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.

References in zbMATH (referenced in 10 articles )

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  1. Papst, Irena; O’Keeffe, Kevin P.; Strogatz, Steven H.: Modeling the interplay between seasonal flu outcomes and individual vaccination decisions (2022)
  2. Enayati, Shakiba; Özaltın, Osman Y.: Optimal influenza vaccine distribution with equity (2020)
  3. Li, Meili; Wang, Hong; Song, Baojun; Ma, Junling: The spread of influenza-like-illness within the household in Shanghai, China (2020)
  4. Duijzer, Lotty Evertje; van Jaarsveld, Willem; Dekker, Rommert: The benefits of combining early aspecific vaccination with later specific vaccination (2018)
  5. Arenas, Abbiana R.; Thackar, Neil B.; Haskell, Evan C.: The logistic growth model as an approximating model for viral load measurements of influenza a virus (2017)
  6. Wang, Zhen; Bauch, Chris T.; Bhattacharyya, Samit; d’Onofrio, Alberto; Manfredi, Piero; Perc, Matjaž; Perra, Nicola; Salathé, Marcel; Zhao, Dawei: Statistical physics of vaccination (2016)
  7. Bisset, Keith R.; Chen, Jiangzhuo; Deodhar, Suruchi; Feng, Xizhou; Ma, Yifei; Marathe, Madhav V.: INDEMICS: an interactive high-performance computing framework for data-intensive epidemic modeling (2014)
  8. Franklin, Jessica M.; Schneeweiss, Sebastian; Polinski, Jennifer M.; Rassen, Jeremy A.: Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases (2014)
  9. Hu, Hao; Nigmatulina, Karima; Eckhoff, Philip: The scaling of contact rates with population density for the infectious disease models (2013)
  10. Poletto, Chiara; Tizzoni, Michele; Colizza, Vittoria: Human mobility and time spent at destination: impact on spatial epidemic spreading (2013)