University of Warwick
Speak for Yourself!
Attitudes to contact tracing applications in the context of COVID-19: results from a nationally representative survey of the UK population to informing governments of appropriate design choices for adequate uptake and participation.
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Title: Speak For Yourself!
Team: Professor Carsten Maple (PI), Dr Rebecca McDonald
Description: To undertake a survey on the design of contact tracing apps. This proposal involves undertaking a nationally representative survey to elicit views of trust in an NHS contact tracing App.
Explaining the science: There is an ongoing debate in the public arena about the use of app-based contact tracing to help manage the COVID-19 pandemic. A number of countries have deployed contact tracing techniques to address the spread of the disease. A trial of a centralised UK app is ongoing on the Isle of Wight.
Despite controversy around what approach is in the public’s best interest, as yet, the opinions of the public have not been gathered, analysed or considered at a representative scale. We have undertaken a nationally-representative survey of the UK public. We utilised a specific method, a Discrete Choice Experiment (DCE), to help understand public opinion on aspects of contact tracing apps.
Real world application: The purpose of this work is to help inform those intending to design and deploy contact tracing apps in time of pandemic, allowing governments to make appropriate design choices to ensure adequate uptake and participation. In cases where concern exists, but a government has an overriding requirement, the insights can inform awareness and informational campaigns, to increase understanding of the design choice.
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Mechanistic marked spatio-temporal point processes
Exploring event- sequence data, one of the most abundant data structures in both natural and artificial ecosystems, resulting from phenomena as diverse as earthquakes, disease outbreaks, economic cycles, and social processes within digital social networks for a novel mechanistic modelling framework.
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Title: Mechanistic marked spatio-temporal point processes for large-scale data-analytic applications in identity systems and cyber-security.
Team: Dr Ioannis Kosmidis (PI), Professor Petros Dellaportas (Co-I), Dr Aristeidis Panos (PDRA)
Description: Event-sequence data is one of the most abundant data structures in both natural and artificial ecosystems, resulting from phenomena as diverse as earthquakes, disease outbreaks, economic cycles, and social processes within digital social networks. The goal of this research project is to revolutionise the analysis of event-sequence data through a novel mechanistic modelling framework for marked spatio-temporal point processes.
Explaining the science: Digital identity services and systems give rise to multiple, multilevel event sequences of unprecedented frequency and detail, both at the user level (e.g sharing of personal information and interaction with social media platforms, use of biometric devices for purchases, authentication, identity verification, and so on), and at the service/system level (such as connection requests between nodes, authentications, ad-hoc communication between linked services, and so on). Such event sequences also come with a wealth of accompanying event- or process-specific information (e.g. user/system/service characteristics, text, location, images, videos, and so on) which is now routinely recorded.
While it is widely recognised that the statistical handling of such data can lead to valuable insights with significant impact on the operation and development of identity systems and to seminal contributions in dynamic cyber-security, current statistical modelling frameworks are challenged by the data's variety, volume, velocity and heterogeneity. The goals of the proposed research program are to:
- revolutionise the analysis of event-sequence data through a novel mechanistic modelling framework for marked spatio-temporal point processes (MSTPPs);
- deliver algorithms of realistic complexity for online and large scale statistical learning of interpretable characteristics of MSTPP, along with inferential procedures about the local and global effects of accompanying information;
- develop open-source software that delivers the developments in G1 and G2 to the data science community;
- seek and engage with novel applications, with particular focus on identity systems and cyber-security.
Real world application: Event-sequence data is one of the most abundant data structures in both natural and artificial ecosystems, resulting from phenomena as diverse as earthquakes, disease outbreaks, economic cycles, and social processes within digital social networks. The outcomes of this project addresses these real world challenges.