Selected article for: "air traffic and SEIR model"

Author: Pai Liu; Payton Beeler; Rajan K Chakrabarty
Title: COVID-19 Progression Timeline and Effectiveness of Response-to-Spread Interventions across the United States
  • Document date: 2020_3_20
  • ID: 6ymuovl2_3
    Snippet: ( Figure 1) Modelling the Network-Driven Epidemic Dyamics of COVID-19. We simulated the COVID-19 epidemic spread in the United States using a Susceptible-Exposed-Infected-Recovered (SEIR) model (1, 2, 12, 13) coupled with network-driven dynamics (14) (15) (16) accounting for the domestic air traffic taking place amongst the 50 US states, Washington DC, and Puerto Rico (hereafter generically denoted as states). In the SEIR model, fractions of susc.....
    Document: ( Figure 1) Modelling the Network-Driven Epidemic Dyamics of COVID-19. We simulated the COVID-19 epidemic spread in the United States using a Susceptible-Exposed-Infected-Recovered (SEIR) model (1, 2, 12, 13) coupled with network-driven dynamics (14) (15) (16) accounting for the domestic air traffic taking place amongst the 50 US states, Washington DC, and Puerto Rico (hereafter generically denoted as states). In the SEIR model, fractions of susceptible ( ), exposed ( ), infected ( ), and recovered ( ) individuals are tracked within a state per the kinetics of mass action; The interstate exchange of passengers is captured with a matrix of quantifying the probability that an individual leaving state ends up in (14) . The governing equations can be expressed as a set of first-order differential equations with respect to time ( ):

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