Containing pandemic influenza with antiviral agents.

By I. Longini, M. Halloran, A. Nizam, Yang Yang. American Journal of Epidemiology, 2004

763 citations recorded by the source corpus.

Subfields: Epidemiology, Infectious Diseases, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/d93d4d3ef3690a4e8747c37eca7366242eea9d8b

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Citations in the corpus

FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model (2010, neutral): “In the model, 5% of individuals taking antiviral agents prophylactically stop after 2 days and 5% taking them for treatment stop after 1 day [19].”
Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions (2007, background): “Whenever AV stockpiles are available, a certain fraction per day pAV of symptomatic infected individuals will enter the AV treatment, the efficacy of which is modeled through a reduction of both the infectiousness and the period during which the individual is contagious [6,7].”
How generation intervals shape the relationship between growth rates and reproductive numbers (2007, background): “Such a difference might explain the discrepancies between the various reported values for the mean generation interval of influenza A (Longini et al. 2004; Mills et al. 2004; Ferguson et al. 2005, 2006).”
Modelling transmission and control of the COVID-19 pandemic in Australia (2020, background): “Stochastic agent-based discrete-time simulation models such as these have been established as useful tools for tracing fine-grained effects of heterogeneous intervention policies in diverse epidemic and pandemic settings [18, 19, 10, 20, 9, 21, 22, 23, 24, 25] , including for policy advice currently”
Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models (2010, background): “We fix the average latency period ε-1 = 2 days and the average infectious period μ-1 = 3 days [4,18,43] equal in the two models.”
Time lines of infection and disease in human influenza: a review of volunteer challenge studies. (2008, background): “6 days estimated from epidemiologic data (4) and substantially shorter than other epidemiologic modeling studies have assumed (2, 5).”
Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. (2006, methodology): “To overcome the problems with estimating age-specific transmission parameters, we could infer likely contact patterns from age distributions and household sizes obtained from census data (2, 4, 13, 14) and from transportation data (15).”
Absolute Humidity and the Seasonal Onset of Influenza in the Continental United States (2010, background): “Estimates of R 0 derived or used by many authors range from 1.3 to 3 [16,21,22,24–27].”
Perspectives on the basic reproductive ratio (2005, background): “Topical issues such as the risks of indoor airborne infection (Rudnick & Milton 2003), bioterrorism (Kaplan et al. 2002; Longini et al. 2004), and computer viruses (Lloyd & May 2001) also rely on this important concept.”
Strategies for containing an emerging influenza pandemic in Southeast Asia (2005, background): “Large-scale prophylaxis has the potential to limit spread substantially in a developed country context 11 , but the very large stocks of drug necessary make this policy impractical if the pandemic is already global.”