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Global Mortality Estimates for the 2009 Influenza Pandemic from the GLaMOR Project: A Modeling Study

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TLDR
A two-stage statistical modeling approach is used to estimate the global mortality burden of the 2009 influenza pandemic from mortality data obtained from multiple countries.
Abstract
Background Assessing the mortality impact of the 2009 influenza A H1N1 virus (H1N1pdm09) is essential for optimizing public health responses to future pandemics. The World Health Organization reported 18,631 laboratory-confirmed pandemic deaths, but the total pandemic mortality burden was substantially higher. We estimated the 2009 pandemic mortality burden through statistical modeling of mortality data from multiple countries.

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Citations
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Journal ArticleDOI

Estimates of global seasonal influenza-associated respiratory mortality: a modelling study

A. Danielle Iuliano, +138 more
- 13 Dec 2017 - 
TL;DR: These global influenza-associated respiratory mortality estimates are higher than previously reported, suggesting that previous estimates might have underestimated disease burden.
Journal ArticleDOI

Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics.

TL;DR: Compared with other epidemic coronaviruses, SARS-CoV-2 causes mild or asymptomatic disease in most cases; however, severe to critical illness occurs in a small proportion of infected individuals, with the highest rate seen in people older than 70 years.
Journal ArticleDOI

Influenza and other respiratory viruses involved in severe acute respiratory disease in northern Italy during the pandemic and postpandemic period (2009-2011).

TL;DR: The proportion of SARI/ARDS cases and deaths due to influenza A(H1N1)pdm09 infection and the impact of other respiratory viruses during pandemic and postpandemic period (2009–2011) in northern Italy is evaluated and a virus discovery technique (VIDISCA-454) enabled the identification of one previously undiagnosed measles infection.
Journal ArticleDOI

Pandemic Preparedness and Response — Lessons from the H1N1 Influenza of 2009

TL;DR: The H1N1 influenza pandemic exposed strengths and weaknesses of the global plan in place to deal with emerging infectious disease threats, and the response of international agencies and governments was reviewed and critiques.
Journal ArticleDOI

Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study

TL;DR: Efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks are described and delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally.
References
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Book

Hierarchical Linear Models: Applications and Data Analysis Methods

TL;DR: The Logic of Hierarchical Linear Models (LMLM) as discussed by the authors is a general framework for estimating and hypothesis testing for hierarchical linear models, and it has been used in many applications.
Journal ArticleDOI

Hierarchical Linear Models: Applications and Data Analysis Methods.

TL;DR: This chapter discusses Hierarchical Linear Models in Applications, Applications in Organizational Research, and Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known.
Journal ArticleDOI

Mortality Associated With Influenza and Respiratory Syncytial Virus in the United States

TL;DR: Mortality associated with both influenza and RSV circulation disproportionately affects elderly persons, and influenza deaths have increased substantially in the last 2 decades, in part because of aging of the population, highlighting the need for better prevention measures, including more effective vaccines and vaccination programs for elderly persons.
Journal ArticleDOI

Amelia II: A Program for Missing Data

TL;DR: The Amelia II package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers.
Book

Flexible Imputation of Missing Data

TL;DR: The problem of missing data concepts of MCAR, MAR and MNAR simple solutions that do not (always) work multiple imputation in a nutshell and some dangers, some do's and some don'ts are covered.
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