One of the benefits of an agent-based model like EMOD over compartmental models is that the the model can be configured to capture heterogeneity in population demographics, migration patterns, disease transmissibility, climate, interventions, and more. This heterogeneity can affect the overall course of the disease outbreak and campaign interventions aimed at controlling it.
Built-in demographics options are available for running EMOD simulations, or you can create customized demographics files to represent particular locations. It is generally recommended that you create a demographics file instead of using built-in demographics.
Every individual within the simulation has a variety of attributes, represented by continuous or discrete state variables. Some are static throughout life, and others dynamically change through the course of the simulation, through response either to aging or to simulation events (such as infection). Static attributes are assigned upon instantiation (simulation initialization or birth after the beginning of the simulation) and include gender, time of birth, time of non-disease death, etc. Dynamic attributes include disease state, history of interventions, and more.
Vital dynamics within EMOD are derived from fertility and mortality tables that are passed to the model as input. Input demographic data can be used to construct a cumulative probability distribution function (CDF) of death date based on individuals’ birth dates. Then, in the model, individual agents will be sampled stochastically from this CDF using an inverse transform of this distribution. Female agents similarly sample the age at next childbirth, if any, upon instantiation and birth of a previous child. Pregnancy is not linked to relationship status, although newly born individuals are linked to a mother. The fertility rate changes by simulation year and female age, and the range for available estimates depends on input data. Values outside of this range can be chosen by “clamping,” or choosing the nearest value within the range. Clamping was also used when necessary to determine the non-disease mortality rate, which varies by gender, age, and simulation year.
For more information on the demographics file, see Demographics file.
Individual and node properties¶
One of the most powerful and flexible features of EMOD is the ability to assign properties to nodes or individuals that can then be used to target interventions or move individuals through a health care system. For example, you might assign various degrees of risk, socioeconomic status, intervention status, and more. In the generic, environmental, and typhoid simulation types, these properties can be leveraged to add heterogeneity in transmission based on the property values assigned to each individual. For example, you might configure higher transmission among school-age children.
Similarly, you can add properties to nodes in a simulation. For example, you might configure some regions to be more urban or rural, to have better health care, or to be difficult to access by health care workers.
By default, EMOD assumes transmission is homogeneous in each node: transmission does not vary based on population density, the population is “well-mixed” in each node, each individual has an equal likelihood of transmitting or contracting a disease. However, all of these aspects can be configured for greater heterogeneity to more accurately simulate a realistic population and disease dynamics. One simple example is varying transmission rates based on population density.
Other EMOD simulation types capture heterogeneous transmission through more biologically mechanistic parameters that control aspects of the simulation such as parasite density, symptom severity, coital acts, and more. Review the model overview for those simulation types for more information.
EMOD can also simulate human and vector migration, which can be important in the transmission of many diseases. You can assign different characteristics to each geographic node to control how the disease spreads.
For more information on how you can target campaign interventions to individuals or locations based on certain criteria, see Creating campaigns.
- Population density and transmission scaling
- Individual and node properties
- Property-based heterogeneous disease transmission
- Multi-route HINT
- Geographic migration