Data Driven Network Modelling for Epidemiology in Dynamic Human Networks
Lead Research Organisation:
University of Cambridge
Department Name: Computer Science and Technology
Abstract
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.
Organisations
- University of Cambridge (Lead Research Organisation)
- Swiss Federal Institute of Technology in Lausanne (EPFL) (Collaboration)
- Ifakara Health Institute (Collaboration)
- London School of Hygiene and Tropical Medicine (LSHTM) (Collaboration)
- Illuminum Greenhouses (Collaboration)
- Microsoft Research (Collaboration)
- Federal University of Amazonas (Collaboration)
- Intel (United States) (Collaboration)
People |
ORCID iD |
Eiko Yoneki (Principal Investigator) |
Publications
Basu P
(2013)
Guest Editorial: Network science
in IEEE Journal on Selected Areas in Communications
Boutet A
(2013)
What's in Twitter, I know what parties are popular and who you are supporting now!
in Social Network Analysis and Mining
Dalibard V
(2017)
BOAT
Dalibard Valentin
(2016)
Tuning the Scheduling of Distributed Stochastic Gradient Descent with Bayesian Optimization
in arXiv e-prints
Eiko Yoneki (Author)
(2012)
On Joint Diagonalization for Dynamic Network Analysis
Eiko Yoneki (Author)
(2012)
Mitigating I/O latency in SSD-based graph traversal
Eiko Yoneki (Author)
(2012)
A Unified Graph Query Layer for Multiple Databases
Description | The project 'Data Driven Network Modelling for Epidemiology in Dynamic Human Networks (DDEPI)' (2010-2015) has developed the FluPhone project in 2010 (http://www.cl.cam.ac.uk/research/srg/netos/fluphone2/), which aims at collecting human proximity information from the general population for building time dependent contact networks. Data collection for symptoms 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. In 2013-2014, the project evolved to understand infectious diseases in Africa. To build decentralised Raspberry Pi networks together with RFID tags, we have designed the Raspberry Pi OpenBeacon Reader networks. This network makes it possible to collect human mobility data, where the Internet access is not deployed, such as in rural areas in Africa. Obtaining mobility data at schools is valuable, as these ore usually mixing places of different families and interaction data can be used for understanding the spread of infectious diseases. In 2014-15, the EpiMap project has been explored for a system of opportunistic networks combined with satellite communication, designed to face the challenges posed by weak power and communications infrastructure in 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. In 2015, unique experiments of human connectivity measurement and communication networking has been deployed. RFID tags and GPS are used to measure human proximity in the Brazilian Amazon rural region, without constant power supply or Internet. Small river communities are fragmented and the only transportation is by boat. This information will be used to develop improved mathematical models of the spread of infectious diseases. The modelling is complemented by a survey to understand the living conditions in the relevant rural communities. The outcome of the project will be potentially used to help design more efficient vaccination strategies and equitable control programmes. In order to realise the above data collection of remote sensing, delay tolerant networks using Raspberry Pi will be deployed. Together with human contact data collection, daily based updated web information will be delivered to remote communities using an advanced web server caching mechanism. Moreover, the project is being extended towards two directions as follows: 1) Human behavioural study: Individuals may change their behaviour for several reasons: by being ill themselves, having to care for others who are ill, or by 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 to work and risk infection. If such precautionary behaviours were to be adopted by a large number of individuals, the economic implications may be profound. 2) Efficient large-scale networked data processing: This requires heavy data processing resources when the scale of the data increases and the novel data processing for the graph data using even with single laptop has been investigated. |
Exploitation Route | 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. We are planning to experiment our research prototype in developing countries in Africa (e.g. Malawi) to demonstrate what we can be modelled using the collected social contact data. It is our intention to develop a range of mathematical models based on contact patterns, which can be used to help guide vaccine policy development over the coming decade in Africa and other developing countries. |
Sectors | Agriculture Food and Drink Communities and Social Services/Policy Digital/Communication/Information Technologies (including Software) Energy Environment Healthcare Other |
URL | http://www.cl.cam.ac.uk/research/srg/netos/DDEPI/ |
