epiworldR

[!NOTE] How to cite epiworldR. If you use epiworldR in published work, please cite it:

Meyer D, Vega Yon GG (2023). epiworldR: Fast Agent-Based Epi Models. Journal of Open Source Software, 8(90), 5781. doi:10.21105/joss.05781

Run citation("epiworldR") in R for the BibTeX entry.

This R package is a wrapper of the C++ library epiworld. It provides a general framework for modeling disease transmission using agent-based models. Some of the main features include:

• Fast simulation with an average of 30 million agents/day per second.
• One model can include multiple diseases.
• Policies (tools) can be multiple and user-defined.
• Transmission can be a function of agents’ features.
• Out-of-the-box parallelization for multiple simulations.

An evolving cross-framework benchmark compares epiworldR, epiworld, epiworldpy, and other epidemic ABM engines. In the SEIRH network scenarios currently covered, the epiworld family is among the fastest, while native epiworld is among the lower-memory implementations. These results are a snapshot of the tested versions, workloads, and environment rather than a universal ranking; the benchmark welcomes additional scenarios, engines, corrections, and implementation improvements through issues and pull requests.

From the package’s description:

A flexible framework for Agent-Based Models (ABM), the epiworldR package provides methods for prototyping disease outbreaks and transmission models using a C++ backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents’ features, providing great complexity for the model dynamics. Furthermore, epiworldR is ideal for simulation studies featuring large populations.

Current available models:

Note: Measles-specific models have been moved to the separate measles R package.

Note: Measles-specific models have been moved to the separate measles R package.

1. ModelDiagram
2. ModelDiffNet
3. ModelSEIR
4. ModelSEIRCONN
5. ModelSEIRD
6. ModelSEIRDCONN
7. ModelSEIRMixing
8. ModelSEIRMixingQuarantine
9. ModelSIR
10. ModelSIRCONN
11. ModelSIRD
12. ModelSIRDCONN
13. ModelSIRLogit
14. ModelSIRMixing
15. ModelSIS
16. ModelSISD
17. ModelSURV

Installation

You can install the development version of epiworldR from GitHub with:

devtools::install_github("UofUEpiBio/epiworldR")

Or from R-universe (recommended for the latest development version):

install.packages(
  'epiworldR',
  repos = c(
    'https://uofuepibio.r-universe.dev',
    'https://cloud.r-project.org'
  )
)

Or from CRAN

install.packages("epiworldR")

Who uses epiworldR?

usescout tracks where epiworldR is used: GitHub repositories, CRAN packages and papers, including papers that use it without citing it. The current list, with the exact place each one was found, is in the usage report; the data lives in the usescout branch. If you use epiworldR in published work, please cite it (see the box at the top).

Examples

This R package includes several popular epidemiological models, including SIS, SIR, and SEIR using either a fully connected graph (similar to a compartmental model) or a user-defined network.

SIR model using a random graph

This Susceptible-Infected-Recovered model features a population of 100,000 agents simulated in a small-world network. Each agent is connected to ten other agents. One percent of the population has the virus, with a 70% chance of transmission. Infected individuals recover at a 0.3 rate:

library(epiworldR)
#> Thank you for using epiworldR! Please consider citing it in your work.
#> You can find the citation information by running
#>   citation("epiworldR")

# Creating a SIR model
sir <- ModelSIR(
  name              = "COVID-19",
  prevalence        = .01,
  transmission_rate = .7,
  recovery          = .3
) |>
  # Adding a Small world population
  agents_smallworld(n = 100000, k = 10, d = FALSE, p = .01) |>
  # Running the model for 50 days
  run(ndays = 50, seed = 1912)
#> _________________________________________________________________________
#> |Running the model...
#> |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#> |

sir
#> ________________________________________________________________________________
#> Susceptible-Infected-Recovered (SIR)
#> It features 100000 agents, 1 virus(es), and 0 tool(s).
#> The model has 3 states.
#> The final distribution is: 0 Susceptible, 0 Infected, and 100000 Recovered.

