DiNeMo: Disease Networks and Mobility

December 5th, 2019


The Disease Networks and Mobility (DiNeMo) Project explores how human infectious diseases found overseas might spread in Australia and overseas, and how these movements can be predicted. The project looks at the patterns of how people move both internationally and domestically in order to forecast the risk of the spread of disease.

Using travel data from the International Air Transportation Association, as well as dengue incidence rates from the Global Health Data Exchange, the spread of dengue can be understood and predicted, allowing countries including Australia to develop plans to protect the country against the increasing risk of infectious diseases.

This work adds to existing DiNeMo research, which used dengue outbreaks in Queensland as a case study to determine how the disease transfers between people. You can read more about this prior work here: [Link to news article with older content]


Australia has historically had low risk of importation and establishment of many infectious diseases, due to its geographic remoteness and separation from the rest of the world. However, significant increases in the volume of people and goods entering Australia has also increased the risk of infectious diseases being imported.

Dengue is endemic in more than 100 countries. While it is not established in Australia, some areas of the country host mosquitoes capable of spreading the disease, which can lead to local outbreaks from imported cases, and in the extreme, potential establishment of the disease. The disease is also under-reported, with a recent study finding that 92 percent of infections world-wide are not reported to authorities, mainly due to misdiagnosis or lack of awareness; it is not known how under-reported Australian cases are.

A key challenge in managing this risk has been the lack of sufficient data to pinpoint these risks and accurately forecast them to guide proactive actions. The DiNeMo project focuses on multiple data sources which describe people movement patterns to determine the risk of diseases coming into the country, where and when outbreaks may happen, and where they may spread to.


Using travel data from the International Air Transportation Association, alongside dengue incidence rates from the Global Health Data Exchange, we have developed a model to predict dengue importation globally and into Australia.

The model allows for estimates to be made on how many infections can be imported into a country per month, based on travel behaviour. In particular, it is capable of determining the infections’ country of origin, and uncovering the routes along which it is most likely to spread.

The model’s predictions, shown below for international airports in August 2015, estimate the number of dengue infected people arriving at any airport based on international travel patterns, as well as the incidence rate and seasonality of dengue at the origin of travel.

The light shaded countries, including Australia, denote countries that are non-endemic, while the dark shaded countries are countries where dengue is already established. The blue circles correspond to the ten airports with the highest number of dengue importations; the area of a node increases with the number of dengue cases imported through the corresponding airport.

global map

The light shaded countries, including Australia, denote countries that are non-endemic, while the dark shaded countries are countries where dengue is already established.

As an example of its prediction abilities, the tool has identified the travel route from Puerto Rico to Florida as having the highest predicted volume of dengue-infected passengers travelling to a non-endemic zone.

The model offers information critical for public health authorities, who can better prepare against the spread of dengue, and allow authorities to effectively surveil airports with the highest risk of receiving infected passengers, and thereby minimise the spread of the disease.

The model can also be applied to other mosquito-borne diseases, such as malaria and Zika virus, and expands on previous work which modelled how dengue might spread in Australia from overseas.

Our Team

Media Coverage

Using tweets and tourist travel data to predict the next dengue outbreak, Computer World, February, 2019.

Australian researchers reveal tool to track spread of dengue and other infectious diseases, Straits Times, February, 2019.

Data61 builds new disease tracker, InnovationAus, February, 2019.

Data61 using machine learning to track human infectious diseases in Australia, ZDNET, February, 2019.

CSIRO researchers build tools to map and predict infectious disease outbreaks, news.com.au, February, 2019.

Related Publications

M. Kim, D. Paini, R. Jurdak, Modeling stochastic processes in disease spread across a heterogeneous social system, Proceedings of the National Academy of Sciences, 116 (2) 401-406, 2019. pdf

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, A. Elfes, B. Kusy, A. Tews, W. Hu, E. Hernandez, N. Kottege, P. Sikka, ”Autonomous Surveillance for Biosecurity”, Trends in Biotechnology, 33(4):201-207, April, 2015.

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.


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