The Epidemiological MODeling software (EMOD) glossary is divided into the following subsections that define terms related to software usage, general epidemiology, and the particular disease being modeled.
The following terms are used to describe both general computing processes and concepts and the files, features, and functionality related to running simulations with Epidemiological MODeling software (EMOD).
- agent-based model¶
A type of simulation that models the actions and interactions of autonomous agents (both individual and collective entities such as organizations or groups).
Free, peer-reviewed, portable C++ source libraries aimed at a wide range of uses including parallel processing applications (Boost.MPI). For more information, please see the Boost website, http://www.boost.org.
- boxcar function¶
A mathematical function that is equal to zero over the entire real line except for a single interval where it is equal to a constant.
A collection of events that use interventions to modify a simulation.
- campaign event¶
A JSON object that determines when and where an intervention is distributed during a campaign.
- campaign file¶
A property of the simulation (for example, “Parasite Prevalence”) that is accumulated once per simulated time step and written to file, typically as an array of the accumulated values.
- class factory¶
A function that instantiate objects at run-time and use information from JSON-based configuration information in the creation of these objects.
- configuration file¶
In computing, a core refers to an independent central processing unit (CPU) in the computer. Multi-core computers have more than one CPU. However, through technologies such as Hyper- Threading Technology (HTT or HT), a single physical core can actually act like two virtual or logical cores, and appear to the operating system as two processors.
- demographics file¶
- disease-specific build¶
A build of the EMOD executable (Eradication.exe) built using SCons without any dynamic link libraries (DLLs).
Microsoft’s implementation of a shared library, separate from the EMOD executable (Eradication.exe), that can be dynamically loaded (and unloaded when unneeded) at runtime. This loading can be explicit or implicit.
A modular component of EMOD that are consumed and used by the EMOD executable (Eradication.exe). Under Windows, a EMODule is implemented as a dynamic link library (DLL) and, under CentOS, EMODules are currently not supported. EMODules are primarily custom reporters.
- Epidemiological MODeling software (EMOD)¶
The modeling software from the Institute for Disease Modeling (IDM) for disease researchers and developers to investigate disease dynamics, and to assist in combating a host of infectious diseases. You may see this referred to as Disease Transmission Kernel (DTK) in the source code.
Typical (default) name for the EMOD executable (Eradication.exe), whether built using monolithic build or modular (EMODule-enabled) build.
- event coordinator¶
A JSON object that determines who will receive a particular intervention during a campaign.
- flattened file¶
A single campaign or configuration file created by combining a default file with one or more overlay files. Multiple files must be flattened prior to running a simulation. Configuration files are flattened to a single-depth JSON file without nesting, the format required for consumption by the EMOD executable (Eradication.exe). Separating the parameters into multiple files is primarily used for testing and experimentation.
- Heterogeneous Intra-Node Transmission (HINT)¶
A feature for modeling person-to-person transmission of diseases in heterogeneous population segments within a node for generic simulations.
- high-performance computing (HPC)¶
The use of parallel processing for running advanced applications efficiently, reliably, and quickly.
- individual properties¶
Labels that can be applied to individuals within a simulation and used to configure heterogeneous transmission, target interventions, and move individuals through a health care cascade.
- input files¶
The JSON and binary files used as inputs to an EMOD simulation. The primary input files are the JSON-formatted configuration, demographics, and campaign files. They may also include the binary files for migration, climate, population serialization, or load- balancing.
- inset chart¶
The default JSON output report for EMOD that includes multiple channels that contain data at each time step of the simulation. These channels include number of new infections, prevalence, number of recovered, and more.
An object aimed at reducing the spread of a disease that is distributed either to an individual; such as a vaccine, drug, or bednet; or to a node; such as a larvicide. Additionally, initial disease outbreaks and intermediate interventions that schedule another intervention are implemented as interventions in the campaign file.
A human-readable, open standard, text-based file format for data interchange. It is typically used to represent simple data structures and associative arrays, and is language-independent. For more information, see https://www.json.org.
- Keyhole Markup Language (KML)¶
A file format used to display geographic data in an Earth browser, for example, Google Maps. The format uses an XML-based structure (tag-based structure with nested elements and attributes). Tags are case-sensitive.
- Link-Time Code Generation (LTCG)¶
A method for the linker to optimize code (for size and/or speed) after compilation has occurred. The compiled code is turned not into actual code, but instead into an intermediate language form (IL, but not to be confused with .NET IL which has a different purpose). The LTCG then, unlike the compiler, can see the whole body of code in all object files and be able to optimize the result more effectively.
- Message Passing Interface (MPI)¶
An interface used to pass information between computing cores in parallel simulations. One example is the migration of individuals from one geographic location to another within EMOD simulations.
