Estimating the relative probability of direct transmission between infectious disease patients.
Background: Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimat...
| Publicado en: | International Journal of Epidemiology Vol. 49; no. 3; pp. 764 - 776 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144891968&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144891968 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03005771 DIH jtl: International Journal of Epidemiology issn: 03005771 maglogo: N pubinfo: dt: Jun2020 vid: 49 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 144891968 144891968 NLM32211747 144891968 10.1093/ije/dyaa031 NLM32211747 144891968 ppf: 764 ppct: 12 formats: tig: atl: Estimating the relative probability of direct transmission between infectious disease patients. aug: au: Leavitt, Sarah V Lee, Robyn S Sebastiani, Paola Horsburgh, C Robert Jenkins, Helen E White, Laura F affil: School of Public Health , Department of Biostatistics, Boston University, Boston, MA, USA sug: subj: Disease Outbreaks Disease Transmission Probability Adult Female Human Aged Middle Age Young Adult Male Germany Comparative Studies Multicenter Studies Evaluation Research Validation Studies Funding Source Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Background: Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases.Methods: Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates.Results: In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany.Conclusions: Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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