Mohammad S. Hashemian
Dylan L. Knowles
ABSTRACT
Contagions - either pathogens spread through contact networks or societal memes spread through social networks - impact the occurrence and character of both epidemic and endemic diseases. While computational models explore disease parameters in the context of a given contact network, these models are always subject to the caveat that reality may not be consistent with the simplified assumptions regarding contact, contagion or network structure. More - and more accurate - data on the contact dynamics between people and places could alleviate some uncertainties, and make models more robust tools for policy-makers and researchers. Properly applied, consumer electronics can serve as a valuable source of this data. Using smartphones as sensor platforms rather than personal communications devices, it is possible to record high fidelity information on a participant's location, activity level, and contacts between both people and places. This paper describes the design, architecture and a preliminary deployment of a general smartphone-based epidemiological data collection system. The dataset, gathered over one month, contains over 45 million records related to the behavioral patterns of 39 participants. We provide an initial analysis of aggregate level statistics to demonstrate the power and scope of the technique for capturing relevant data. Demonstrating the potential for such data to inform decision-making, we further perform an agent-based simulation of a flu-like illness that uses the dataset to capture aspects of both person-person and environmental (place-person) transmission. We demonstrate that the data collection is possible, valuable, and scalable and that the data can be leveraged to inform detailed models capturing more complex physical interactions than were previously feasible.
- Anderson, R. M. and May, R. M. 1991. Infectious diseases of humans : dynamics and control. Oxford; New York: Oxford University PressGoogle Scholar
- Brauer, F., van den Driessche, P., and Wu, J. 2008. Spatial Structure: Partial Differential Equations Models Mathematical Epidemiology 1945 (2008), 191--203.Google Scholar
- Chunhua, O. and Jianhong, W. 2006. Spatial Spread of Rabies Revisited: Influence of Age Dependent Diffusion on Nonlinear Dynamics. SIAM J. Appl. Math. 67:1 (Nov. 2006) 138--163.Google Scholar
- Codeco, C. 2001. Endemic and epidemic dynamics of cholera: the role of the aquatic reservoir. BMC Infectious Diseases, 1:1 (Feb. 2001), 1.Google ScholarCross Ref
- Dibble, C., Wendel, S., and Carle, K. 2007. Simulating pandemic influenza risks of US cities. Proceedings of the 39th Winter Simulation Conference, 1548--1550. Google ScholarDigital Library
- Eagle, N. and Pentland, A. 2006. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, Volume 10 Issue 4, 255--268. Google ScholarDigital Library
- Eames K. T. D. and Keeling M. J. 2003. Contact tracing and disease control. Proceedings of the Royal Society B: Biological Sciences, 270 (Dec. 2003), 2565--2571.Google Scholar
- Fall, K. 2003. A delay-tolerant network architecture for challenged internets, Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications, August 25-29, 2003, Karlsruhe, Germany, 27--34. Google ScholarDigital Library
- FluWatch, Public Health Agency of Canada, viewed 29 June 2011 http://origin.phac-aspc.gc.ca/fluwatch/09-10/w34_10/index-eng.php.Google Scholar
- Hagenaars, T. J., Donnelly, C. A., Ferguson, N. M., and Anderson, R. M. 2000. The transmission dynamics of the aetiological agent of scrapie in a sheep flock. Mathematical Biosciences, 168:2 (Dec. 2000), 117--135.Google ScholarCross Ref
- Hashemian, M., Stanley, K., and Osgood, N. 2010. Flunet: Automated tracking of contacts during flu season. In Proc. of The 6th Intl. workshop on Wireless Network Measurements, (Avignon, France, May 31, 2010), 348--353.Google Scholar
- Hashemian, M., Stanley, K. G., 2011. Effective Utilization of Place as a Resource in Pocket Switched Networks, to be appeared in 36th IEEE Conference on Local Computer Networks, (Bonn, Germany, October 4-7, 2011). Google ScholarDigital Library
- Hethcote, H. W. and Yorke J. A. 1984. Gonorrhea transmission dynamics and control. Springer Lecture Notes in Biomathematics. Berlin, Springer.Google Scholar
- Hui, P., Chaintreau, A., Scott, J., Gass, R., Crowcroft, J. and Diot, C. 2005. Pocket switched networks and human mobility in conference environments, In Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking, 244--251, August 26, 2005, Philadelphia, USA. Google ScholarDigital Library
- Ionides, E. L., Breto, C., and King, A. A. 2006. Inference for nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 103:49 (Dec. 2006), 18438--18443.Google ScholarCross Ref
- Kermack, W. O. and A. G. McKendrick. 1927. A Contribution to the Mathematical Theory of Epidemics. Proc. R. Soc. Lond. A 115: 772 (Aug. 1927), 700--721.Google ScholarCross Ref
- Klovdahl, A., Graviss, E., Yaganehdoost, A., Ross, M., Wanger, A., Adams, G., et al. (2001). Networks and tuberculosis: an undetected community outbrea involving public places. Soc. Sci. Med., 52 (Mar. 2001), 681--694.Google Scholar