A type of “miniature model” that operates within the framework of EMOD to compute a particular set of parameters. Each microsolver, in effect, is creating a microsimulation in order to accurately capture the dynamics of that particular aspect of the model.
- monolithic build¶
A single EMOD executable (Eradication.exe) with no DLLs that includes all components as part of Eradication.exe itself. You can still use EMODules with the monolithic build; for example, a custom reporter is a common type of EMODule. View the documentation on EMODules and emodules_map.json for more information about creation and use of EMODules.
- Monte Carlo method¶
A class of algorithms using repeated random sampling to obtain numerical results. Monte Carlo simulations create probability distributions for possible outcomes, which provides a more realistic way of describing uncertainty.
A grid size that is used for modeling geographies. Within EMOD, a node is a geographic region containing simulated individuals. Individuals migrate between nodes either temporarily or permanently using mobility patterns driven by local, regional, and long- distance transportation.
- node properties¶
Labels that can be applied to nodes within a simulation and used to target interventions based on geography.
- node-targeted intervention¶
An intervention that is distributed to a geographical node rather than to a single individual. One example is larvicides, which affect all mosquitoes living and feeding within a given node.
- output report¶
A file that is the output from an EMOD simulation. Output reports are in JSON, CSV, or binary file format. You must pass the data from an output report to graphing software if you want to visualize the output of a simulation.
- overlay file¶
An additional configuration, campaign, or demographic file that overrides the default parameter values in the primary file. Separating the parameters into multiple files is primarily used for testing a nd experimentation. In the case of configuration and campaign files, the files can use an arbitrary hierarchical structure to organize parameters into logical groups. Configuration and campaign files must be flattened into a single file before running a simulation.
Software that undergoes a shorter testing cycle in order to make it available more quickly. Previews may contain software defects that could result in unexpected behavior. Use EMOD previews at your own discretion.
- regression test¶
A test to verify that existing EMOD functionality works with new updates, typically used to refer to one of a set of scenarios included in the EMOD bundle and located in the Regression subdirectory of the bundle. Directory names of each subdirectory in Regression describe the main regression attributes, for example, “1_Generic_Seattle_MultiNode”. Also can refer to the process of regression testing of software.
Software that includes new functionality, scientific tutorials leveraging new or existing functionality, and/or bug fixes that have been thoroughly tested so that any defects have been fixed before release. EMOD releases undergo full regression testing.
Functionality that extracts simulation data, aggregates it, and saves it as an output report. EMOD provides several built-in reporters for outputting data from simulations and you also have the ability to create a custom reporter.
A collection of input files that describes a real-world example of a disease outbreak and interventions. Many scenarios are included with EMOD source installations or are available to download at EMODScenarios to learn more about epidemiology and disease modeling.
A text or JSON file that can be generated from the EMOD executable (Eradication.exe) that defines all configuration and campaign parameters.
An execution of the EMOD software using an associated set of input files.
- simulation type¶
The disease or disease class to model.
EMOD supports the following simulation types for modeling a variety of diseases:
Generic disease (GENERIC_SIM), which can be used for modeling a variety of diseases such as influenza or measles
Vector-borne diseases (VECTOR_SIM), which can be used for modeling vector-borne diseases such as dengue
Malaria (MALARIA_SIM), which adds features specific to malaria biology and treatment
Tuberculosis with HIV coinfection (TBHIV_SIM), which can be used for modeling TB transmission, with the option to add HIV coinfection as a contributing factor
Sexually transmitted infections (STI_SIM), which adds features for sexual relationship networks
HIV (HIV_SIM), which adds features specific to HIV biology and treatment
Environmental transmission (ENVIRONMENTAL_SIM), which adds features for diseases transmitted through contaminated food or water
Typhoid (TYPHOID_SIM), which adds features specific to typhoid biology and treatment
Solvers are used to find computational solutions to problems. In simulations, they can be used, for example, to determine the time of the next simulation step, or to compute the states of a model at particular time steps.
- Standard Template Library (STL)¶
A library that contains a set of common C++ classes (including generic algorithms and data structures) that are independent of container and implemented as templates, which enables compile-time polymorphism (often more efficient than run-time polymorphism). For more information and discussion of STL, see Wikipedia.
- state transition event¶
A change in state (e.g. healthy to infected, undiagnosed to positive diagnosis, or birth) that may trigger a subsequent action, often an intervention. “Campaign events” should not be confused with state transition events.
- time step¶
A discrete number of hours or days in which the “simulation states” of all “simulation objects” (interventions, infections, immune systems, or individuals) are updated in a simulation. Each time step will complete processing before launching the next one. For example, a time step would process the migration data for populations moving between nodes via rail, airline, and road. The migration of individuals between nodes is the last step of the time step after updating states.
