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http://dx.doi.org/10.3201/eid2107.150286 | DOI Listing |
bioRxiv
December 2024
Institute of Ecology and Evolution, University of Edinburgh, Edinburgh, UK.
In Bayesian phylogenetic and phylodynamic studies it is common to summarise the posterior distribution of trees with a time-calibrated consensus phylogeny. While the maximum clade credibility (MCC) tree is often used for this purpose, we here show that a novel consensus tree method - the highest independent posterior subtree reconstruction, or HIPSTR - contains consistently higher supported clades over MCC. We also provide faster computational routines for estimating both consensus trees in an updated version of TreeAnnotator X, an open-source software program that summarizes the information from a sample of trees and returns many helpful statistics such as individual clade credibilities contained in the consensus tree.
View Article and Find Full Text PDFBMC Glob Public Health
April 2024
College of Health Sciences, University of Liberia, Monrovia, Liberia.
Background: The burden of the COVID-19 pandemic in terms of morbidity and mortality differentially affected populations. Between and within populations, behavior change was likewise heterogeneous. Factors influencing precautionary behavior adoption during COVID-19 have been associated with multidimensional aspects of risk perception; however, the influence of lived experiences during other recent outbreaks on behavior change during COVID-19 has been less studied.
View Article and Find Full Text PDFEmerg Infect Dis
December 2024
PLoS Comput Biol
November 2024
Department of Pathobiology and Population Sciences, Royal Veterinary College, United Kingdom.
Accurately estimating the effective reproduction number (Rt) of a circulating pathogen is a fundamental challenge in the study of infectious disease. The fields of epidemiology and pathogen phylodynamics both share this goal, but to date, methodologies and data employed by each remain largely distinct. Here we present EpiFusion: a joint approach that can be used to harness the complementary strengths of each field to improve estimation of outbreak dynamics for large and poorly sampled epidemics, such as arboviral or respiratory virus outbreaks, and validate it for retrospective analysis.
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