Showing 6 of total 6 results (show query)
epimodel
EpiModel:Mathematical Modeling of Infectious Disease Dynamics
Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).
Maintained by Samuel Jenness. Last updated 2 months ago.
agent-based-modelingepidemicsepidemiologyinfectious-diseasesnetwork-graphcpp
250 stars 11.43 score 315 scripts
ropensci
nlrx:Setup, Run and Analyze 'NetLogo' Model Simulations from 'R' via 'XML'
Setup, run and analyze 'NetLogo' (<https://ccl.northwestern.edu/netlogo/>) model simulations in 'R'. 'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses. The user defines all information that is needed in an intuitive framework, using class objects. Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user. By nesting model calls in future environments, large simulation design with many runs can be executed in parallel. This also enables simulating 'NetLogo' experiments on remote high performance computing machines. In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system.
Maintained by Sebastian Hanss. Last updated 7 months ago.
agent-based-modelingindividual-based-modellingnetlogopeer-reviewed
78 stars 8.86 score 195 scripts
uofuepibio
epiworldR:Fast Agent-Based Epi Models
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.
Maintained by Andrew Pulsipher. Last updated 1 days ago.
abmagent-based-modelingcovid-19epidemicsepidemiologyr-programmingrpackrpkgseirseir-modelsimulationsirsir-modelcppopenmp
9 stars 8.35 score 58 scripts 1 dependents
zizroc
villager:A Framework for Designing and Running Agent Based Models
This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs.
Maintained by Thomas Thelen. Last updated 10 months ago.
abmagent-based-modelingsimulation
56 stars 6.78 score 18 scriptsuofuepibio
epiworldRShiny:A 'shiny' Wrapper of the R Package 'epiworldR'
R 'shiny' web apps for epidemiological Agent-Based Models. It provides a user-friendly interface to the Agent-Based Modeling (ABM) R package 'epiworldR' (Meyer et al., 2023) <DOI:10.21105/joss.05781>. Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and Susceptible-Exposed-Infected-Recovered (SEIR) models. 'epiworldRShiny' provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively.
Maintained by Derek Meyer. Last updated 3 days ago.
abmagent-based-modelingcovid-19epidemic-simulationsepidemiologynetscinetwork-analysisshinyappsshinydashboardsimulation-modeling
1 stars 4.18 score 4 scripts
jacobnabe
DEPONS2R:Read, Plot and Analyse Output from the DEPONS Model
Methods for analyzing population dynamics and movement tracks simulated using the DEPONS model <https://www.depons.eu> (v.3.0), for manipulating input raster files, shipping routes and for analyzing sound propagated from ships.
Maintained by Jacob Nabe-Nielsen. Last updated 2 days ago.
agent-based-modelingenvironmental-modellingmarine-biology
2.95 score 4 scripts