Abstract
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.
Highlights
With the emergence of new pathogens, such as SARS and MERS; the resurgence of diseases thought to be near elimination, such as measles and mumps; recent widespread epidemics of deadly diseases, such as Ebola; and the threat of pandemics from swine flu, and avian flu; it is essential to be able to model and understand the spread of an infectious disease
In this paper we have presented an approach to creating an open data-driven Agent-based models (ABMs) for human infectious disease epidemiology
An agent-based model for infectious disease outbreaks modelling parameters, such as probability of infection and exposure time, the model can be adjusted to simulate the spread of any infectious disease that follows the SEIR person to person transmission dynamics
Summary
With the emergence of new pathogens, such as SARS and MERS; the resurgence of diseases thought to be near elimination, such as measles and mumps; recent widespread epidemics of deadly diseases, such as Ebola; and the threat of pandemics from swine flu, and avian flu; it is essential to be able to model and understand the spread of an infectious disease. We propose using an agent-based model to simulate the spread of an airborne infectious diseases in Irish towns. This paper presents a data-driven agent-based model to simulate infectious disease in Irish towns. An agent-based model for infectious disease outbreaks use in the experiments is outlined using the ODD protocol [25] in the Appendix 1 of the paper and can be found online in the Netlogo User Community Model Library Agents are added to the town based on the population data described in the “Population Statistics” section, adults are added first and given an age and sex based on the distributions of age and sex in the appropriate small area from the census data. This is a naive transportation model, for small towns, such as those described in the paper, where distances travelled are short it is a reasonable simplification
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