Приказ основних података о документу
Inverting convolutional neural networks for super-resolution identification of regime changes in epidemiological time series
dc.contributor | Morić, Ivana | |
dc.contributor | Đorđević, Valentina | |
dc.creator | M. G. Vilar, Jose | |
dc.date.accessioned | 2023-07-24T10:10:33Z | |
dc.date.available | 2023-07-24T10:10:33Z | |
dc.date.issued | 2023 | |
dc.identifier.isbn | 978-86-82679-14-1 | |
dc.identifier.uri | https://belbi.bg.ac.rs/ | |
dc.identifier.uri | https://imagine.imgge.bg.ac.rs/handle/123456789/1948 | |
dc.description.abstract | Inferring the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is as relevant as challenging. It is a requirement for current approaches to overcome the need to know the details of the perturbations to proceed with the analyses. However, the general problem of connecting epidemiological curves with the underlying incidence lacks the highly effective methodology present in other inverse problems, such as super-resolution and dehazing from machine vision. I will present an unsupervised physics-informed convolutional neural network approach in reverse to connect death records with an incidence that allows the identification of regime changes at a single-day resolution. Applied to COVID-19 data with proper regularization and model-selection criteria, the approach can identify the implementation and removal of lockdowns and other nonpharmaceutical interventions with ± 0.9-day accuracy over the span of a year. | sr |
dc.language.iso | en | sr |
dc.publisher | Belgrade : Institute of molecular genetics and genetic engineering | sr |
dc.relation | J.M.G.V. acknowledges support from Ministerio de Ciencia e Innovacion under grants PGC2018-101282-B-I00 and PID2021-128850NB-I00 (MCI/ AEI/FEDER, UE). | sr |
dc.rights | openAccess | sr |
dc.source | 4th Belgrade Bioinformatics Conference | sr |
dc.subject | bioinformatics | sr |
dc.subject | physics-informed neural networks | sr |
dc.subject | epidemiology | sr |
dc.title | Inverting convolutional neural networks for super-resolution identification of regime changes in epidemiological time series | sr |
dc.type | conferenceObject | sr |
dc.rights.license | ARR | sr |
dc.rights.holder | © 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade | sr |
dc.citation.epage | 13 | |
dc.citation.spage | 13 | |
dc.citation.volume | 4 | |
dc.description.other | Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023 | sr |
dc.identifier.fulltext | https://imagine.imgge.bg.ac.rs/bitstream/id/298028/BELBI-Abstracts-final-07072023_1-15,29,129.pdf | |
dc.identifier.rcub | https://hdl.handle.net/21.15107/rcub_imagine_1948 | |
dc.type.version | publishedVersion | sr |
Документи
Овај документ се појављује у следећим колекцијама
-
Belgrade Bioinformatics Conference
BelBi conferences