hawkes_growth: Hawkes Process Model for Epidemic Data

In swash: Health Geography Toolbox for Model-Based Analysis of Infections Panel Data

Hawkes Process Model for Epidemic Data

Description

Estimation of Hawkes Process models from incremental infections data

Usage

hawkes_growth(
  y,
  optim_method = "L-BFGS-B",
  verbose = FALSE
  )

Arguments

y	numeric vector with incremental infections data over time (e.g., daily infections)
optim_method	character specifying the optimization algorithm, passed to stats::optim
verbose	bool argument which indicates whether progress messages are displayed

Details

This function allows the estimation of a Hawkes Process model, with the time decay being expressed as exponential function, which results in three estimated parameters (\mu, \alpha, and \beta). The user must specify the dependent variable (incremental infections). The estimation is performed using nonlinear estimation via stats::optim. See the corresponding documentation for available optimization methods (default: "L-BFGS-B").

Value

object of class hawkes-class

Author(s)

Thomas Wieland

References

Rizoiu MA, Mishra S, Kong Q, Carman M, Xie L. (2018) SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations. In: Proceedings of the 2018 World Wide Web Conference. WWW’18. Republic and Canton of Geneva, CHE: International World Wide Web Conferences Steering Committee, p. 419–428. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1145/3178876.3186108")}

See Also

logistic_growth, exponential_growth, breaks_growth

Examples

data(Infections)
# Confirmed SARS-CoV-2 cases in Germany

hawkes_BS <- hawkes_growth(
  y = Infections$infections_daily
)
# Hawkes Process model

summary(hawkes_BS)
# Summary of Hawkes model estimates

plot(hawkes_BS)
# Plot of Hawkes Process model

swash documentation built on April 7, 2026, 1:06 a.m.