Visualizing the outputs

summary(sir)
#> ________________________________________________________________________________
#> ________________________________________________________________________________
#> SIMULATION STUDY
#>
#> Name of the model   : Susceptible-Infected-Recovered (SIR)
#> Population size     : 100000
#> Agents' data        : (none)
#> Number of entities  : 0
#> Days (duration)     : 50 (of 50)
#> Number of viruses   : 1
#> Last run elapsed t  : 90.00ms
#> Last run speed      : 55.26 million agents x day / second
#> Rewiring            : off
#> Last seed used      : 1912
#>
#> Global events:
#>  (none)
#>
#> Virus(es):
#>  - COVID-19
#>
#> Tool(s):
#>  (none)
#>
#> Model parameters:
#>  - Recovery rate     : 0.3000
#>  - Transmission rate : 0.7000
#>
#> Distribution of the population at time 50:
#>   - (0) Susceptible :  99000 -> 0
#>   - (1) Infected    :   1000 -> 0
#>   - (2) Recovered   :      0 -> 100000
#>
#> Transition Probabilities:
#>  - Susceptible  0.85  0.15     -
#>  - Infected        -  0.70  0.30
#>  - Recovered       -     -  1.00

plot(sir)

plot_incidence(sir)

SEIR model with a fully connected graph

The SEIR model is similar to the SIR model but includes an exposed state. Here, we simulate a population of 10,000 agents with a 0.01 prevalence, a 0.6 transmission rate, a 0.5 recovery rate, and 7 days-incubation period. The population is fully connected, meaning agents can transmit the disease to any other agent:

model_seirconn <- ModelSEIRCONN(
  name                = "COVID-19",
  prevalence          = 0.01,
  n                   = 10000,
  contact_rate        = 10,
  incubation_days     = 7,
  transmission_rate   = 0.1,
  recovery_rate       = 1 / 7
) |> add_virus(
  virus(
    name = "COVID-19 (delta)",
    prevalence = 0.01,
    as_proportion = TRUE,
    prob_infecting = 0.2,
    recovery_rate = 0.6,
    prob_death = 0.5,
    incubation = 7
))

set.seed(132)
run(model_seirconn, ndays = 100)
#> _________________________________________________________________________
#> Running the model...
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
summary(model_seirconn)
#> ________________________________________________________________________________
#> ________________________________________________________________________________
#> SIMULATION STUDY
#>
#> Name of the model   : Susceptible-Exposed-Infected-Removed (SEIR) (connected)
#> Population size     : 10000
#> Agents' data        : (none)
#> Number of entities  : 0
#> Days (duration)     : 100 (of 100)
#> Number of viruses   : 2
#> Last run elapsed t  : 18.00ms
#> Last run speed      : 53.77 million agents x day / second
#> Rewiring            : off
#> Last seed used      : 2587
#>
#> Global events:
#>  - Update infected individuals (runs daily)
#>
#> Virus(es):
#>  - COVID-19
#>  - COVID-19 (delta)
#>
#> Tool(s):
#>  (none)
#>
#> Model parameters:
#>  - Avg. Incubation days : 7.0000
#>  - Contact rate         : 10.0000
#>  - Prob. Recovery       : 0.1429
#>  - Prob. Transmission   : 0.1000
#>
#> Distribution of the population at time 100:
#>   - (0) Susceptible :  9800 -> 58
#>   - (1) Exposed     :   200 -> 0
#>   - (2) Infected    :     0 -> 2
#>   - (3) Recovered   :     0 -> 9940
#>
#> Transition Probabilities:
#>  - Susceptible  0.95  0.05     -     -
#>  - Exposed         -  0.86  0.14     -
#>  - Infected        -     -  0.79  0.21
#>  - Recovered       -     -     -  1.00

Computing some key statistics

plot(model_seirconn)

repnum <- get_reproductive_number(model_seirconn)

head(plot(repnum))

