The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the U.S. reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real time represent a nonstationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes nonparametric modeling of varying transmission rates, nonparametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the U.S. Centers for Disease Control through the COVID-19 Forecast Hub under the name MechBayes. We examine the performance relative to a baseline model as well as alternate models submitted to the forecast hub. Additionally, we include an ablation test of our extensions to the classic SEIR model. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes, when compared to a baseline model, and show that MechBayes ranks as one of the top two models out of nine which regularly submitted to the COVID-19 Forecast Hub for the duration of the pandemic, trailing only the COVID-19 Forecast Hub ensemble model of which which MechBayes is a part.
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http://dx.doi.org/10.1214/22-aoas1671 | DOI Listing |
Front Genet
December 2024
Department of Geriatrics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Background: Sarcopenia is a prevalent condition associated with aging. Inflammation and pyroptosis significantly contribute to sarcopenia.
Methods: Two sarcopenia-related datasets (GSE111016 and GSE167186) were obtained from the Gene Expression Omnibus (GEO), followed by batch effect removal post-merger.
Exp Ther Med
February 2025
Department of Emergency, Xianning Central Hospital, The First Affiliated Hospital of Hubei University of Science and Technology, Xianning, Hubei 437199, P.R. China.
Previous research has highlighted the critical role of amino acid metabolism (AAM) in the pathophysiology of sepsis. The present study aimed to explore the potential diagnostic and prognostic value of AAM-related genes (AAMGs) in sepsis, as well as their underlying molecular mechanisms. Gene expression profiles from the Gene Expression Omnibus (GSE65682, GSE185263 and GSE154918 datasets) were analyzed.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Myocardial infarction (MI), one of the most serious cardiovascular diseases, is also affected by altered mitochondrial metabolism and immune status, but their crosstalk is poorly understood. In this paper, we use bioinformatics to explore key targets associated with mitochondrial metabolic function in MI.
Methods: The datasets (GSE775, GSE183272 and GSE236374) were from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) in conjunction with mitochondrial gene data that were downloaded from the MitoCarta 3.
Plants (Basel)
December 2024
College of Grassland Science, Qingdao Agricultural University, Qingdao 266109, China.
The gene family plays a crucial role in plant growth, development, and responses to biotic and abiotic stresses. , a warm-season turfgrass with exceptional salt tolerance, can be irrigated with seawater. However, the gene family in seashore paspalum remains poorly understood.
View Article and Find Full Text PDFLancet Reg Health Am
December 2024
Latin American Centre of Excellence for Climate Change and Health, Universidad Peruana Cayetano Heredia, San Martín de Porres, 15102, Peru.
This article delves into the complex relationship between climate change, migration patterns, and health outcomes in Latin America and the Caribbean (LAC). While the severe impact of climate change on health in LAC is widely acknowledged, the article sheds light on the often-overlooked multiple effects on migration and the well-being of migrants. These impacts encompass poverty, food and water insecurity, and adverse physical and mental health outcomes.
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