A set of instructions in the documentation to learn more about epidemiology and disease modeling. Tutorials are based on real-world scenarios and demonstrate the mechanics of the the model. Each tutorial consists of one or more scenarios.
- working directory¶
The directory that contains the configuration and campaign files for a simulation. You must be in this directory when you invoke Eradication.exe at the command line to run a simulation.
The following terms are used to describe processes, concepts, and the files, features, and functionality related to using COMPS.
- asset collection¶
Collection of user created input files, such as demographics, temperature, weather, and overlay files. These files are stored in COMPS and can be available for use by other users.
The COMPS dashboard provides an overview of computing cluster usage, including current and queued jobs. Resource management is simple due to the job-priority system used by the platform.
Logical grouping of simulations. This allows for managing numerous simulations as a single unit or grouping.
COMPS provides powerful charting functionality to visualize the output channels for simulations. A chart can include output for a single simulation or for multiple simulations. Viewing multiple simulations in a single chart (multi-chart) provides a fast, flexible way to filter simulations to view only data of interest.
Logical grouping of experiments. This allows for managing multiple experiments as a single unit or grouping.
- work item¶
Work item is used to build experiments and suites. It builds a set of simulations or groups of simulations, such as creating parameter sweeps. Work item defines how many simulations run at the start of the experiment to determine if the configuration settings are functional.
- work order¶
JSON formatted file used for the creation of a work item, in combination with a configuration file, and (optional) campaign and additional files.
The following terms are used to describe general concepts and processes in the field of epidemiology and disease modeling.
A blood protein produced in response to and counteracting a specific antigen. Antibodies combine chemically with substances that the body recognizes as foreign (eg. bacteria, viruses, or other substances).
A substance that is capable of inducing a specific immune response and that evokes the production of one or more antibodies.
- Clausius-Clayperon relation¶
A way of characterizing a transition between two phases of matter; provides a method to find a relationship between temperature and pressure along phase boundaries. Frequently used in meteorology and climatology to describe the behavior of water vapor. See Wikipedia - Clausius-Clayperon relation for more information.
- compartmental model¶
A disease model that divides the population into a number of compartments that represent different disease states, such as susceptible, infected, or recovered. Every person in a compartment is considered identical. Many compartmental models are deterministic, but some are stochastic.
Characterized by the output being fully determined by the parameter values and the initial conditions. Given the same inputs, a deterministic model will always produce the same output.
- diffusive migration¶
The diffusion of people in and out of nearby nodes by foot travel.
- disability-adjusted life years (DALY)¶
The number of years of life lost due to premature mortality plus the years lost due to disability while infected. Used to quantify the burden of disease.
An outbreak of an infectious disease, such that a greater number of individuals than normal has the disease. Epidemics have very high R0 (Recall R0>1 for a disease to spread) and are often associated with acute, highly transmissible pathogens that can be directly transmitted. Further, pathogens with lower infectious periods create more explosive epidemics. To control epidemics, it is necessary to reduce R0. This can be done by:
Decreasing the number of susceptibles (by vaccination, for example).
Decreasing the mean number of contacts or the transmissibility, such as by improving sanitation, or limiting the number of interactions sick people have with healthy people.
Reducing the length of the infectious period.
The portion of an antigen that the immune system recognizes. An epitope is also called an antigenic determinant.
- Euler method¶
Used in mathematics and computational science, this method is a first-order numerical procedure for solving ordinary differential equations with a given initial value.
The exponential function, \(e^x\), where \(e\) is the number (approximately 2.718281828) such that the function \(e^x\) is its own derivative. The exponential function is used to model a relationship in which a constant change in the independent variable gives the same proportional change (i.e. percentage increase or decrease) in the dependent variable. The function is often written as \(exp(x)\). The graph of \(y = exp(x)\) is upward-sloping and increases faster as \(x\) increases.
Individual who has been infected with a pathogen, but due to the pathogen’s incubation period, is not yet infectious.
- force of infection (FoI)¶
A measure of the degree to which an infected individual can spread infection; the per-capita rate at which susceptibles contract infection. Typically increases with transmissibility and prevalence of infection.
- herd immunity¶
The resistance to the spread of a contagious disease within a population that results if a sufficiently high proportion of individuals are immune to the disease, especially through vaccination. The portion of the population that needs to be immunized in order to achieve herd immunity is P > 1 – (1/ R0), where P = proportion vaccinated * vaccine efficacy.
Unable to become infected/infectious, whether through vaccination or having the disease in the past.
The rate of new cases of a disease during a specified time period. This is a measure of the risk of contracting a disease.
Individual who is infected with a pathogen and is capable of transmitting the pathogen to others.