#>   virus_id    virus date      avg   n       sd   lb    ub
#> 1        0 COVID-19    0 4.720000 100 4.249967 0.00 16.05
#> 2        0 COVID-19    2 5.090909  11 4.134115 0.25 12.50
#> 3        0 COVID-19    3 4.225806  31 3.148647 0.75 11.25
#> 4        0 COVID-19    4 3.756757  37 3.522983 0.00 11.30
#> 5        0 COVID-19    5 3.526316  38 3.725274 0.00 14.00
#> 6        0 COVID-19    6 3.421053  57 3.087691 0.00 11.60

head(plot_generation_time(model_seirconn))

#>   date      avg  n       sd ci_lower ci_upper    virus virus_id
#> 1    0 8.340909 88 5.858261        2   21.825 COVID-19        0
#> 2    2 8.600000 10 6.022181        3   20.425 COVID-19        0
#> 3    3 7.033333 30 4.286976        2   16.550 COVID-19        0
#> 4    4 7.562500 32 5.028548        2   17.450 COVID-19        0
#> 5    5 7.714286 35 5.659825        2   21.800 COVID-19        0
#> 6    6 7.489796 49 5.272264        2   21.200 COVID-19        0

SIR Logit

This model provides a more complex transmission and recovery pattern based on agents’ features. With it, we can reflect co-morbidities that could change the probability of infection and recovery. Here, we simulate a population including a dataset with two features: an intercept and a binary variable Female. The probability of infection and recovery are functions of the intercept and the Female variables. The following code simulates a population of 100,000 agents in a small-world network. Each agent is connected to eight other agents. One percent of the population has the virus, with an 80% chance of transmission. Infected individuals recover at a 0.3 rate:

# Simulating a population of 100,000 agents
set.seed(2223)
n <- 100000

# Agents' features
X <- cbind(
  Intercept = 1,
  Female    = sample.int(2, n, replace = TRUE) - 1
)

coef_infect  <- c(.1, -2, 2)
coef_recover <- rnorm(2)

# Creating the model
model_logit <- ModelSIRLogit(
  "covid2",
  data = X,
  coefs_infect      = coef_infect,
  coefs_recover     = coef_recover,
  coef_infect_cols  = 1L:ncol(X),
  coef_recover_cols = 1L:ncol(X),
  prob_infection = .8,
  recovery_rate = .3,
  prevalence = .01
)

# Adding a small-world population
agents_smallworld(model_logit, n, 8, FALSE, .01)

# Running the model
run(model_logit, 50)
#> _________________________________________________________________________
#> |Running the model...
#> |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#> |

plot(model_logit)

# Females are supposed to be more likely to become infected
rn <- get_reproductive_number(model_logit)

(table(
  X[, "Female"],
  (1:n %in% rn$source)
) |> prop.table())[, 2]
#>       0       1
#> 0.20521 0.22094

# Looking into the agents
get_agents(model_logit)
#> Agents from the model "Susceptible-Infected-Removed (SIR) (logit)":
#> Agent: 1, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 2, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 3, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 4, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 5, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 6, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 7, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 8, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 9, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 10, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> ... 99990 more agents ...

Transmission network

This example shows how we can draw a transmission network from a simulation. The following code simulates a population of 500 agents in a small-world network. Each agent is connected to ten other agents. One percent of the population has the virus, with a 50% chance of transmission. Infected individuals recover at a 0.5 rate:

# Creating a SIR model
sir <- ModelSIR(
  name           = "COVID-19",
  prevalence     = .01,
  transmission_rate = .5,
  recovery       = .5
) |>
  # Adding a Small world population
  agents_smallworld(n = 500, k = 10, d = FALSE, p = .01) |>
  # Running the model for 50 days
  run(ndays = 50, seed = 1912)
#> _________________________________________________________________________
#> |Running the model...
#> |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#> |

