emod-common — This package is a disease-specific distribution of the EMOD binary for the Generic (common to all sim types) simulation type.

EMOD

Epidemiological MODeling software (EMOD), is an agent-based model (ABM) that simulates 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 by using decision rules. These models have strong predictive power and are able to leverage spatial and temporal dynamics.

EMOD is also stochastic, meaning that there is randomness built into the model. Infection and recovery processes are represented as probabilistic Bernoulli random draws. In other words, when a susceptible person comes into contact with a pathogen, they are not guaranteed to become infected. Instead, you can imagine flipping a coin that has a λ chance of coming up tails S(t) times, and for every person who gets a “head” you say they are infected. This randomness better approximates what happens in reality. It also means that you must run many simulations to determine the probability of particular outcomes.

As of V2.22, EMOD will only support malaria and HIV and will no longer support diseases such as TB and Typhoid.

Project status

EMOD-Hub projects are provided as open source software under the MIT License for community use, research, and development.

Unless otherwise noted, these projects are no longer actively maintained or supported by IDM or the Gates Foundation.

Community contributions are welcome, and trusted collaborators may review and merge pull requests, but no guarantees are made regarding support, pull request review, security response, maintenance, or release timelines.

Running EMOD

Most users should interact with EMOD through the disease-specific Python workflow packages rather than directly through the C++ model source code.

These packages simplify configuring simulations, running experiments across different execution environments, and analyzing results.

• emodpy-malaria
• emodpy-hiv

The C++ source code in this repository is primarily intended for users interested in understanding the internal implementation of the model or modifying the core simulation engine.

Source Code Installation for Development

The following link provides instructions for installing the prerequisites required to build and run EMOD. This intended for code development and not doing research.

https://emod.idmod.org/EMOD/dev-install-overview/

Developer Documentation

For more information about modifying the disease model of EMOD, please see:

https://emod.idmod.org/EMOD/

Directory Structure

• baseReportLib - A library of commonly used report components and base classes.
• cajun - A C++ API for JSON
• campaign - A library of commonly used intervention components and base classes.
• componentTests - A collection of unit tests that verify that the EMOD pieces do the right thing.
• Dependencies - Microsoft Cluster Pack
• docs - Source files for documentation about how to modify the EMOD source code.
• Eradication - The core components of EMOD including human intra-host, relationship, and vector models.
• interventions - A collection of interventions that can be used with EMOD.
• libsqlite - The SQLite source code for reading and creating SQLite databases.
• lz4 - A fast compression engine used to read and write serialized populations.
• rapidjson - A fast JSON parser/generator for C++ with box SAX/DOM style API
• Regression - A collection of scripts, input data, and output data used to verify that EMOD models things correctly.
• reporters - A collection of data extraction, or report, classes used to collect data during a simulation.
• Scripts - A collection of support scripts
• snappy - A fast compression engine used to read and write serialized populations.
• UnitTest++ - A C++ unit test framework used by the componentTests
• untils - A collection of utility classes to do things like help with configuring and generating pseudo random numbers.

If wanting to navigate through the code, the place to start is Eradication\Eradication.cpp.

More information on the EMOD Architecture can be found at:

https://emod.idmod.org/EMOD/dev-architecture-overview/

History & Publication Samples

EMOD development was started by Philip Welkoff in 2010 to model malaria. Since that time, EMOD has been used in numerous studies and policy decisions. Below is short sample of papers about EMOD and that used EMOD:

A malaria transmission-directed model of mosquito life cycle and ecology

• Philip A Eckhoff
• Malaria Journal, 2011
• https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-10-303

Description of the EMOD-HIV Model v0.7

• Anna Bershteyn, Daniel J. Klein, Edward Wenger, and Philip A. Eckhoff
• arXiv.org, 2012
• https://arxiv.org/pdf/1206.3720

Effectiveness of reactive case detection for malaria elimination in three archetypical transmission settings: a modelling study

• Jaline Gerardin,Caitlin A. Bever, Daniel Bridenbecker, Busiku Hamainza, Kafula Silumbe, John M. Miller, Thomas P. Eisele, Philip A. Eckoff, and Edward A. Wenger
• Malaria Journal, 2017
• https://malariajournal.biomedcentral.com/articles/10.1186/s12936-017-1903-z

Implementation and applications of EMOD, an individual-based multi-disease modeling platform

• Anna Bershteyn, Jaline Gerardin, Daniel Bridenbecker, Christopher W Lorton, Jonathan Bloedow, Robert S Baker, Guillaume Chabot-Couture, Ye Chen, Thomas Fischle, Kurt Frey, Jillian S Gauld, Hao Hu, Amanda S Izzo, Daniel J Klein, Dejan Lukacevic, Kevin A McCarthy, Joel C Miller, Andre Lin Ouedraogo, T Alex Perkins, Jeffrey Steinkraus, Tony Ting, Quirine A ten Bosch, Hung-Fu Ting, Svetlana Titova, Bradley G Wagner, Philip A Welkhoff, Edward A Wenger, Christian N Wiswell
• Pathogens and Disease, 2018
• https://academic.oup.com/femspd/article/76/5/fty059/5050059?login=false

Vector genetics, insecticide resistance and gene drives: an agent-based modeling approach to evaluate malaria transmission and elimination

• Prashanth Selvaraj, Edward A. Wenger, Daniel Bridenbecker, Nikolai Windbichler, Jonathan R. Russell , Jaline Gerardin, Caitlin A. Bever, Milen Nikolov
• BioRxiv, 2020
• https://www.biorxiv.org/content/10.1101/2020.01.27.920421v1.full

The effect of 90-90-90 on HIV-1 incidence and mortality in eSwatini: a mathematical modelling study

• Adam Akullian , Michelle Morrison, Geoffrey P Garnett, Zandile Mnisi, Nomthandazo Lukhele, Daniel Bridenbecker, Anna Bershteyn
• The Lancet HIV, 2020
• https://www.thelancet.com/action/showPdf?pii=S2352-3018%2819%2930436-9

Community

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Contributing

If you have feature requests, issues, or new code, please see our CONTRIBUTING page for how to provide your feedback.