Data Driven Network Modelling for Epidemiology in Dynamic Human Networks

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.