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Estimating geographic variation of infection fatality ratios during epidemics

Infectious Disease Modelling 2024年第2期9卷 634-643页

作者： Joshua Ladau Eoin L.Brodie Nicola Falco Ishan Bansal Elijah B.Hoffman Marcin P.Joachimiak Ana M.Mora Angelica M.Walker Haruko M.Wainwright Yulun Wu Mirko Pavicic Daniel Jacobson matthias hess James B.Brown Katrina Abuabara Departments of Computational Precision Health and Dermatology University of CaliforniaSan FranciscoCA94115USA Arva Intelligence Inc.Salt Lake CityUT84101USA Computational Biosciences Group Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Earth and Environmental Sciences Area Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Graduate Group in Biostatistics University of CaliforniaBerkeleyCA94720USA Biosystems Data Science Environmental Genomics and Systems BiologyLawrence Berkeley National LaboratoryBerkeleyCA94720USA Center for Environmental Research and Community Health(CERCH) School of Public HealthUniversity of CaliforniaBerkeleyCA94720USA Bredesen Center for Interdisciplinary Research and Graduate Education University of TennesseeKnoxvilleTN37996USA Department of Nuclear Science and Engineering Massachusetts Institute of TechnologyBostonMA02139USA Biosciences Oak Ridge National LaboratoryOak RidgeTN37830USA University of California DavisCA95616USA Statistics Department University of CaliforniaBerkeleyCA94720USA Division of Epidemiology and Biostatistics University of California Berkeley School of Public Health2121 Berkeley WayBerkeleyCA94720USA

Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an *** We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek *** demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 *** The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model *** the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of *** The proposed estimation framework can be used to identify geographic variation in IFRs across settings.

关键词： Infection fatality ratio Infection fatality rate Noncentral hypergeometric distribution COVID-19 SARS-CoV-2

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Improved Method for Isolation of Microbial RNA from Biofuel Feedstock for Metatranscriptomics

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Advances in Microbiology 2013年第1期3卷 101-107页

作者： Hailan Piao Lye Meng Markillie David E. Culley Roderick I. Mackie matthias hess Department of Animal Sciences Institute for Genomic Biology and Energy Biosciences University of Illinois Urbana USA DOE Joint Genome Institute Walnut Creek USA Pacific Northwest National Laboratory Richland USA School of Molecular Biosciences Washington State University Richland USA

Metatranscriptomics—gene express profiling via DNA sequencing—is a powerful tool to identify genes that are actively expressed and might contribute to the phenotype of individual organisms or the phenome (the sum of several phenotypes) of a microbial community. Furthermore, metatranscriptome studies can result in extensive catalogues of genes that encode for enzymes of industrial relevance. In both cases, a major challenge for generating a high quality metatranscriptome is the extreme lability of RNA and its susceptibility to ubiquitous RNAses. The microbial community (the microbiome) of the cow rumen efficiently degrades lignocelullosic biomass, generates significant amounts of methane, a greenhouse gas twenty times more potent than carbon dioxide, and is of general importance for the physiological wellbeing of the host animal. Metatranscriptomes of the rumen microbiome from animals kept under different conditions and from various types of rumen-incubated biomass can be expected to provide new insights into these highly interesting phenotypes and subsequently provide the framework for an enhanced understanding of this socioeconomically important ecosystem. The ability to isolate large amounts of intact RNA will significantly facilitate accurate transcript annotation and expression profiling. Here we report a method that combines mechanical disruption with chemical homogenization of the sample material and consistently yields 1 mg of intact RNA from 1 g of rumen-incubated biofuel feedstock. The yield of total RNA obtained with our method exceeds the RNA yield achieved with previously reported isolation techniques, which renders RNA isolated with the method presented here as an ideal starting material for metatranscriptomic analyses and other molecular biology applications that require significant amounts of starting material.

关键词： RNA Extraction Biofuels CAZymes Metatranscriptomics Systems Microbiology Microbial Ecology

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