Generic model overview

Disease models play an important role in understanding and managing the transmission dynamics of various pathogens. We can use them to describe the spatial and temporal patterns of disease prevalence, as well as to explore or better understand the factors that influence infection incidence. Modeling is a key step in understanding what treatments and interventions can be most effective, how cost-effective these approaches may be, and what specific factors need to be considered when trying to eradicate disease.

To understand the complex dynamics underlying disease transmission, epidemiologists often use a set of models called compartmental models. Developed in the early 20th century, these models stratify a population into groups, generally based on their risk or infection status. Underlying these models is a system of differential equations that track the number of people in each category over time. If you would like a more in-depth introduction to epidemiology and disease modeling, you may want to take the Epidemics course from The Pennsylvania State University through Coursera.

Epidemiological MODeling software (EMOD) is an agent-based model (ABM), another powerful tool used to help understand the complexity inherent in disease transmission systems. These models form a type of “microscale model,” where they simulate the simultaneous interactions of agents in an effort to recreate complex phenomena. Each agent (such as a human or vector) can be assigned a variety of properties (for example, age, gender, etc.), and their behavior and interactions with one another are determined using decision rules. EMOD supports different simulation types for various diseases and disease classes; the features present in the generic simulation type, which can be configured to model a variety of different diseases, are inherited by the more specific simulation types, such as malaria or HIV. For more information on the software architecture and inheritance, see Overview of EMOD software.

These models have strong predictive power and are able to leverage spatial and temporal dynamics. Further, complex environments can be developed in which the agents act, and agents may “learn” from interactions or “adapt” to their environment. As a result, ABMs are excellent for identifying emerging properties of the system: patterns that are not explicitly modeled, but instead occur as a consequence of the rules that govern the agents.

The figure below demonstrates the main components of the generic EMOD simulation type. Further information on aspects of these components can be found in the following sections.

Network diagram illustrating the generic model and its constituent components.

• Compartmental models and EMOD
  • Detailed model comparison
    • Compartmental models
    • EMOD model
      • SIR and SIRS models
        • SIR model
          • SIR without vital dynamics
          • SIR with vital dynamics
        • SIRS model
          • SIRS without vital dynamics
          • SIRS with vital dynamics
      • SEIR and SEIRS models
        • SEIR model
          • SEIR without vital dynamics
          • SEIR with vital dynamics
        • SEIRS model
          • SEIRS with vital dynamics
      • SI and SIS models
        • SI model
          • SI without vital dynamics
          • SI with vital dynamics
        • SIS
          • SIS without vital dynamics
          • SIS with vital dynamics
• Disease outbreaks and animal reservoirs
  • Outbreak events
    • Emergent outbreak
    • Endemic disease
  • Infectivity reservoirs
• Adding heterogeneity
  • Population density and transmission scaling
    • Frequency-dependent scaling
    • Density-dependent scaling
    • Saturating function of population density
  • Individual and node properties
    • Create individual properties other than age
    • Create properties for age ranges
  • Property-based heterogeneous disease transmission
  • Multi-route HINT
  • Geographic migration
• Creating campaigns
  • Multiple interventions
  • Campaign file overview
    • Targeting interventions to nodes or individuals
      • Target to nodes based on geography
      • Target to nodes or individuals using properties
      • Illustrated examples
        • Single property, single value
        • Single property, multiple values
        • Multiple properties, individuals must match all values
        • Multiple properties, individuals must match at least one value
        • Target an age range
    • Vaccination and herd immunity
      • Multiple rounds of vaccinations
    • Cascade of care
      • Cascade of care for the HIV model
        • Entry into the cascade: types of testing
        • Moving through the cascade
        • Configuring the cascade