Modeling targeted layered containment of an influenza pandemic in the United States

By M. Halloran, Neil M. Ferguson, Stephen G. Eubank, I. Longini, Derek A. T. Cummings, B. Lewis, Shufu Xu, Christophe Fraser, A. Vullikanti, T. Germann and 7 more. Proceedings of the National Academy of Sciences of the United States of America, 2008

701 citations recorded by the source corpus.

Subfields: Epidemiology, Infectious Diseases, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/50c9404f4acb70b43d3ac3a22c2499dbba7379e9

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Citations in the corpus

Dynamics and Control of Diseases in Networks with Community Structure (2010, background): “patterns seen in the spread of directly transmissible diseases such as HIV/AIDS [1,4,5], SARS [6,7], influenza [8,9,10,11] etc.”
Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models (2010, methodology): “[10] where three individual-based models with different assumptions and data - one at the description level of a city and two at the description level of a country - were compared through their predictions in the case of interventions against a new pandemic influenza strain.”
State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--3. (2012, background): “Joint publication where several groups have evaluated similar policy questions by using disparate approaches can help build confidence in the use of models as a tool for policy (e.g., [61] ), as shown by a recently conducted appraisal of modeling tools for evaluating the cost-effectiveness of variou”
Modelling transmission and control of the COVID-19 pandemic in Australia (2020, background): “Thus, a majority of the transmission and contact probabilities follow previous work on pandemic influenza [69, 44, 18, 36, 19, 20], see Supplementary Tables 9 and 10 in Appendix E.”
Human mobility: Models and applications (2017, background): “The level of granularity present in such models, enables minute analysis such as the effect of closing specific schools, employing different vaccination protocols (say by age) on disease propagation [358, 359].”
A Third Wave in the Economics of Climate Change (2015, background): “2007; Epstein 2002), pandemics (Halloran et al. 2008; Germann et al. 2006; Epstein 2009), urban planning (Brown and Robinson 2006; Ligtenberg et al.”
A high-resolution human contact network for infectious disease transmission (2010, background): “Schools are particularly vulnerable to infectious disease spread because of the high frequency of close proximity interactions (CPIs) that most infectious disease transmission depends on (3, 4).”
FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model (2010, background): “These contact probabilities are in general agreement with other simulation models [8] and with a recent study of physical contacts between individuals [21].”
Mining social mixing patterns for infectious disease models based on a two-day population survey in Belgium (2009, positive): “the basic reproduction number R0, equaled 2 (thus mimicking the emergence of pandemic influenza [23,24]).”
Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model (2010, background): “The intensive CPU requirements of agent-based models limit the feasibility of large explorations of the space of parameters aimed at estimation procedures, or at performing sensitivity analysis on the parameters included in the models to assess effects in the simulated results induced by their chang”