An open-data-driven agent-based model to simulate infectious disease outbreaks.
Agent-based models are a tool that can be used to better understand the dynamics of an infectious disease outbreak. An infectious disease outbreak is influenced by many factors including vaccination or immunity levels, population density, and the age structure of the population. We hypothesize that...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0208775 |
| _version_ | 1819016815059140608 |
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| author | Elizabeth Hunter Brian Mac Namee John Kelleher |
| author_facet | Elizabeth Hunter Brian Mac Namee John Kelleher |
| author_sort | Elizabeth Hunter |
| collection | DOAJ |
| description | Agent-based models are a tool that can be used to better understand the dynamics of an infectious disease outbreak. An infectious disease outbreak is influenced by many factors including vaccination or immunity levels, population density, and the age structure of the population. We hypothesize that these factors along with interactions of factors and the actions of individuals would lead to outbreaks of different size and severity even in two towns that appear similar on paper. Thus, it is necessary to implement a model that is able to capture these interactions and the actions of individuals. Using openly available data we create a data-driven agent-based model to simulate the spread of an airborne infectious disease in an Irish town. Agent-based models have been known to produce results that include the emergence of patterns and behaviours that are not directly programmed into the model. Our model is tested by simulating an outbreak of measles that occurred in Schull, Ireland in 2012. We simulate the same outbreak in 33 different towns and look at the correlations between the model results and the town characteristics (population, area, vaccination rates, age structure) to determine if the results of the model are affected by interactions of those town characteristics and the decisions on the agents in the model. As expected our results show that the outbreaks are not strongly correlated with any of the main characteristics of the towns and thus the model is most likely capturing such interactions and the agent-based model is successful in capturing the differences in the outbreaks. |
| first_indexed | 2024-12-21T02:53:37Z |
| format | Article |
| id | doaj.art-f464c31203b142308a2c895445cf29f2 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T02:53:37Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f464c31203b142308a2c895445cf29f22022-12-21T19:18:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011312e020877510.1371/journal.pone.0208775An open-data-driven agent-based model to simulate infectious disease outbreaks.Elizabeth HunterBrian Mac NameeJohn KelleherAgent-based models are a tool that can be used to better understand the dynamics of an infectious disease outbreak. An infectious disease outbreak is influenced by many factors including vaccination or immunity levels, population density, and the age structure of the population. We hypothesize that these factors along with interactions of factors and the actions of individuals would lead to outbreaks of different size and severity even in two towns that appear similar on paper. Thus, it is necessary to implement a model that is able to capture these interactions and the actions of individuals. Using openly available data we create a data-driven agent-based model to simulate the spread of an airborne infectious disease in an Irish town. Agent-based models have been known to produce results that include the emergence of patterns and behaviours that are not directly programmed into the model. Our model is tested by simulating an outbreak of measles that occurred in Schull, Ireland in 2012. We simulate the same outbreak in 33 different towns and look at the correlations between the model results and the town characteristics (population, area, vaccination rates, age structure) to determine if the results of the model are affected by interactions of those town characteristics and the decisions on the agents in the model. As expected our results show that the outbreaks are not strongly correlated with any of the main characteristics of the towns and thus the model is most likely capturing such interactions and the agent-based model is successful in capturing the differences in the outbreaks.https://doi.org/10.1371/journal.pone.0208775 |
| spellingShingle | Elizabeth Hunter Brian Mac Namee John Kelleher An open-data-driven agent-based model to simulate infectious disease outbreaks. PLoS ONE |
| title | An open-data-driven agent-based model to simulate infectious disease outbreaks. |
| title_full | An open-data-driven agent-based model to simulate infectious disease outbreaks. |
| title_fullStr | An open-data-driven agent-based model to simulate infectious disease outbreaks. |
| title_full_unstemmed | An open-data-driven agent-based model to simulate infectious disease outbreaks. |
| title_short | An open-data-driven agent-based model to simulate infectious disease outbreaks. |
| title_sort | open data driven agent based model to simulate infectious disease outbreaks |
| url | https://doi.org/10.1371/journal.pone.0208775 |
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