Isaac H Goldstein, Jon Wakefield, Volodymyr M Minin
{"title":"Incorporating testing volume into estimation of effective reproduction number dynamics","authors":"Isaac H Goldstein, Jon Wakefield, Volodymyr M Minin","doi":"10.1093/jrsssa/qnad128","DOIUrl":null,"url":null,"abstract":"<jats:title>Abstract</jats:title> Branching process inspired models are widely used to estimate the effective reproduction number—a useful summary statistic describing an infectious disease outbreak—using counts of new cases. Case data is a real-time indicator of changes in the reproduction number, but is challenging to work with because cases fluctuate due to factors unrelated to the number of new infections. We develop a new model that incorporates the number of diagnostic tests as a surveillance model covariate. Using simulated data and data from the SARS-CoV-2 pandemic in California, we demonstrate that incorporating tests leads to improved performance over the state of the art.","PeriodicalId":517419,"journal":{"name":"The Journal of the Royal Statistical Society, Series A (Statistics in Society)","volume":"29 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of the Royal Statistical Society, Series A (Statistics in Society)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jrsssa/qnad128","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract Branching process inspired models are widely used to estimate the effective reproduction number—a useful summary statistic describing an infectious disease outbreak—using counts of new cases. Case data is a real-time indicator of changes in the reproduction number, but is challenging to work with because cases fluctuate due to factors unrelated to the number of new infections. We develop a new model that incorporates the number of diagnostic tests as a surveillance model covariate. Using simulated data and data from the SARS-CoV-2 pandemic in California, we demonstrate that incorporating tests leads to improved performance over the state of the art.
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