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  • 1. Hoffmann, Till
    et al.
    Alsing, Justin
    Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum. Stockholms universitet, Naturvetenskapliga fakulteten, Oskar Klein-centrum för kosmopartikelfysik (OKC).
    Faecal shedding models for SARS-CoV-2 RNA among hospitalised patients and implications for wastewater-based epidemiology 2023Ingår i: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876, Vol. 72, nr 2, s. 330-345Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The concentration of SARS-CoV-2 RNA in faeces is not well characterised, posing challenges for quantitative wastewater-based epidemiology (WBE). We developed hierarchical models for faecal RNA shedding and fitted them to data from six studies. A mean concentration of 1.9 × 106 mL-1 (2.3 × 105–2.0 × 108 95% credible interval) was found among unvaccinated inpatients, not considering differences in shedding between viral variants. Limits of quantification could account for negative samples based on Bayesian model comparison. Inpatients represented the tail of the shedding profile with a half-life of 34 hours (28–43 95% credible interval), suggesting that WBE can be a leading indicator for clinical presentation. Shedding among inpatients could not explain the high RNA concentrations found in wastewater, consistent with more abundant shedding during the early infection course. 

  • 2.
    Karlsson, Måns
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Hössjer, Ola
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Identification of taxon through classification with partial reject options2023Ingår i: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876, Vol. 72, nr 4, s. 937-975Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Identification of taxa can significantly be assisted by statistical classification based on trait measurements either individually or by phylogenetic (clustering) methods. In this article, we present a general Bayesian approach for classifying species individually based on measurements of a mixture of continuous and ordinal traits, and any type of covariates. The trait vector is derived from a latent variable with a multivariate Gaussian distribution. Decision rules based on supervised learning are presented that estimate model parameters through blocked Gibbs sampling. These decision regions allow for uncertainty (partial rejection), so that not necessarily one specific category (taxon) is output when new subjects are classified, but rather a set of categories including the most probable taxa. This type of discriminant analysis employs reward functions with a set-valued input argument, so that an optimal Bayes classifier can be defined. We also present a way of safeguarding against outlying new observations, using an analogue of a p-value within our Bayesian setting. We refer to our Bayesian set-valued classifier as the Karlsson–Hössjer method, and it is illustrated on an original ornithological data set of birds. We also incorporate model selection through cross-validation, exemplified on another original data set of birds. 

  • 3. Thorvaldsen, Steinar
    et al.
    Hössjer, Ola
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Estimating the information content of genetic sequence data2023Ingår i: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876, Vol. 72, nr 5, s. 1310-1338Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A prominent problem in analysing genetic information has been a lack of mathematical frameworks for doing so. This article offers some new statistical methods to model and analyse information content in proteins, protein families, and their sequences. We discuss how to understand the qualitative aspects of genetic information, how to estimate the quantitative aspects of it, and implement a statistical model where the qualitative genetic function is represented jointly with its probabilistic metric of self-information. The functional information of protein families in the Cath and Pfam databases are estimated using a method inspired by rejection sampling. Scientific work may place these components of information as one of the fundamental aspects of molecular biology.

  • 4. Wegmann, Bertil
    et al.
    Lundquist, Anders
    Eklund, Anders
    Villani, Mattias
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.
    Bayesian modelling of effective and functional brain connectivity using hierarchical vector autoregressions2024Ingår i: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression hierarchical model for analysing brain connectivity within resting-state functional magnetic resonance imaging, and apply it to simulated data and a real data set with subjects in different groups. Our approach models functional and effective connectivity simultaneously and allows for both group- and single-subject inference. We combine analytical marginalization with Hamiltonian Monte Carlo to obtain highly efficient posterior sampling. We show that our model gives similar inference for effective connectivity compared to models with a common covariance matrix to all subjects, but more accurate inference for functional connectivity between regions compared to models with more restrictive covariance structures. A Stan implementation of our model is available on GitHub.

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