidemiological data. This is achieved by using a time-dependent transmission matrix of the disease between different population age groups. This transmission matrix is estimated using an ensemble-based data assimilation system applied to a meta-population model and time series data of age-dependent accumulated cases and deaths. We conducted a set of idealized twin experiments to explore the performance of different ways in which social interactions can be parametrized through the transmission matrix of the meta-population model. These experiments show that, in an age-compartmental model, all the independent parameters of the transmission matrix cannot be unequivocally estimated, i.e., they are not all identifiable. Nevertheless, the time-dependent transmission matrix can be estimated under certain parameterizations. These estimated parameters lead to an increase in forecast accuracy within age-group compartments compared to a single-compartmental model assimilating observations of age-dependent accumulated cases and deaths in Argentina. Furthermore, they give reliable estimations of the effective reproduction number. The age-dependent data assimilation and forecasting of virus transmission are crucial for an accurate prediction and diagnosis of healthcare demand.<\/jats:p>","DOI":"10.1371\/journal.pone.0318426","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T17:46:51Z","timestamp":1745862411000},"page":"e0318426","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Transmission matrix parameter estimation of COVID-19 evolution with age compartments using ensemble-based data assimilation"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5414-3672","authenticated-orcid":true,"given":"Santiago","family":"Rosa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel A.","family":"Pulido","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5079-641X","authenticated-orcid":true,"given":"Juan J.","family":"Ruiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tadeo J.","family":"Cocucci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"pone.0318426.ref001","unstructured":"Ferguson NM, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, et al. 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