FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model

By D. Chao, M. Halloran, Valerie Obenchain, I. Longini, A. McLean. PLoS Comput. Biol., 2010

374 citations recorded by the source corpus.

Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development.

Subfields: Epidemiology, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/7d9f426165cc10a1ce279136f8c3b3362f7a0bb9

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Citations in the corpus

Modelling transmission and control of the COVID-19 pandemic in Australia (2020, background): “We also define a scaling factor κ (proportional to the reproductive number R 0 ), as a free Table 1 : Daily contact probabilities c g j→i for different contact groups g, reported by [24] , reproduced from [14] , except for the rates in household clusters.”
Projecting social contact matrices in 152 countries using contact surveys and demographic data (2017, background): “In actual usage, the translation of a policy into a specific change in contact rates should be supported by evidence, while individual-based simulation models [64] provide a more flexible framework to capture shared contacts between individuals and allows policies such as reactive school closure to ”
Networks and the Epidemiology of Infectious Disease (2010, background): “A similar approach of generating artificial networks of individuals for stochastic simulations of respiratory disease has been recently applied to influenza at the scale of the United States, and the software made generally available [36].”
Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models (2010, background): “Indeed, as observed in earlier works, models with heterogeneous transmission rates across population groups present different attack rates - usually lower - than those with homogenous mixing, even for the same overall value of R 0 (See for instance the discussion in [47,48] and references therein).”