Description | BBC, Times, and various online websites interviewed me about the FluPhone project (e.g. http://www.bbc.co.uk/news/uk-england-cambridgeshire-13281131). The BBC website on FluPhone app 'helps track spread of infectious diseases (http://www.bbc.co.uk/news/uk-england-cambridgeshire-13281131) received the highest number of hits on that week in the world. Beneficiaries: Public audience, Government, Health agencies, etc. The research contributed to a large extent to establish the topic of 'Digital Epidemiology'. The project demonstrated the ability of measuring human proximity and building contact networks using inexpensive devices such as mobile phones, RFID tags, Raspberry Pi, and GPS. This approach especially benefits developing countries, where such information is most needed, e.g. for efficient vaccination strategies with limited resources. Additionally, the project provides internet accessibility in rural remote regions, e.g. for useful information related to health and potentially building a remote diagnosis system. Since 2012, the outcomes of the project have been extended in various ways as exemplified below. The direction of research has been well integrated into the GCRF funding scheme. 1) Modelling for TB and other Airborne Diseases by monitoring human contact networks as well as air flow in Tanzania, where households of TB patients to estimate the variations of carbon dioxide levels in the house, and social contact patterns (associated risks of TB infection). 2) A smart greenhouse monitoring solution, which uses image recognition to readily detect pest and disease infestations and alert the farmers. The underlying technology was extended from the EPSRC project (EP/H003959/1). We have established a strong and long-term collaboration with IHI in Bagamoyo, the head quarter of IHI in Africa, with several branch institutes throughout the continent. We have been involved in projects by postgraduate students in the African Institute of Mathematical Science (AIMS), where we supervised on data analytics project and were able to publish a paper to a conference at Brain Analysis using Connectivity Networks (MICCAI Conference on Medical Image Computing and Computer Assisted Intervention). This specific project is more geared towards analytical methodology on the brain data. In future, the engagement of postgraduate students in AIMS to TB project will be expected and that will enhance the project scope in future to benefit from their inputs and connections throughout Africa. In 2020, the outbreak of the coronavirus might trigger re-calibration of the work we achieved in this project, and currently we are re-assessing our findings and potential contributions to measure/prevent the spread of the diseases. |
First Year Of Impact | 2012 |
Sector | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Healthcare |
Impact Types | Cultural |
Description | Espot: Early Detection of Pest and Disease in Greenhouses in Africa |
Amount | £80,000 (GBP) |
Organisation | University of Cambridge |
Sector | Academic/University |
Country | United Kingdom |
Start | 03/2019 |
End | 08/2019 |
Description | GLOBAL CHALLENGES RESEARCH FUND |
Amount | £50,000 (GBP) |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 05/2016 |
End | 03/2017 |
Description | GLOBAL CHALLENGES RESEARCH FUND |
Amount | £32,000 (GBP) |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2017 |
End | 03/2018 |
Description | RAEng Frontiers of Development |
Amount | £20,000 (GBP) |
Organisation | Royal Academy of Engineering |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 02/2018 |
End | 02/2019 |
Title | Data Mule in Amazon |
Description | Human connectivity patterns in rural area in Brazilian Amazon |
Type Of Material | Database/Collection of data |
Provided To Others? | No |
Impact | Rare information of human connection network in rural and remote villages in developing countries (modelling in progress). |
Title | EEG and Eye tracking: Social influence |
Description | Measurement and model of social infleunce in the brain using EEG and eye tracking. |
Type Of Material | Database/Collection of data |
Provided To Others? | No |
Impact | rare and important research inspiration of measuring social influence. |
Title | Mobility data |
Description | Collection of human mobility data using RFID tags, Blutooth enables sensors. |
Type Of Material | Database/Collection of data |
Year Produced | 2013 |
Provided To Others? | Yes |
Impact | Other researches use the data for comparison to their approaches and the research topic is promoted. |
URL | http://www.crawdad.org/keyword-DTN.html |
Description | BUGALERT: Pest and Disease Monitoring in Greenhouses with Raspberry-Pi Network |
Organisation | Illuminum Greenhouses |
Country | Kenya |
Sector | Private |
PI Contribution | Develpment of a smart greenhouse monitoring solution using image recognition to readily pick up pest and disease infestation and alert the farmers. The resulting plant images can also be used to advice on proper feeding patterns to ensure leaves remain green and healthy. |
Collaborator Contribution | Deployment of the system to various parts of Kenya and analyse different data to extract beneficial information: • Obtain user-centred feedback on the system and information provided. • Access to small holder networks in Kenya through our established partnerships. • Transfer technology to users beyond greenhouses, including farmers using drip irrigation technology. |
Impact | A prototype of image processing system. |
Start Year | 2018 |
Description | Collaboration with London School of Hygiene and Tropical Medicine |
Organisation | London School of Hygiene and Tropical Medicine (LSHTM) |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Collaboration with John Edmunds and Ken Eames at London School of Hygiene and Tropical Medicine, and with Jon Reed at the University of Liverpool and working on the joint funding proposal. |
Start Year | 2010 |
Description | Distributed data processing |
Organisation | Microsoft Research |
Department | Microsoft Research Cambridge |
Country | United Kingdom |
Sector | Private |
