Počet záznamů: 1
Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19
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SYSNO ASEP 0558938 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19 Tvůrce(i) Suchopárová, Gabriela (UIVT-O) RID, ORCID, SAI
Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
Neruda, Roman (UIVT-O) SAI, RID, ORCID
Šmíd, Martin (UTIA-B) RID, ORCIDCelkový počet autorů 4 Zdroj.dok. Engineering Applications of Neural Networks. - Cham : Springer, 2022 / Iliadis L. ; Jayne Ch. ; Tefas A. ; Pimenidis E. - ISSN 1865-0929 - ISBN 978-3-031-08222-1 Rozsah stran s. 310-320 Poč.str. 11 s. Forma vydání Tištěná - P Akce EANN 2022: International Conference on Engineering Applications of Neural Networks /23./ Datum konání 17.06.2022 - 20.06.2022 Místo konání Chersonissos / Virtual Země GR - Řecko Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CH - Švýcarsko Klíč. slova Deep learning ; Risk model ; Immunity waning Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Institucionální podpora UIVT-O - RVO:67985807 ; UTIA-B - RVO:67985556 UT WOS 000926169100026 EID SCOPUS 85133002397 DOI 10.1007/978-3-031-08223-8_26 Anotace The proportional hazard Cox model is traditionally used in survival analysis to estimate the effect of several variables on the hazard rate of an event. Recently, neural networks were proposed to improve the flexibility of the Cox model. In this work, we focus on an extension of the Cox model, namely on a non-proportional relative risk model, where the neural network approximates a non-linear time-dependent risk function. We address the issue of the lack of time-varying variables in this model, and to this end, we design a deep neural network model capable of time-varying regression. The target application of our model is the waning of post-vaccination and post-infection immunity in COVID-19. This task setting is challenging due to the presence of multiple time-varying variables and different epidemic intensities at infection times. The advantage of our model is that it enables a fine-grained analysis of risks depending on the time since vaccination and/or infection, all approximated using a single non-linear function. A case study on a data set containing all COVID-19 cases in the Czech Republic until the end of 2021 has been performed. The vaccine effectiveness for different age groups, vaccine types, and the number of doses received was estimated using our model as a function of time. The results are in accordance with previous findings while allowing greater flexibility in the analysis due to a continuous representation of the waning function. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2023 Elektronická adresa https://dx.doi.org/10.1007/978-3-031-08223-8_26
Počet záznamů: 1