# Transmission network
net <- get_transmissions(sir)
net <- subset(net, source >= 0)

# Plotting
library(epiworldR)
library(netplot)
#> Loading required package: grid
x <- igraph::graph_from_edgelist(
  as.matrix(net[, 2:3]) + 1
)

nplot(x, edge.curvature = 0, edge.color = "gray", skip.vertex = TRUE)

Multiple simulations

epiworldR supports running multiple simulations using the run_multiple function. The following code simulates 50 SIR models with 1000 agents each. Each agent is connected to ten other agents. One percent of the population has the virus, with a 90% chance of transmission. Infected individuals recover at a 0.1 rate. The results are saved in a data.frame:

model_sir <- ModelSIRCONN(
  name = "COVID-19",
  prevalence = 0.01,
  n = 1000,
  contact_rate = 2,
  transmission_rate = 0.9, recovery_rate = 0.1
)

# Generating a saver
saver <- make_saver("total_hist", "reproductive")

# Running and printing
# Notice the use of nthread = 2 to run the simulations in parallel
run_multiple(model_sir, ndays = 100, nsims = 50, saver = saver, nthread = 2)
#> Starting multiple runs (50) using 2 thread(s)
#> _________________________________________________________________________
#> _________________________________________________________________________
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.

# Retrieving the results
ans <- run_multiple_get_results(model_sir)

head(ans$total_hist)
#>   sim_num date nviruses       state counts
#> 1       1    0        1 Susceptible    990
#> 2       1    0        1    Infected     10
#> 3       1    0        1   Recovered      0
#> 4       1    1        1 Susceptible    964
#> 5       1    1        1    Infected     35
#> 6       1    1        1   Recovered      1
head(ans$reproductive)
#>   sim_num virus_id    virus source source_exposure_date rt
#> 1       1        0 COVID-19    827                   11  0
#> 2       1        0 COVID-19    790                    8  0
#> 3       1        0 COVID-19    700                    8  0
#> 4       1        0 COVID-19    378                    8  0
#> 5       1        0 COVID-19    339                    8  0
#> 6       1        0 COVID-19    246                    8  0

plot(ans$reproductive)

Tutorials

• The virtual INSNA Sunbelt 2023 session can be found here: https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbelt2023-virtual

• The in-person INSNA Sunbelt 2023 session can be found here: https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbetl2023-inperson

Citation

If you use epiworldR in your research, please cite it as follows:

citation("epiworldR")
#> To cite epiworldR in publications use:
#>
#>   Meyer, Derek and Vega Yon, George (2023). epiworldR: Fast Agent-Based
#>   Epi Models. Journal of Open Source Software, 8(90), 5781,
#>   https://doi.org/10.21105/joss.05781
#>
#> And the actual R package:
#>
#>   Meyer D, Pulsipher A, Vega Yon G (2025). _epiworldR: Fast Agent-Based
#>   Epi Models_. R package version 0.15.1-0,
#>   <https://github.com/UofUEpiBio/epiworldR>.
#>
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.

Existing Alternatives

Several alternatives to epiworldR exist and provide researchers with a range of options, each with its own unique features and strengths, enabling the exploration and analysis of infectious disease dynamics through agent-based modeling. Below is a manually curated table of existing alternatives, including ABM [@ABM], abmR [@abmR], cystiSim [@cystiSim], villager [@villager], and RNetLogo [@RNetLogo].

Package	Multiple Viruses	Multiple Tools	Multiple Runs	Global Actions	Built-In Epi Models	Dependencies	Activity
epiworldR	yes	yes	yes	yes	yes
ABM	-	-	-	yes	yes
abmR	-	-	yes	-	-
cystiSim	-	yes	yes	-	-

Other ABM R packages

You may want to check out other R packages for agent-based modeling: ABM, abmR, cystiSim, villager, and RNetLogo.

Contributing to epiworldR

We welcome contributions to the epiworldR package! If you would like to contribute, please review our development guidelines before creating a pull request.

Code of Conduct

The epiworldR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.