- Koppen-Geiger Climate Classification System¶
A system based on the concept that native vegetation is a good expression of climate. Thus, climate zone boundaries have been selected with vegetation distribution in mind. It combines average annual and monthly temperatures and precipitation, and the seasonality of precipitation. EMOD has several options for configuring the climate, namely air temperature, rainfall, and humidity.
One option utilizes input files that associate geographic nodes with Koppen climate indices. The modified Koppen classification uses three letters to divide the world into five major climate regions (A, B, C, D, and E) based on average annual precipitation, average monthly precipitation, and average monthly temperature. Each category is further divided into sub-categories based on temperature and precipitation. While the Koppen system does not take such things as temperature extremes, average cloud cover, number of days with sunshine, or wind into account, it is a good representation of our earth’s climate.
- loss to follow-up (LTFU)¶
Patients who at one point were actively participating in disease treatment or clinical research, but have become lost either by error or by becoming unreachable at the point of follow-up.
See loss to follow-up.
- ordinary differential equation (ODE)¶
A differential equation containing one or more functions of one independent variable and its derivatives.
The rate of all cases of a disease during a specified time period. This is a measure of how widespread a disease is.
Individual who is either no longer infectious, or “removed” from the population.
- reproductive number¶
In a fully susceptible population, the basic reproductive number R0 is the number of secondary infections generated by the first infectious individual over the course of the infectious period. R0=S*L* \(\beta\) (where S = the number of susceptible hosts, L = length of infection, and \(\beta\) = transmissibility). When R0> 1, disease will spread. It is essentially a measure of the expected or average outcome of transmission. The effective reproductive number takes into account non-susceptible individuals. This is the threshold parameter used to determine whether or not an epidemic will occur, and determines:
The initial rate of increase of an epidemic (the exponential growth phase).
The final size of an epidemic (what fraction of susceptibles will be infected).
The endemic equilibrium fraction of susceptibles in a population (1/ R0).
The critical vaccination threshold, which is equal to 1-(1/ R0), and determines the number of people that must be vaccinated to prevent the spread of a pathogen.
- routine immunization (RI)¶
The standard practice of vaccinating the majority of susceptible people in a population against vaccine-preventable diseases.
- SEIR model¶
A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, exposed, infectious, and recovered.
- SEIRS model¶
A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, exposed, infectious, recovered, and susceptible.
- SI model¶
A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible and infectious.
- simulation burn-in¶
A modeling concept in which a simulation runs for a period of time before reaching a steady state and the output during that period is not used for predictions. This concept is borrowed from the electronics industry where the first items produced by a manufacturing process are discarded.
- SIR model¶
A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, infectious, and recovered.
- SIRS model¶
A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, infectious, recovered, and susceptible.
- SIS model¶
A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, infectious, and susceptible.
Characterized by having a random probability distribution that may be analyzed statistically but not predicted precisely.
- stochastic die-out¶
When an disease outbreak ends, despite having an effective R0 above 1, due to randomness. A deterministic model cannot estimate the probability of stochastic die- out, but a stochastic model can.
When an individual is infected but asymptomatic, so the infection is not readily detectable.
- supplemental immunization activity (SIA)¶
In contrast to routine immunization (RI), SIAs are large-scale operations with a goal of delivering vaccines to every household.
Individual who is able to become infected.
- transmissibility (\(\beta\))¶
Also known as the effective contact rate, is the product of the contact rate and the probability of transmission per contact.
The capacity of a pathogen to produce disease. It is proportional to parasitemia, or the number of circulating copies of the pathogen in the host. The higher the virulence (given contact between S and I individuals), the more likely transmission is to occur. However, higher virulence means contact may be less likely as infected hosts show more symptoms of the disease. There is a trade-off that occurs between high transmissibility and disease- induced mortality.
- WAIFW matrix¶
A matrix of values that describes the rate of transmission between different population groups. WAIFW is an abbreviation for Who Acquires Infection From Whom.
- Weibull distribution¶
A probability distribution often used in EMOD and that requires both a shape parameter and a scale parameter. The shape parameter governs the shape of the density function. When the shape parameter is equal to 1, it is an exponential distribution. For shape parameters above 1, it forms a unimodal (hump-shaped) density function. As the shape parameter becomes large, the function forms a sharp peak. The inverse of the shape parameter is sometimes referred to here as the “heterogeneity” of the distribution (heterogeneity = 1/shape), because it can be helpful to think about the degree of heterogeneity of draws from the distribution, especially for hump-shaped functions with heterogeneity values between 0 and 1 (i.e., shape parameters greater than 1). The scale parameter shifts the distribution from left to right. When heterogeneity is small (i.e., the shape parameter is large), the scale parameter sets the location of the sharp peak.