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dc.contributor.authorVanella, Patrizio
dc.contributor.authorBasellini, Ugofilippo
dc.contributor.authorLange, Berit
dc.date.accessioned2021-08-25T09:04:10Z
dc.date.available2021-08-25T09:04:10Z
dc.date.issued2021-08-09
dc.identifier.citationGenus. 2021;77(1):16. doi: 10.1186/s41118-021-00123-9. Epub 2021 Aug 9.en_US
dc.identifier.issn0016-6987
dc.identifier.pmid34393261
dc.identifier.doi10.1186/s41118-021-00123-9
dc.identifier.urihttp://hdl.handle.net/10033/622999
dc.description.abstractThe COVID-19 outbreak has called for renewed attention to the need for sound statistical analyses to monitor mortality patterns and trends over time. Excess mortality has been suggested as the most appropriate indicator to measure the overall burden of the pandemic in terms of mortality. As such, excess mortality has received considerable interest since the outbreak of COVID-19 began. Previous approaches to estimate excess mortality are somewhat limited, as they do not include sufficiently long-term trends, correlations among different demographic and geographic groups, or autocorrelations in the mortality time series. This might lead to biased estimates of excess mortality, as random mortality fluctuations may be misinterpreted as excess mortality. We propose a novel approach that overcomes the named limitations and draws a more realistic picture of excess mortality. Our approach is based on an established forecasting model that is used in demography, namely, the Lee-Carter model. We illustrate our approach by using the weekly age- and sex-specific mortality data for 19 countries and the current COVID-19 pandemic as a case study. Our findings show evidence of considerable excess mortality during 2020 in Europe, which affects different countries, age, and sex groups heterogeneously. Our proposed model can be applied to future pandemics as well as to monitor excess mortality from specific causes of death.en_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCOVID-19 pandemicen_US
dc.subjectCross-country mortality trendsen_US
dc.subjectDemographyen_US
dc.subjectEpidemiologyen_US
dc.subjectExcess mortality assessmenten_US
dc.subjectMonte Carlo simulationen_US
dc.subjectMortality forecastingen_US
dc.subjectPrincipal component analysisen_US
dc.subjectStochasticityen_US
dc.subjectTime series analysisen_US
dc.titleAssessing excess mortality in times of pandemics based on principal component analysis of weekly mortality data-the case of COVID-19.en_US
dc.typeArticleen_US
dc.contributor.departmentHZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany.en_US
dc.identifier.journalGenusen_US
dc.source.volume77
dc.source.issue1
dc.source.beginpage16
dc.source.endpage
refterms.dateFOA2021-08-25T09:04:11Z
dc.source.journaltitleGenus
dc.source.countryGermany


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International