Containing Pandemic Influenza at the Source

By I. Longini, A. Nizam, Shufu Xu, K. Ungchusak, W. Hanshaoworakul, Derek A. T. Cummings, M. Halloran. Science, 2005

1,226 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5cf9be61a7c5a57aadc483c524bc46654ab43ac6

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Citations in the corpus

Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions (2007, neutral): “For this reason, several recent studies provided evidence that the use of AV drugs is an effective strategy for the local containment of the emerging pandemic strain at the source [7,10].”
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.”
Networks and the Epidemiology of Infectious Disease (2010, background): “The quantity β, therefore, defines a population-level transmission rate that can be interpreted in one of two ways as N → ∞.”
Game Theory of Social Distancing in Response to an Epidemic (2010, result): “Thus, the simple epidemic model is often considered as a worst-casescenario, when compared with more complex network models [29,30] and agent-based models [31–33].”
EpiSimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks (2008, methodology): “Two other simulation methods that are closely related to EpiSimdemics are the parallel simulation systems developed by Longini et al [25], and Ferguson [24].”
On the Use of Human Mobility Proxies for Modeling Epidemics (2013, background): “sion characterized by an exponentially distributed infectious period with average m~3 days [43,44], and explore three epidemic scenarios by varying the transmissibility b and corresponding to the following values of the basic reproductive number (average number of secondary cases per primary case in”
Avian Influenza Virus (H5N1): a Threat to Human Health (2007, background): “Simulation models of pandemics in silico have provided insights into the potential impact of options or combinations of options that may conceivably stop an incipient pandemic or mitigate its impact (51, 52, 135).”
Modelling transmission and control of the COVID-19 pandemic in Australia (2020, background): “Parameter Range R0 (y1) Tgen (y2) Ċ (y3) Ac (y4) μi,1 σi,1 μ ∗ i,2 σi,2 μ ∗ i,3 σi,3 μ ∗ i,4 σi,4 Time-to-peak, days (x1) [4, 7] 0.”
Predicting Flu Trends using Twitter data (2011, background): “Studies have shown that preventive measures can be taken to contain epidemics, if an early detection is made during the germination of an epidemic [2], [3].”
A high-resolution human contact network for infectious disease transmission (2010, neutral): “(3, 4, (12) (13) (14) (15) .”
Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases (2008, background): “Preparing for outbreaks of directly transmitted pathogens such as pandemic influenza [1] [2] [3] and SARS [4] [5] [6] [7] [8] [9] , and controlling endemic diseases such as tuberculosis and meningococcal diseases, are major public health priorities.”
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.”