Computer Laboratory Systems Research Group

Project DDEPI: Data Driven Network Modelling for Epidemiology

Project DDEPI












 Digital Epidemiology: Modelling of Epidemic Spread in Africa using Human Mobility Data

Respiratory and other close-contact infectious diseases, such as TB, measles and pneumonia, are major killers in much of the developing world. Mathematical models are essential for understanding how these diseases spread and for identifying how best to control them. Although central to the models, few quantitative data are available on relevant contact patterns, and no study to measure these factors has yet been attempted in rural Africa. This situation is particularly problematic given the high burden of infectious diseases in the developing world and the resource-driven requirements for optimally targeted interventions. The recent population shift in Africa and elsewhere from rural to urban areas adds additional challenges for the understanding of complex social networks. How people behave and interact during a large outbreak of an infectious disease directly impacts not only the spread of infection, but also the efficacy of control strategies, and it can also have wide-reaching economic implications. We will develop mathematical models based on these new data, which will help us gain valuable insight into the spread and control of diseases. Examples of diseases to be modelled are tuberculosis, pneumococcal disease, meningococcal disease, measles, and disease associated with Haemophilus influenza.  

Improved knowledge of relevant contact patterns will hugely help the understanding of social and spatial patterns of infection spread. The recent emergence of wireless technologies (e.g., mobile-phones and sensors) makes it possible to collect real-world data on human connectivity along with environmental and contextual information. Capturing human interactions with such devices will provide empirical, quantitative measurements of societal mixing patterns to underpin mathematical models of the spread of close-contact diseases. The use of sensors and mobile-phones for these purposes has distinct advantages over other methods of collecting contact data (such as diaries and interviews), because they can gather proximity data automatically, allowing detailed longitudinal with no possibilities of re-call bias, no barriers due to problems of literacy or understanding, and minimal disruption to the participants of the survey. Such approaches therefore offer an unparalleled and timely opportunity to collect information on social contact patterns that would allow a step-change in our understanding of the patterns of disease spread. The use of sensors and mobile-phones for this purpose has not been explored for this application before. We plan to develop a human mobility study framework for low cost RFID sensors and mobile-phones. We will use radio technology and Bluetooth communication for detecting devices within radio proximity. In a rural environment, simple RFID sensors will be used together with low-cost computers as readers (e.g., Raspberry PI), which can be deployed outdoor without any power or communication infrastructure. We will integrate minimum satellite communication. Mobile-phones may be used more in urban environments, along with sensing movement, light, and humidity. Various sensors embedded in the phone will capture contextual changes in the environment to infer behavioural patterns of the phone carriers. The dynamic network of connections between participants will be used to investigate the topology of the social network, including: 1) duration-weighted pairs – time spent in close-proximity is a powerful determinant of infection risk and these can be considered as a weighted link between individuals with location and context associations; 2) number of encounters per person – are some individuals responsible for a disproportionate number of contacts? 3) social distances – betweenness and centrality measures describe how far apart individuals are in a network, and strongly impact disease dynamics; 4) community structure – identify individuals that form bridging links between otherwise distinct groups offers efficient targeted interventions.  

Mathematical and computational models of social networks and epidemic spread, and methods to analyse the collected data are critical for public health and epidemiology. Advances in computing and large-scale data mining now create fresh opportunities to support a new generation of epidemiology, i.e. Digital Epidemiology.

2013-2014 Summer Projects

1. Measuring Mobility and Interaction of People using Active RFID tag

In this project we would deploy the active RFID tags and readers in the research lab or hospital to measure the mobility of people and interaction of them. The RFID tags are from OpenBeacon platform ( and the task of the project is building up sensing platform, which has accurate data collection to meet the experimental criteria. Additionally you can challenge to build a light weight RFID tag reader using Raspberry Pi together with OpenBeacon USB reader.

This project is an extended version from the FluPhone project, which was reported BBC at  

2. Building a light weight RFID tag reader platform using Raspberry Pi by integrating OpenBeacon RFID USB reader

In the umbrella project we would deploy the active RFID tags and readers in the research lab or hospital to measure the mobility of people and interaction of them. The RFID tags are from OpenBeacon platform ( and the task of this project is building communication platform using OpenBeacon USB stick reader ( by integrating to Raspberry Pi. It provides a similar function as OpenBeacon Ethernet EasyReader PoE II does. If time allows, a testbed of the sensing platform using built readers can be deployed for experimenting accuracy of data collection.

 Source Code Repository

  • tbd.

 Related Publications

  • E. Yoneki: RasPiNET: Decentralised Communication and Sensing Platform with Satellite Connectivity.  ACM CHANTS, September, 2014 (PDF).
  • H. Kim, K. Beznosov and E. Yoneki: Finding Influential Neighbors to Maximize Information Diffusion in Twitter.  WWW - SIMPLEX, Seoul, April, 2014 (PDF).
  • D. Fay, J. Kunegis, and E. Yoneki Centrality and Mode Detection in Dynamic Contact Graphs; a Joint Diagonalisation Approach.  IEEE/ACM ASONAM, Niagara Falls, Canada, Canada, August, 2013 (PDF). 
  • E. Yoneki and J. Crowcroft: EpiMap: Towards Quantifying Contact Networks for Understanding Epidemiology in Developing Countries.  Elsevier Ad Hoc Networks Journal: Special Issue on Wireless Technology for Humanitarian Relief, Vol. 13, pp. 83-93, 2014 (online version 2012) (PDF).
  • A. Martin-Campillo, J. Crowcroft, E. Yoneki, and Ramon Marti  Evaluating Opportunistic Networks in Disaster Scenarios.  Journal of Network and Computer Applications, Elsevier, 2012 (PDF).
  • E. Yoneki, I. Baltopoulos and J. Crowcroft D^3N: Programming Distributed Computation in Pocket Switched Networks.  ACM MOBIHELD at SIGCOMM, 2009  (PDF).
  • D. Murray, E. Yoneki, J. Crowcroft and S. Hand  The Case for Crowd Computing.  ACM MOBIHELD at SIGCOMM,  2010  (PDF).
  • A. Martin-Campillo, E. Yoneki, J. Crowcroft, and Ramon Marti  Electronic Triage Tag and Opportunistic Networks in Disasters.  CoNext Special Workshop on the Internet and Disasters, December, 2011 (PDF).


  • Abraham Martin (former PostDoc)

  • Jack Coady (2014 SUmmer Intern)

  • University of Cambridge Physics Department

  • Eiko Yoneki

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