Mitigation strategies for pandemic influenza in the United States.

By T. Germann, K. Kadau, I. Longini, C. Macken. Proceedings of the National Academy of Sciences of the United States of America, 2006

1,137 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Epidemiology, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/d5c54719df80fc0de92eb8790ebd4fcd91e6fcc6

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Citations in the corpus

Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases (2008, methodology): “For instance, household size, class size, transport statistics, and workplace size distribution have been used in recent models to define the contact structure [2,3,33,34].”
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].”
Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models (2010, background): “The agent-based model is defined at the level of the country, and, as in other individual-based stochastic simulations describing the scale of a given region [3,6,7], it is based on the importation of cases from abroad.”
Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions (2007, background): “In this context, models that do not consider international travel [33] are missing an important variable and cannot provide a full picture of the occurrence of global pandemics.”
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].”
Networks and the Epidemiology of Infectious Disease (2010, background): “Four main forms of movement network have played important roles in understanding the spread of infectious diseases: the airline transportation network [37, 38], the movement of individuals to and from work [39, 40], the movement of dollar bills (from which the movement of people can be inferred) [41”
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).”
Estimating the impact of school closure on influenza transmission from Sentinel data (2008, background): “Irrespective of {dcom, dhous}, we find that accounting for holidays improves the model fit: (1) log-likelihoods are larger (see Supplementary Information); (2) child RPE becomes close to 0% during holidays (Fig.”
EpiSimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks (2008, background): “Recently, a number of other researchers have also proposed the use of realistic social contact networks in studying the spread of contagion [10]–[12].”
Multiscale mobility networks and the spatial spreading of infectious diseases (2009, background): “INTRODUCTION Computational approaches to the realistic modeling of spatial epidemic spread make use of a wide array of simulation schemes [1] ranging from very detailed agent based approaches [2, 3, 4, 5, 6] to structured metapopulation models based on data-driven mobility schemes at the inter-popul”
A Third Wave in the Economics of Climate Change (2015, background): “…been applied in a diverse range of different contexts, including conflict dynamics (Lim et al. 2007; Epstein 2002), pandemics (Halloran et al. 2008; Germann et al. 2006; Epstein 2009), urban planning (Brown and Robinson 2006; Ligtenberg et al. 2001; Batty 2009), electricity markets (Weidlich and…”
Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model (2010, methodology): “, the US [24,19,27], the UK [19], Italy [8], Thailand [33,18] – up to the continent of Europe [34].”
Modelling transmission and control of the COVID-19 pandemic in Australia (2020, methodology): “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”
FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model (2010, methodology): “Sensitivity analyses of similar epidemic models have shown that results are robust to uncertainty in many parameters [3,5,6,11].”