Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden

Journal article

Most environments harbor large numbers of microbial taxa with ecologies that remain poorly described and characterizing the functional capabilities of whole communities remains a key challenge in microbial ecology. Shotgun metagenomic analyses are increasingly recognized as a powerful tool to understand community-level attributes. However, much of this data is under-utilized due, in part, to a lack of conceptual strategies for linking the metagenomic data to the most relevant community-level characteristics. Microbial ecologists could benefit by borrowing the concept of community-aggregated traits (CATs) from plant ecologists to glean more insight from the ever-increasing amount of metagenomic data being generated. CATs can be used to quantify the mean and variance of functional traits found in a given community. A CAT-based strategy will often yield far more useful information for predicting the functional attributes of diverse microbial communities and changes in those attributes than the more commonly used analytical strategies. A more careful consideration of what CATs to measure and how they can be quantified from metagenomic data, will help build a more integrated understanding of complex microbial communities

Authors: Bergström, F; Günther, F; Höhle, M; Britton, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS Computational Biology
• Volume: 18
• Issue: 12
• Pages: e1010767-e1010767
• Publication date: 2022-12-07
• DOI: 10.1371/journal.pcbi.1010767
• EISSN: 1553-7358
• ISSN: 1553-734X
• Language: English
• Keywords: Econometrics; Coronavirus disease 2019 (COVID-19); Geography; Nowcasting; Machine learning; Computer science; Meteorology; Infectious disease (medical specialty); Medical emergency; Medicine; Public health; Bayesian probability; Mathematics; Public health surveillance; Time series; Statistics; Disease; Artificial intelligence; Pandemic