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dc.contributor.authorSingh, David E
dc.contributor.authorMarinescu, Maria-Cristina
dc.contributor.authorCarretero, Jesus
dc.contributor.authorDelgado-Sanz, Concepcion 
dc.contributor.authorGomez-Barroso, Diana 
dc.contributor.authorLarrauri, Amparo
dc.identifier.citationBMC Infect Dis. 2020 Apr 5;20(1):265.es_ES
dc.description.abstractBACKGROUND: Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph's modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET). METHODS: Our meteorological model is based on the regression model developed by AB and JS, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region. RESULTS: We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10% produces an increment of about 1.6% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1% per extra degree. CONCLUSIONS: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.es_ES
dc.description.sponsorshipThis work has been partially supported by the Spanish “Ministerio de Economía y Competitividad” under the project grant TIN2016-79637-P “Towards Unification of HPC and Big Data paradigms”. The work of Maria-Cristina Marinescu has been partially supported by the H2020 European project GrowSmarter under project grant ref. 646456. The role of both funders was limited to financial support and did not imply participation of any kind in the study and collection, analysis, and interpretation of data, nor in the writing of the manuscript.es_ES
dc.publisherBioMed Central (BMC) es_ES
dc.subjectInfluenza epidemices_ES
dc.subjectMeteorological modeles_ES
dc.titleEvaluating the impact of the weather conditions on the influenza propagationes_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.contributor.funderMinisterio de Economía y Competitividad (España) 
dc.contributor.funderUnión Europea. Comisión Europea. H2020 
dc.identifier.journalBMC infectious diseaseses_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemiologíaes_ES
dc.rights.accessRightsopen accesses_ES

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Atribución 4.0 Internacional
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