Increasing opportunities have been provided to collect massive social signals from varieties of human activity records than ever before, such as micro-blogging, information sharing, and geo-tagged activity logs in online social media platforms, with the help of technological advances in social sensing. The Disease Networks and Mobility (DiNeMo) project focuses on using these signals and their associated geotags to infer the time and place of likely disease outbreaks, and to map disease risk across the country.
As specific examples, the figures below show the nation-wide detections of influenza like illnesses in Australia detected from Twitter (top), and the probability of an average individual to be at a point in 2-D space around their centre of gravity (bottom), based on their geo-tagged tweets.
M. Kim, D. Paini, R. Jurdak, Real-world diffusion dynamics based on point process approaches: a review, Artificial Intelligence Review, September, 2018. pdf
M. Shahzamal, R. Jurdak. B. Mans, F. De Hoog, “A Graph Model with Indirect Co-location Links,” In proceedings of the 14th International Workshop on Mining and Learning with Graphs, co-located with KDD, London, UK, August, 2018.
M. Shahzamal, R. Jurdak, B. Mans, A. El Shoghri, F. De Hoog, ” Impacts of Indirect Contacts in Emerging Infectious Diseases on Social Networks,” In proceedings of Big Data Analysis for Social Computing @ PAKDD, Melbourne, Australia, June, 2018.
M. Shahzamal, R. Jurdak, R. Arablouei, M. Kim, K. Thilakarathna, B. Mans, “Airborne Disease Propagation on Large Scale Social Contact Network,” In proceedings of the Second International Workshop on Social Sensing (SocialSens), as part of CPSWeek, Pittsburg, USA, April, 2017.
M. Kim, R. Jurdak, “Heterogeneous Social Signals Capturing Real-world Diffusion Processes,” In proceedings of the Second International Workshop on Social Sensing (SocialSens), as part of CPSWeek, Pittsburg, USA, April, 2017.
K. Zhang, R. Arablouei, and R. Jurdak, “Predicting Prevalence of Influenza-Like Illness in Australia From Geo-Tagged Tweets,” In proceedings of the 1st International Workshop on Social Computing (IWSC), as part of the 26th International World Wide Web (WWW) Conference, Perth, Australia, April, 2017.
S. Khan, N. Bergmann, R. Jurdak and B. Kusy, “Mobility in Cities: Comparative Analysis of Mobility Models Using Geo-tagged Tweets in Australia,” In proceedings of the IEEE 2nd International Conference on Big Data Analysis (ICBDA), Bejing, China, March, 2017.
K. Zhao and R. Jurdak, “Understanding the spatiotemporal pattern of grazing cattle movement,” Nature Scientific Reports, 6:31967 EP, August 2016.
Thomas B, Jurdak R, Zhao K, Atkinson I (2016) Diffusion in Colocation Contact Networks: The Impact of Nodal Spatiotemporal Dynamics. PLoS ONE 11(8): e0152624. doi:10.1371/journal. pone.0152624
R. Jurdak, K. Zhao, J. Liu, M. AbouJaoude, M. Cameron, D. Newth, “Understanding Human Mobility from Twitter,” PLOS ONE, 10(7): e0131469. doi:10.1371/journal.pone.0131469. July, 2015.
J. Liu, K. Zhao, S. Khan, M. Cameron, R. Jurdak, “Multi-scale Population and Mobility Estimation with Geo-tagged Tweets,” In proceedings of 31st IEEE International Conference on Data Engineering (ICDE) Workshop BioBAD 2015, Seoul Korea, April 2015.
M. Trad, R. Jurdak, and R. Rana, “Guiding Ebola Patients to Suitable Health Facilities: An SMS-based Approach,” F1000 Research 4:43, February 2015.