FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model

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

Person-to-person contact probabilities for all social mixing groups in FluTE.

<p>Person-to-person contact probabilities for all social mixing groups in FluTE.</p

Age-specific influenza illness attack rates in past influenza epidemics (from [46]) and in a simulation of metropolitan Seattle.

<p>Age-specific influenza illness attack rates in past influenza epidemics (from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000656#pcbi.1000656-Longini5" target="_blank">[46]</a>) and in a simulation of metropolitan Seattle.</p

International traffic to the 15 US airports built into FluTE.

<p>Data from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000656#pcbi.1000656-U1" target="_blank">[45]</a>.</p

The prevalence of influenza in a single simulation of the United States 100 days after the start of an influenza epidemic with .

<p>The color of each dot corresponds to the illness prevalence in a census tract. Image created using ArcGIS (Environmental Systems Research Institute, Inc.)</p

Illness attack rates and daily prevalence of influenza in simulations of metropolitan Seattle.

<p>(A) Daily prevalence of symptomatic influenza in simulations of metropolitan Seattle for various and (B) for with various interventions. The interventions, which begin 30 days after the first case is detected, are: giving a course of antiviral agents to ascertained cases, closing schools either permanently or for 60 days, and pre-vaccination of 50% of the population with a well-matched seasonal influenza vaccine. (C) Final illness attack rates (180 days) vs for FluTE (simulating metropolitan Seattle) and a model with random mixing. Results for all panels are from one run of metropolitan Seattle for each or intervention strategy except for the simulation for in panel (A), which was run 5 times with different random number seeds and plotted to show stochastic variability.</p

Major sources of influenza transmission in simulations of metropolitan Seattle.

<p>Major sources of influenza transmission in simulations of metropolitan Seattle.</p

The ratio of cumulative illness attack rates between school-age children (ages 5–18) and adults (ages 19–64) over time in simulated epidemics.

<p>Results plotted are from one simulation of metropolitan Seattle for each value of .</p

Probabilities that an individual will withdraw to the home 0, 1, or 2 days after becoming symptomatic.

<p>Data from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000656#pcbi.1000656-Elveback2" target="_blank">[16]</a>.</p

Orchestrating high-throughput genomic analysis with Bioconductor.

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors