EpiModel

EpiModel models the population dynamics of infectious disease transmission using stochastic network-based mathematical models implemented in the R programming environment.

Key Features:

• Network-Based Modeling Framework: Models epidemics on contact networks to represent interpersonal contact patterns that drive transmission.
• Stochastic Simulation Framework: Implements a general stochastic framework to represent variability in epidemic spread on networks.
• Integration with Temporal Exponential Random Graph Models (TERGMs): Leverages TERGMs to fit and incorporate empirical contact network structure and temporal network dynamics into simulations.
• Extensible Application Programming Interface (API): Provides an API to extend models and implement custom network, epidemic, or intervention components.
• Implementation in R: Implemented within the R programming environment.

Scientific Applications:

• Outbreak Simulation: Simulates outbreak scenarios to explore epidemic curves and transmission dynamics.
• Intervention Strategy Evaluation: Evaluates the epidemiological impact of interventions and control strategies through network-based simulations.
• Public Health Policy Assessment: Assesses potential effects of public health policies on disease spread at the population level.
• Transmission Dynamics with Empirical Contact Data: Investigates diseases whose transmission depends on social interactions by integrating empirical contact data into network models.

Methodology:

Constructs stochastic network-based models with temporal dynamics and simulates epidemic spread, integrating temporal exponential random graph models (TERGMs) to incorporate empirical contact networks; implemented in R.