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Mobility-Based SEAIR Epidemic Models
seairmobility provides tools for simulating, analysing,
and fitting mobility-based SEAIR
(Susceptible–Exposed–Asymptomatic–Infectious– Recovered) compartmental
epidemic models with heterogeneous individual mobility.
Each individual in the population carries a fixed mobility trait
m ∈ (0, 1) that scales both susceptibility and
infectiousness via a rank-one kernel. The infectious period is split
into an asymptomatic stage with relative infectiousness α
and a symptomatic stage with mobility-reduction factor
δ.
The package extends the mobility-based SIR framework of Jiang, Chu, and Li (2025, SIAM J. Appl. Math. 85(5), 2355–2375, doi:10.1137/24M1691557).
seair_solve() — numerical solver for the mobility-based
SEAIR PDE system (method-of-lines discretisation +
deSolve).R0_seair() — closed-form basic reproduction
number.final_size() / final_size_general() —
final epidemic size via the scalar fixed-point equation.fit_mobility() — parametric (Beta-mixture)
least-squares fit of the mobility distribution from an observed
symptomatic time series.library(seairmobility)
pars <- seair_params(beta = 1.5, sigma = 0.3, kappa = 0.2,
gamma_A = 0.1, gamma_I = 0.13,
alpha = 0.5, delta = 0.3)
m <- seq(0, 1, length.out = 101)
f <- dbeta(m, 2, 2)
init <- seair_init(m, f, I_seed = 1e-4)
sol <- seair_solve(init, pars, times = seq(0, 80, by = 1))
plot_seair(sol, which = c("S", "I", "R"))
R0_seair(pars, f, m_grid = m)
final_size(pars, f, m_grid = m)MIT
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.