Epigrass: a tool to study disease spread in complex networks.
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- Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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This manuscript is a preprint. A published version is available at:
10.1186/1751-0473-3-3 (Peer Reviewed) Published in Source Code for Biology and Medicine 2008, 3:3- Document Type:
- Manuscript
- Date:
- Received 06 July 2007 16:25 UTC; Posted 06 July 2007
- Subjects:
- Bioinformatics
- Abstract:
The construction of complex statial simulation models such as those used in network epidemiology, is a daunting task due to the large amount of data involved in their parameterization. Such data, which frequently resides on large geo-referenced databases, has to be processed and assigned to the various components of the model. All this just to construct the model, then it still has to be simulated and analyzed under different epidemiological scenarios. This workflow can only be achieved efficiently by computational tools that can automate most if not all these time-consuming tasks. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior.
A Network epidemiological model representing the spread of a directly transmitted disease through a bus-transportation network connecting mid-size cities in Brazil. Results show that the topological context of the starting point of the epidemic is of great importance from both control and preventive perspectives.
Epigrass is shown to facilitate greatly the construction, simulation and analysis of complex network models. The output of model results in standard GIS file formats facilitate the post-processing and analysis of results by means of sophisticated GIS software.
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- This document is licensed to the public under the Creative Commons Attribution 2.5 License
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Coelho, Flávio Codeço, Codeco, Claudia, and Cruz, Oswaldo. Epigrass: a tool to study disease spread in complex networks.. Available from Nature Precedings <http://hdl.handle.net/10101/npre.2007.378.1> (2007)
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Published version:
10.1186/1751-0473-3-3 (Peer Reviewed) Published in Source Code for Biology and Medicine 2008, 3:3 -
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Mario Pineda-Krch on 31 August 2007 00:27 UTC
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Recently a preprint manuscript was posted on the Nature Precedings site entitled Epigrass: a tool to study disease spread in complex networks by Flávio Codeço Coelho et al. (Flávio also happens to have the blog Python in Science). Epigrass is avaliable for download at sourceforge.net.
The preprint describes Epigrass as a simulation software for network epidemiological models (simulation and analysis),
It enables researchers to perform comprehensive spatio-temporal simulations incorporating epidemiological data and models for disease transmission and control in order to create complex scenario analyses. Epigrass is designed towards facilitating the construction and simulation of large scale metapopulational models.
One interesting feature of the model is that it incorporates the transportation delay of passengers between the cities. Because the length of of the bus routes are known it is possible to estimate…
the delay associated with the duration of each bus trip. The delay $latex \delta$ was calculated as the number of days (rounded down) that a bus, traveling at an average speed of 60km/h, would take to complete a given trip.
This of course makes total sense and they way it is implemented is quite neat in its simplicity. I have never seen transportation delay being used in this type of tactical models before. It is something worth thinking about for the national FMD model that I am involved in at CADMS (see post What am I up to at CADMS?).
The manuscript provides an example of a network model using Epigrass to simulate the spread of a directly transmitted disease through a
bus-transportation network connecting mid-size cities in Brazil. I am curious about how this model performs for something a bit larger, say for studying the outbreak dynamics of some livestock disease across the contact network between all the livestock premises in the US. We are talking 2 million+ premises, each having it’s own internal epidemiological dynamics, and where livestock shipments routes (the edges connecting the nodes) criss cross all of the country.