Simulating City-level Airborne Infectious Diseases

📝 Abstract

With the exponential growth in the world population and the constant increase in human mobility, the danger of outbreaks of epidemics is rising. Especially in high density urban areas such as public transport and transfer points, where people come in close proximity of each other, we observe a dramatic increase in the transmission of airborne viruses and related pathogens. It is essential to have a good understanding of the `transmission highways’ in such areas, in order to prevent or to predict the spreading of infectious diseases. The approach we take is to combine as much information as is possible, from all relevant sources and integrate this in a simulation environment that allows for scenario testing and decision support. In this paper we lay out a novel approach to study Urban Airborne Disease spreading by combining traffic information, with geo-spatial data, infection dynamics and spreading characteristics.

💡 Analysis

With the exponential growth in the world population and the constant increase in human mobility, the danger of outbreaks of epidemics is rising. Especially in high density urban areas such as public transport and transfer points, where people come in close proximity of each other, we observe a dramatic increase in the transmission of airborne viruses and related pathogens. It is essential to have a good understanding of the `transmission highways’ in such areas, in order to prevent or to predict the spreading of infectious diseases. The approach we take is to combine as much information as is possible, from all relevant sources and integrate this in a simulation environment that allows for scenario testing and decision support. In this paper we lay out a novel approach to study Urban Airborne Disease spreading by combining traffic information, with geo-spatial data, infection dynamics and spreading characteristics.

📄 Content

Simulating City-level Airborne Infectious Dis- eases Shan Mei1,2,∗, Xuan Zhou1, Yifan Zhu1, Zhenghu Zu3, Tao Zheng3, A.V. Boukhanovsky4, P.M.A Sloot2,4,5 1National University of Defense Technology, P. R. China 2Computational Science, University of Amsterdam, Amsterdam, Nether- lands 3Beijing Institute of Biotechnology, Academy of Military Medical Science, P. R. China 4National Research University ITMO, Russia 5Nanyang Technological University, Singapore ∗Corresponding author. Telephone: (+)86-731-84573558, Fax: (+)86-731- 84573535. Email addresses: SM: Meishan.ann@gmail.com XZ: qiangjunxingguo@163.com YZ: stephen.zhuyifan@gamil.com ZZ: zzhbiot08@126.com TZ: zhengtao@126.com AVB: avb mail@mail.ru PMAS: P.M.A.Sloot@uva.nl 1 arXiv:1201.0160v1 [cs.OH] 30 Dec 2011 Abstract With the exponential growth in the world population and the constant increase in human mobility, the danger of outbreaks of epidemics is raising. Especially in high density urban areas such as public transport and transfer points, where people come in close proximity of each other, we observe a dramatic increase in the transmission of airborne viruses and related pathogens. It is essential to have a good understanding of the ‘transmission highways’ in such areas, in order to prevent or to predict the spreading of infectious diseases. The approach we take is to combine as much information as is possible, from all relevant sources and integrate this in a simulation environment that allows for scenario testing and decision support. In this paper we lay out a novel approach to study Urban Airborne Disease spreading by combining traffic information, with geo-spatial data, infection dynamics and spreading characteristics. Keywords: Geographical Information System (GIS), Multi-Agent Systems (MAS), Infectious Diseases, Epidemics

1. Introduction City-level airborne epidemics is a threat to healthy living. With the ex- ponential growth in the world population and the constant increase in human mobility, the danger of outbreaks of epidemics is raising. For example, the novel Influenza A (H1N1), also known as Human Swine Influenza/Swine Flu, spreading internationally from Mexico in 2009, has caused a serious epidemic in China. China is highly susceptible to pandemic influenza A (H1N1) due to its big population and high residential density. According to the Ministry of Health of China, until 30th Sep 2009, the provinces in China mainland had reported 19589 confirmed cases, 14348 cured cases, 10 sever cases and a few death cases (Ministry of Health of China, 2009). Preprint submitted to Computers, Environment and Urban Systems May 22, 2018 In high density urban areas such as public transport and transfer points, where people come in close proximity of each other, we observe a dramatic in- crease in the transmission of airborne viruses and related pathogens. In order to elaborately model and simulate the airborne epidemics, the city under study needs to be modeled in detail from the infrastructural aspect. We utilize the Geographic Information System (GIS) technology to model the infrastructure of a city which might be threatened by certain epidemic attacks. GIS is a com- bination of database management capabilities for collecting and storing large amounts of geospatial data, together with spatial analysis capabilities to in- vestigate geospatial relationships among the entities represented by that data, plus map display capabilities to portray the geospatial relationships in two- and three-dimensional map form (Nyerges et al., 2009). GIS facilitates storing, querying and visualizing city infrastructure including roads, regions with diverse functionality, public transportation and so forth. We also address path routing based on city transportation to capture transmissions that occur to localities, especially public transport. This is because in many developing countries such as China, the overly crowded public transportation usually escalates airborne epidemics. On the basis of the geo-spatial information, we model a local population that dwell in a city under study and their spatio-dynamical behavior. There is growing recognition that the solutions to the most vexing public health problems are likely to be those that embrace the behavioral and social sciences as key players (Mabry et al., 2008). Human behavior plays an important role in the spread of infectious diseases, and understanding the influence of behavior on the spread of diseases can be key to improving control efforts (Funk et al., 2010). It is essential to have a good understanding of the ‘transmission highways’ in urban areas, in order to prevent or to predict the spreading of infectious diseases. Therefore, investigating into the patterns that are relevant for social contacts, and consequent airborne virus transmissions, is of great importance. In this study lay out a novel approach to study Urban Airborne Disease spreading by combining traffic information, with geo-spatial data, infection dy- 3 namic

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