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Details of Grant 

EPSRC Reference: EP/H003959/1
Title: Data Driven Network Modelling for Epidemiology in Dynamic Human Networks
Principal Investigator: Yoneki, Dr E
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Laboratory
Organisation: University of Cambridge
Scheme: Career Acceleration Fellowship
Starts: 31 March 2010 Ends: 30 March 2015 Value (£): 488,208
EPSRC Research Topic Classifications:
Networks & Distributed Systems Social Stats., Comp. & Methods
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
01 Jul 2009 Fellowships 2009 Final Allocation Panel Announced
08 Jun 2009 Fellowships 2009 Interview - Panel E Deferred
Summary on Grant Application Form
My research will develop data-driven modelling of human interaction dynamics, where experimental measurements are followed by mathematical modelling. I emphasise that real-world data needs to drive modelling. Such refined modelling will predict potential disease outbreaks and enables building synthetic networks, which will provide opportunities to scale up the network environment and experimentally control epidemics. I aim to build such a prediction system. Realising this vision will involve both sophisticated data collection and model construction. Especially data collection takes an important role. Current popular detection mechanisms using WiFi access points or short range radio involve high failures, communication protocol limitation and complex statistics. Without in-depth understanding of data collection mechanisms, modelling such networks will not be reliable. The derived epidemic models will need to be accurate and parameterised with data on human interaction patterns, modularity, and details of time dependent activity. Thus, a model can determine epidemic spread accordingly, and synthetic networks can be constructed. Data collection will also require careful attention on ethics and privacy issues. The epidemic spread of infectious disease remains a serious threat, both in the UK and around the world. The proposed research aims at understanding human interactions in the real world using wireless technology to develop advanced modelling of epidemic spread. This research will provide unique insight into medical and statistical problems, ultimately contributing to diminish control epidemic spread at the public health level. Infectious disease epidemics are analogous to wireless computer epidemics, especially when computer devices (e.g. mobile phones) are carried by people, as both types of epidemics rely on human interactions. I propose to advance research in both infectious disease epidemiology and in wireless computer epidemiology in two ways. First, I will aid our understanding of social networks by extending and developing analysis and modelling approaches with empirical real-world human connectivity data. Second, I will establish high quality data collection by investigating effective approaches using multiple hardware and communication mechanisms. The proposed research will provide an advanced model of epidemic spread of infectious disease, and the results will highlight solutions to medical and statistical issues which could not be addressed before. The outcome of this effort will provide more accurate and improved predictions of infectious disease epidemics.The quantitative understanding of human interactions is complex and has not been explored at any depth. Theoretical modelling and simulation based approaches are limited, and rich real-world data will be key to refine the modelling. Current models in network theory are too simplified, and multiple large-scale experimental data are needed both for modelling and building systems. The recent emergence of wireless technology provides a unique opportunity to collect precise human connectivity data. For example, people can carry tiny wireless sensors (<1 cm^2) that record dynamic information about other devices nearby. A post-facto analysis of this data will yield valuable insight into complex human interactions, which in turn will support meaningful modelling of understanding networks. Specific individuals can be identified who act as coalescing hubs at different points in space and time and who influence data flow. By neutralising such hubs, we can prevent the spread of viruses. The developed prediction system in the proposed research can be used in various ways. Sexually transmitted diseases are on the increase: the AIDS epidemic of the past two decades is a prime example of a situation that could be stopped by the prediction system before reaching the fatal stage.
Key Findings
It has been 2.8 years since the project ‘Data Driven Network Modelling for Epidemiology in Dynamic Human Networks (DDEPI)’ (2010-2015) has started. I have demonstrated the FluPhone project in 2010 (http://www.cl.cam.ac.uk/research/srg/netos/fluphone2/), which aims at collection of human proximity information from the general population for building time dependent contact networks. The data collection along the symptom data from infectious disease is built over the framework of the Haggle project (http://www.haggleproject.org/ 2006-2010). The FluPhone project has shown another dimension of human contact networks study. The project has been reported in many news media including University of Cambridge Press (http://www.cam.ac.uk/research/features/fluphone-disease-tracking-by-app), BBC (http://www.bbc.co.uk/news/uk-england-cambridgeshire-13281131), Times newspaper, and others.

I am currently extending the vision of FluPhone project to the EpiMap project for a system of opportunistic networks combined with satellite communication, designed to face the challenges posed by weak power and communications infrastructure in the rural regions of developing countries in Asia, Africa and South America. I will use a delay-tolerant small satellite for data transfer between developing countries and Europe and North America. Data collected through EpiMap can be used to help design more efficient vaccination strategies and equitable control programmes.

Moreover, the project is being extended towards human behavioural study. Individuals may change their behaviour for several reasons: through being ill themselves, having to care for others who are ill, or through changing their normal habits in the belief it will prevent or minimise their risk of infection. A recent study suggests that public transport usage may decline in the event of an influenza pandemic and that people may stay at home rather than go into work and risk infection. If such precautionary behaviour were to be adopted by a large number of individuals the economic implications may be profound.

Potential use in non-academic contexts
The project will be used to develop improved mathematical models for the spread of infectious diseases, such as measles, tuberculosis and pneumococcal diseases in developing countries in Africa and Asia. These diseases are vaccine preventable and there has been a significant investment in improving vaccine coverage and introduction of new vaccines in some of the poorest countries. Funding for vaccination programmes is limited and many countries face difficult decisions to refine the effective vaccination strategies within the limited budget.
Impacts
Description BBC, Times, various online web sites interviewed me on the FluPhone project (e.g. http://www.bbc.co.uk/news/uk-england-cambridgeshire-13281131).
Summary BBC web site on 'FluPhone app 'helps track spread of infectious diseases' http://www.bbc.co.uk/news/uk-england-cambridgeshire-13281131 got the highesst hit of the web page on that week in the world.
Date Materialised 4 May 2011
Sectors submitted by the Researcher
Economy; Healthcare; Information & Communication Technologies; Other; Social Diversity
Project URL: http://www.cl.cam.ac.uk/research/srg/netos/fluphone2/
Further Information:  
Organisation Website: http://www.cam.ac.uk