A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

DC FieldValueLanguage
dc.contributor.authorStojanovic, Olivera
dc.contributor.authorLeugering, Johannes
dc.contributor.authorPipa, Gordon
dc.contributor.authorGhozzi, Stephane
dc.contributor.authorUllrich, Alexander
dc.date.accessioned2021-12-23T16:08:35Z-
dc.date.available2021-12-23T16:08:35Z-
dc.date.issued2019
dc.identifier.issn19326203
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/8360-
dc.description.abstractIn this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-art hhh4 model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology.
dc.language.isoen
dc.publisherPUBLIC LIBRARY SCIENCE
dc.relation.ispartofPLOS ONE
dc.subjectCRITERIA
dc.subjectDISTRIBUTIONS
dc.subjectMODEL
dc.subjectMultidisciplinary Sciences
dc.subjectScience & Technology - Other Topics
dc.titleA Bayesian Monte Carlo approach for predicting the spread of infectious diseases
dc.typejournal article
dc.identifier.doi10.1371/journal.pone.0225838
dc.identifier.isiISI:000534242500023
dc.description.volume14
dc.description.issue12
dc.contributor.orcid0000-0002-3911-9573
dc.contributor.orcid0000-0001-9820-3479
dc.contributor.orcid0000-0002-3416-2652
dc.contributor.orcid0000-0002-4894-6124
dc.contributor.orcid0000-0003-0956-4139
dc.contributor.researcheridM-1813-2014
dc.publisher.place1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
dcterms.isPartOf.abbreviationPLoS One
dcterms.oaStatusGreen Published, gold, Green Submitted
crisitem.author.deptInstitut für Kognitionswissenschaft-
crisitem.author.deptidinstitute28-
crisitem.author.orcid0000-0002-3416-2652-
crisitem.author.parentorgFB 08 - Humanwissenschaften-
crisitem.author.grandparentorgUniversität Osnabrück-
crisitem.author.netidPiGo340-
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