Simulating city-level airborne infectious diseases

With the exponential growth in the world population and the constant increase in human mobility, the possible impact of outbreaks of epidemics on cities is increasing, especially in high-density urban areas such as public transportation and transfer points. The volume and proximity of people in these areas can lead to an observed dramatic increase in the transmission of airborne viruses and related pathogens. Due to the critical role these areas play in transmission, it is vital that we have a comprehensive understanding of the ‘transmission highways’ in these areas to predict or prevent the spreading of infectious diseases in general. The principled approach of this paper is to combine and utilize as much information as possible from relevant sources and to integrate these data in a simulated environment that allows for scenario testing and decision support. In this paper, we describe a novel approach to study the spread of airborne diseases in cities by combining traffic information with geo-spatial data, infection dynamics and spreading characteristics. The system is currently being used in an attempt to understand the outbreak of influenza in densely populated cities in China.

[1]  P. Atkinson,et al.  Individual space–time activity-based modelling of infectious disease transmission within a city , 2008, Journal of The Royal Society Interface.

[2]  S. Eubank,et al.  If smallpox strikes Portland.... , 2005, Scientific American.

[3]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[4]  Francis Heylighen,et al.  The Science of Self-Organization and Adaptivity , 1999 .

[5]  Herbert W. Hethcote,et al.  Mixing patterns between age groups in social networks , 2007, Soc. Networks.

[6]  V. Jansen,et al.  Modelling the influence of human behaviour on the spread of infectious diseases: a review , 2010, Journal of The Royal Society Interface.

[7]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[8]  J. M. Riese,et al.  Semi-empirical power-law scaling of new infection rate to model epidemic dynamics with inhomogeneous mixing. , 2006, Mathematical biosciences.

[9]  Rick Quax,et al.  Increasing risk behaviour can outweigh the benefits of antiretroviral drug treatment on the HIV incidence among men-having-sex-with-men in Amsterdam , 2010, BMC infectious diseases.

[10]  Peter M. A. Sloot,et al.  Quantitatively evaluating interventions in the influenza A (H1N1) epidemic on China campus grounded on individual-based simulations , 2010, ICCS.

[11]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[12]  Patric Nilsson,et al.  An individual-based network model to evaluate interventions for controlling pneumococcal transmission , 2008, BMC infectious diseases.

[13]  Chandra R. Bhat,et al.  A comprehensive daily activity-travel generation model system for workers , 2000 .

[14]  A. Crofts,et al.  Structure and function of the -complex of , 1992 .

[15]  J Koopman.  Models of Infectious Disease Agent Study , 2006 .

[16]  Matthew J. Roorda,et al.  Validation of TASHA: A 24-Hour Activity Scheduling Microsimulation Model , 2008 .

[17]  Catherine H Mercer,et al.  Scale-Free Networks and Sexually Transmitted Diseases: A Description of Observed Patterns of Sexual Contacts in Britain and Zimbabwe , 2004, Sexually transmitted diseases.

[18]  Ling Bian,et al.  Spatial–temporal transmission of influenza and its health risks in an urbanized area , 2010, Computers, Environment and Urban Systems.

[19]  William N. Reynolds,et al.  A General Framework for Representing Behavior in Agent Based Modeling , 2000 .

[20]  Yifan Zhu,et al.  Complex agent networks explaining the HIV epidemic among homosexual men in Amsterdam , 2008, Math. Comput. Simul..

[21]  Amy H Auchincloss,et al.  A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. , 2008, American journal of epidemiology.

[22]  Timothy L. Nyerges,et al.  GIS and Society , 2009 .

[23]  Xiong He,et al.  [Quantitative evaluation on the effectiveness of prevention and control measures against pandemic influenza A (H1N1) in Beijing, 2009]. , 2010, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.

[24]  P. Coveney,et al.  HIV decision support: from molecule to man , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  P. Mabry,et al.  Interdisciplinarity and systems science to improve population health: a view from the NIH Office of Behavioral and Social Sciences Research. , 2008, American journal of preventive medicine.

[26]  Peter M. A. Sloot,et al.  International Journal of Computer Mathematics Stochastic Simulation of Hiv Population Dynamics through Complex Network Modelling Stochastic Simulation of Hiv Population Dynamics through Complex Network Modelling , 2022 .

[27]  Michael G. McNally,et al.  A Microsimulation of Daily Activity Patterns , 2000 .

[28]  G. Chowell,et al.  Comparative estimation of the reproduction number for pandemic influenza from daily case notification data , 2007, Journal of The Royal Society Interface.

[29]  Chee Keong Kwoh,et al.  Evaluating Temporal Factors in Combined Interventions of Workforce Shift and School Closure for Mitigating the Spread of Influenza , 2012, PloS one.