2026-06-24T03:22:19Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/148672025-05-12T12:14:55Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19609

00925njm 22002777a 4500 dc Penedos, Ana Raquel author Fernandez-Garcia, Aurora author Lazar, Mihaela author Ralh, Kajal author Williams, David author Brown, Kevin E author 2022-05 Background: Assessing relatedness of pathogen sequences in clinical samples is a core goal in molecular epidemiology. Tools for Bayesian analysis of phylogeny, such as the BEAST software package, have been typically used in the analysis of sequence/time data in public health. However, they are computationally-, time-, and knowledge-intensive, demanding resources that many laboratories do not have available or cannot allocate frequently. Methods: To evaluate a faster and simpler alternative method to support the routine interpretation of sequence data for epidemiology, we obtained sequences for two regions in the measles virus genome, N-450 and MF-NCR, from patient samples of genotypes B3, D4 and D8 taken between 2011 and 2017 in the UK and Romania. A mathematical model incorporating time, possible shared ancestry and the Poisson distribution describing the number of expected substitutions at a given time point was developed to exclude epidemiological relatedness between pairs of sequences. The model was validated against the commonly used Bayesian phylogenetic method using an independent dataset collected in 2017–19. Findings: We demonstrate that our model, using time and sequence information to predict whether two samples may be related within a given time frame, minimises the risk of erroneous exclusion of relatedness. An easy-to-use implementation in the form of a guide and spreadsheet is provided for convenient application. Interpretation: The proposed model only requires a previously calculated substitution rate for the locus and pathogen of interest. It allows for an informed but quick decision on the likelihood of relatedness between two samples within a time frame, without the need for phylogenetic reconstruction, thus facilitating rapid epidemiological interpretation of sequence data. EBioMedicine. 2022 May;79:103989. 10.1016/j.ebiom.2022.103989 2352-3964 EBioMedicine 35398788 http://hdl.handle.net/20.500.12105/14867 Measles Outbreak Elimination Epidemiology Molecular epidemiology Clinical virology Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data