PI Contribution | Discussion on the topics in common interest and joint supervision of a PhD student, who works on related topic of the project. |
Collaborator Contribution | Feedback of the project work including reading papers prepared for the publication. |
Impact | Research feedback. |
Start Year | 2011 |
Description | Human mobility measurement and delay tolerant communication experiments in Brazilian Amazon remote region |
Organisation | Federal University of Amazonas |
Country | Brazil |
Sector | Academic/University |
PI Contribution | Joint experiments to deploy raspberry pi based stand alone networks in remote region of Brazilian Amazon |
Collaborator Contribution | Human mobility measurement using RFID tags and Raspberry Pi over 100 population. |
Impact | 2016/2017 plan of another experiment of hjuman mobility measurement and delay tolerant networks depending on funding availability. |
Start Year | 2015 |
Description | Large-scale graph processing using external memory |
Organisation | Swiss Federal Institute of Technology in Lausanne (EPFL) |
Country | Switzerland |
Sector | Public |
PI Contribution | Joint work on SSD based external memory approach on large graph processing sepcifically on sequential access methodology. |
Collaborator Contribution | Random access methodology on used of SSD inlarge scale graph processing |
Impact | publications: K. Nilakant, V. Dalibard, A. Roy, and E. Yoneki: PrefEdge: SSD Prefetcher for Large-Scale Graph Traversal. ACM Internataional Systems and Storage Conference (SYSTOR), June, 2014. E. Yoneki and A. Roy "Scale-up Graph Processing: A Storage-centric View". ACM SIGMOD - GRADES, New York, USA, June, 2013. |
Start Year | 2012 |
Description | Social Proximity Network Data Collection, Modelling and Analysis of TB in Tanzania |
Organisation | Ifakara Health Institute |
Country | Tanzania, United Republic of |
Sector | Charity/Non Profit |
PI Contribution | We aim at contributing to our understanding of these social proximity networks, their roles in TB transmission dynamics, and methods for measuring them, where we combine carbon dioxide exchange and electronic proximity sensing to understanding social contact and quantify risk of transmission. |
Collaborator Contribution | Our partner reviewed existing TB transmission models to understand how data on proximity networks might be incorporated into them. |
Impact | Plan for funding proposal to MRC Research Council. I have obtained EPSRC GCRF University of Cambridge Institutional funding in 2016-2018. |
Start Year | 2016 |
Description | Storage and memory system for graph processing |
Organisation | Intel Corporation |
Department | INTEL Research |
Country | United States |
Sector | Private |
PI Contribution | Analysis of graph processing specific of storage systems |
Collaborator Contribution | Joint papers |
Impact | Joint papers |
Start Year | 2015 |
Description | Cambridge University Big Data summit |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | At the event, where University of Cambridge initiates to give a series of lectures on recent movement on Big Data. I presented the large scale data processing perspectives. |
Year(s) Of Engagement Activity | 2015 |
URL | http://www.bigdata.cam.ac.uk/resources/the-vocabulary-of-big-data/ |
Description | Dagstuhl Seminar 14462 - Systems and Algorithms for Large-scale Graph Analytics |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Participants in your research and patient groups |
Results and Impact | Building research commnity on the topic of large scale data processing Establishing the research integration between industry and academia writing white paper on large sclae graph processing organisation of workshops |
Year(s) Of Engagement Activity | 2014 |
URL | http://www.dagstuhl.de/no_cache/en/program/calendar/semhp/?semnr=14462 |
Description | FluPhone Project |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | http://www.cl.cam.ac.uk/research/srg/netos/fluphone2/. |
Year(s) Of Engagement Activity | 2011 |
URL | https://www.fluphone.org. |
Description | Keynote talk at Complex Network Conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Future collaboration in Complex Network and MachineLearning Research topics. |
Year(s) Of Engagement Activity | 2016 |
Description | Seminar at KTH, Sweden |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Giving a presentation to the seminar series in KTH, stockholm, Sweden. |
Year(s) Of Engagement Activity | 2014 |
Description | Seminar at University of Helsinki |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Discussion summary on future mobile Internet |
Year(s) Of Engagement Activity | 2016 |
Description | Seminar to General Public - Pint of Science |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Public/other audiences |
Results and Impact | connect science and general public - good conversation after the talk. |
Year(s) Of Engagement Activity | 2016 |
Description | iSocial: Marie Curie Initial Training Networks: Keynote lecture |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Schools |
Results and Impact | The wrokshop is organised for mainly graduate students working on the topics of Online Social networks: Data Management and Privacy/Security Issues. The expert researchers from those reserach topics gave a series of lectures, and there was a good level of research discussion during the lecture time as well as social occasion. Many future collaborations were discussed. |
Year(s) Of Engagement Activity | 2015 |
URL | http://linc.ucy.ac.cy/isocial/ |