Estimation of potential global pandemic influenza mortality on the basis of vital registry data from the 1918-20 pandemic: a quantitative analysis.

By Christopher J. L. Murray, Alan D. Lopez, B. Chin, D. Feehan, K. Hill. The Lancet, 2006

606 citations recorded by the source corpus.

Subfields: Epidemiology, Modeling and Simulation, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/fc6f079652082d396b1a71d6edb0672db4eb1579

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

1. James H. Jones
2. Marcel Salathé

In Modeling and Simulation

1. Marcel Salathé
2. James H. Jones

In Public Health, Environmental and Occupational Health

1. P. Levis
2. Maria A. Kazandjieva
3. James H. Jones
4. Jung Woo Lee
5. Michal Feldman
6. Marcel Salathé

Citations in the corpus

• Scaling regression inputs by dividing by two standard deviations (2008, background): “• Table 2 of Murray et al. (2006), which reports linear regression coefficients for log income and latitude; the latter has a wide range in the dataset and so unsurprisingly has a coefficient estimate that is very small on the absolute scale.”
• Cross-Species Virus Transmission and the Emergence of New Epidemic Diseases (2008, neutral): “while retaining high human pathogenicity (25, 83).”
• The COVID-19 pandemic and health inequalities (2020, background): “For example, India had a mortality rate 40 times higher than Denmark and the mortality rate was 20 times higher in some South American countries than in Europe.4 In Norway, mortality rates were highest among the working-class districts of Oslo5; in the USA, they were highest among the unemployed and”
• A high-resolution human contact network for infectious disease transmission (2010, background): “P spread of an infectious disease is one of the biggest threats to society because of the potentially high mortality and high economic costs associated with such an event (1, 2).”
• Nonpharmaceutical interventions implemented by US cities during the 1918-1919 influenza pandemic. (2007, methodology): “We also generated scatterplots and Spearman rank correlation coefficients to explore other potential or confounding associations (as independent determinants): (1) EDR in the 4 successive waves of the pandemic; (2) city-specific population size vs EDR; (3) cityspecific population density vs EDR; (4)”