- Latkin, C., Mandell, W., Vlahov, D., Oziemkowska, M., and Celentano, D. 1996. People and places: behavioral settings and personal network characteristics as correlates of needle sharing. J. Acquir. Immune Defic. Syndr. Hum. Retrovirol., 13 (Nov. 1996), 273--280.Google Scholar
- Lee, K., Hong, S., Kim, S. J., Rhee, I., and Chong, S. 2009. SLAW: A Mobility Model for Human Walks. In Proceedings of INFOCOM, (Rio de Janeiro, Brazil, April 19-25, 2009), 855--863. DOI=10.1109/INFCOM.2009.5061995Google Scholar
- Madan, A., Cebrian, M., Lazer, D. and Pentland, A. 2010. Social Sensing for Epidemiological Behavior Change, In Proceedings of the 12th ACM Intl. conference on Ubiquitous computing(Copenhagen, Denmark, Sept. 26-29, 2010), 291--300. Google ScholarDigital Library
- McBryde, E. S. and McElwain, D. L. S. 2006. A Mathematical Model Investigating the Impact of an Environmental Reservoir on the Prevalence and Control of Vancomycin-Resistant Enterococci. Journal of Infectious Diseases, 193:10 (May 2006), 1473--1474.Google ScholarCross Ref
- Miller, M. W., Hobbs, N. T., and Tavener, S. J. 2006. Dynamics of prion disease transmission in mule deer. Ecological Applications, 16:6 (Dec. 2006), 2208--2214.Google ScholarCross Ref
- Morris, M. and Kretzschmar, M. 1995 Concurrent Partnerships and transmission dynamics in networks. Social Networks, 17:3-4 (Oct. 1995), 299--318.Google ScholarCross Ref
- Read, J. M., Eames, K. T. D., and Edmunds, W. J. 2008. Dynamic social networks and the implications for the spread of infectious disease. Journal of The Royal Society Interface, 5 (Sept. 2008), 1001--1007.Google ScholarCross Ref
- Rohani, P., Breban, R., Stallknecht, D. E., and Drake, J. M. 2009. Environmental transmission of low pathogenicity avian influenza viruses and its implications for pathogen invasion. In Proceedings of the National Academy of Sciences, 106:25, 10365--10369.Google ScholarCross Ref
- Salathe, M., Kazandjieva, M., Lee, J. W., Levis, P., Feldman, M. W., and Jones, J. H. 2010. A High-Resolution Human Contact Network for Infectious Disease Transmission. In Proc. of National Academy of Science, 107:51 (Dec. 2010).Google ScholarCross Ref
- Schneeberger, A., Mercer, C. H., Gregson, S. A. J., Ferguson, N. M., Nyamukapa, C. A., Anderson, R. M., et al. 2004. Scale-Free Networks and Sexually Transmitted Diseases: A Description of Observed Patterns of Sexual Contacts in Britain and Zimbabwe. Sexually transmitted diseases, 31:6 (Jun. 2004), 380--387.Google Scholar
- Small T. and Hass A. 2005. Resource and Performance trade-offs in delay-tolerant wireless networks. In Proc. of the 2005 ACM SIGCOMM workshop on Delay-Tolerant Networking, (Philadelphia, USA, August 22-26, 2005), 260--267. Google ScholarDigital Library
- Smieszek, T. 2009. A mechanistic model of infection: Why duration and intensity of contacts should be included in models of disease spread. Theoretical Biology and Medical Modelling (Nov. 2009), 6--25.Google Scholar
- Song, C., Qu, Z., Blumm, N., and Barabási, A. 2010. Limits of Predictability in Human Mobility. Science 327(5968), 1018--1021, DOI=10.1126/science.1177170.Google ScholarCross Ref
- Stanley, K. G. and Osgood N. D. 2011. The Potential of Sensor-Based Monitoring as a Health Care, Health Promotion, and Research Tool. Editorial in Annals of Family Medicine. vol. 9, 296--298.Google Scholar
- Tuite, A. R., Greer, A. L., Whelan, M., Winter, A., Lee, B., Yan, P., Wu, J., Moghadas, S., Buckeridge, D., Pourbohloul, B. and Fisman, D. N. 2010. Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza. CMAJ, 182:2 (Dec. 2009), 131--136.Google ScholarCross Ref
- Viboud, C., Bjørnstad, O. N., Smith, D. L., Simonsen, L., Miller, M. A., and Grenfell, B. T. 2006. Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza. Science, 312:5772 (Apr. 2006) 447--451.Google ScholarCross Ref
- Weber, T. P. and Stilianakis, N. I. 2008. Inactivation of influenza A viruses in the environment and modes of transmission: A critical review. Journal of Infection, 57:5 (Oct. 2008), 361--373.Google ScholarCross Ref
- Wylie, J. L., Shah, L., and Jolly, A. 2007. Incorporating geographic settings into a social network analysis of injection drug use and bloodborne pathogen prevalence. Health & Place, 13:3 (Sept. 2007), 617--628.Google ScholarCross Ref
Index Terms
- Human network data collection in the wild: the epidemiological utility of micro-contact and location data
Recommendations
Epidemiological modeling of news and rumors on Twitter
SNAKDD '13: Proceedings of the 7th Workshop on Social Network Mining and AnalysisCharacterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and ...
An application of epidemiological modeling to information diffusion
SBP'10: Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and PredictionMessages often spread within a population through unofficial - particularly web-based - media. Such ideas have been termed “memes.” To impede the flow of terrorist messages and to promote counter messages within a population, intelligence analysts must ...
Modeling epidemic spread with spike-based models
ICONS 2020: International Conference on Neuromorphic Systems 2020The Susceptible-Infected-Recovered/Removed model is a standard model for epidemiological spread of disease through vulnerable populations. In this paper we show how SIR network dynamics can be implemented using spiking neurons.
Comments