SPARSE-MOD: Overview and Key Features

SPARSE-MOD stands for SPAtial Resolution-SEnsitive Models of Outbreak Dynamics. Our goal with this R package is to offer a framework for simulating the dynamics of stochastic and spatially-explicit models of infectious disease. As we develop the package, our goal is to add more model structures and more user-control of the model dynamics. Our SPARSEMODr package offers several key features that should make it particularly relevant for pedogogical and practical use. See our COVID-19 model vignette for detailed walk-throughs of how to run the model, to plot the output, and to simulate customized time-windows.


Time windows

One of the benefits of the SPARSEMODr design is that the user can specify how certain parameters of the model change over time. In this particular example, we show how the time-varying R0 changes in a stepwise fashion due to ‘interventions’ and ‘release of interventions’. We assume that when a parameter value changes between two time windows, there is a linear change over the number of days in that window. In other words, the user specifies the value of the parameter acheived on the last day of the time window. Note, however, that the user is allowed to supply daily parameter values to avoid this linear-change assumption. Here we show an example of a pattern of time-varying R0 that the user might specify, and how the C++ code is essentially interpretting these values on the back-end.

# Set up the dates of change. 5 time windows
n_windows = 5
# Window intervals
start_dates = c(mdy("1-1-20"),  mdy("2-1-20"),  mdy("2-16-20"), mdy("3-11-20"), mdy("3-22-20"))
end_dates   = c(mdy("1-31-20"), mdy("2-15-20"), mdy("3-10-20"), mdy("3-21-20"), mdy("5-1-20"))
# Time-varying R0
changing_r0 = c(3.0,            0.8,            0.8,            1.4,            1.4)

#R0 sequence
r0_seq = NULL

r0_seq[1:(yday(end_dates[1]) - yday(start_dates[1]) + 1)] =

for(i in 2:n_windows){

  r0_temp_seq = NULL
  r0_temp = NULL

  if(changing_r0[i] != changing_r0[i-1]){

    r0_diff = changing_r0[i-1] - changing_r0[i]
    n_days = yday(end_dates[i]) - yday(start_dates[i]) + 1
    r0_slope = - r0_diff / n_days

    for(j in 1:n_days){
      r0_temp_seq[j] = changing_r0[i-1] + r0_slope*j

    n_days = yday(end_dates[i]) - yday(start_dates[i]) + 1
    r0_temp_seq = rep(changing_r0[i], times = n_days)

  r0_seq = c(r0_seq, r0_temp_seq)


# Create a data frame for plotting
## Date sequence:
date_seq = seq.Date(start_dates[1], end_dates[n_windows], by = "1 day")
r0_seq_df = data.frame(r0_seq, date_seq)
date_breaks = seq(range(date_seq)[1],
                  by = "1 month")

ggplot(r0_seq_df) +
  geom_path(aes(x = date_seq, y = r0_seq)) +
  scale_x_date(breaks = date_breaks, date_labels = "%b") +
  labs(x="", y="Time-varying R0") +
    axis.text = element_text(size = 10, color = "black"),
    axis.title = element_text(size = 12, color = "black"),
    axis.text.x = element_text(angle = 45, vjust = 0.5)

Dispersal kernel

As we discuss in the documentation (see ?SPARSEMODr::Movement), we allow migration between populations in the meta-population to affect local and regional transmission dynamics. For now, migration is determined by a simple dispersal kernel, although we are working on adding more customizable gravity kernels. The user can control the shape of this kernel with the dist_param option, as follows: \[ p\_{i,j} = \\frac{1}{\\text{exp}(d\_{i,j} / \\text{dist\_param})}, \] where pi, j is the probability of moving from population j to population i and di, j is the euclidean distance between the two populations.

We can see how the dist_param controls the probability below. In general, larger values of dist_param make it more likely for hosts to travel farther distances.

Density-dependent Transmission

As we describe in the documentation (e.g., see ?SPARSEMODr::covid19_model_interface), we allow the user to implement frequency-dependent or density-dependent (DD) transmission in the SPARSEMODr models. For DD transmission, the user can specify a (non-)linear Monod equation that describes the relationship between host population density and the transmission rate β via the model’s (optional) parameter, dd_trans_monod_k. The Monod equation is: \[ \\beta\_{\\text{realized}} = \\beta\_{\\text{max}} \\frac{\\text{Dens}}{K + \\text{Dens}}, \] where βmax is the maximum possible transmission rate across all densities, Dens is the density of the focal host population, and K is a constant that controls the effect of density on the transmission rate and is user-controlled by specifying dd_trans_monod_k. More specifically, K is the half-velocity constant at which point βrealized/βmax = 0.5.

We can see how dd_trans_monod_k controls the transmission rate below. In general, larger values of dd_trans_monod_k mean that transmission rate is more strongly limited by population density.

[1] A set of distinct, focal populations that are connected by migration

[2] Also known as the effective reproduction number or the instantaneous R0 (Rt)

[3] The effects of probabilistic events that befall a population and that can affect epidemic trajectories.

[4] Models are based off of differential equation models, but we use a tau-leaping algorithm - in the Gillespie family - to simulate the model one day at a time.