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Dr Guangquan Li

Assistant Professor

Department: Mathematics, Physics and Electrical Engineering

EE Guangquan Li Staffprofile 255 (1)I am a Lecturer in Statistics at Northumbria University. My research interests lie in developing and applying Bayesian methods to analyze data arising from health and social sciences. My current research areas include developing novel spatiotemporal models for forecasting, policy evaluation and surveillance (with applications to disease and crime) and developing clustering techniques for time-dependent data (e.g. clustering time trends of burglary rates in a study region).

After I obtained my degree in Mathematics from Newcastle University in 2004, I moved to Imperial College London to study for a Ph.D. in medical statistics.  My thesis, entitled “Stochastic models for carcinogenesis”, focuses on developing mathematical models for describing the initiation and progression of cancer at the cellular level. My Ph.D. viva took place in May 2008 with a rather unexpected outcome of “passed without corrections” (many thanks to my PhD supervisors Dr. Mark Little and Prof Paolo Vineis). After the submission of the thesis, I spent three interesting months in Japan, working at the Radiation Effects Research Foundation in Hiroshima. There I worked on a collaborative research project on evaluating impacts, due to errors in dose estimations, on risk assessment of the atomic bomb survivors. Since late 2008 (till mid-2013), I had been a research associate back at Imperial College London. I worked on various projects with a number of national and international collaborators. In July 2013, I joined Northumbria University as a Lecturer in Statistics. I am a fellow of the Royal Statistical Society

  • Please visit the Pure Research Information Portal for further information
  • Integrating wastewater and randomised prevalence survey data for national COVID surveillance, Li, G., Diggle, P., Blangiardo, M. 1 Mar 2024, In: Scientific Reports
  • Statistical Modeling of Spatially Stratified Heterogeneous Data, Wang, J., Haining, R., Zhang, T., Xu, C., Hu, M., Yin, Q., Li, L., Zhou, C., Li, G., Chen, H. 15 Mar 2024, In: Annals of the American Association of Geographers
  • A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic, Li, G., Denise, H., Diggle, P., Grimsley, J., Holmes, C., James, D., Jersakova, R., Mole, C., Nicholson, G., Smith, C., Richardson, S., Rowe, W., Rowlingson, B., Torabi, F., Wade, M., Blangiardo, M. 1 Feb 2023, In: Environment international
  • Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data, Bennett, J., Rashid, T., Zolfaghari, A., Doyle, Y., Suel, E., Pearson-Stuttard, J., Davies, B., Fecht, D., Muller, E., Nathvani, R., Sportiche, N., Daby, H., Johnson, E., Li, G., Flaxman, S., Toledano, M., Asaria, M., Ezzati, M. 1 Apr 2023, In: The Lancet Regional Health - Europe
  • Spatial accessibility of pre-exposure prophylaxis (PrEP): different measure choices and the implications for detecting shortage areas and examining its association with social determinants of health, Luan, H., Li, G., Duncan, D., Sullivan, P., Ransome, Y. 1 Oct 2023, In: Annals of Epidemiology
  • Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models, Torabi, F., Li, G., Mole, C., Nicholson, G., Rowlingson, B., Smith, C., Jersakova, R., Diggle, P., Blangiardo, M. Nov 2023, In: Heliyon
  • Association of environmental and socioeconomic indicators with serious mental illness diagnoses identified from general practitioner practice data in England: A spatial Bayesian modelling study, Cruz, J., Li, G., Aragon, M., Coventry, P., Jacobs, R., Prady, S., White, P. 30 Jun 2022, In: PLoS Medicine
  • Data-Driven Responses to COVID-19: Lessons Learned: OMDDAC Research Compendium, Allsopp, R., Bessant, C., Dawda, S., Ditcham, K., Emmett, C., Higgs, M., Janjeva, A., Li, G., Sutton, S., Warner, M., Oswald, M. 12 Oct 2021
  • Life expectancy and risk of death in 6791 communities in England from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data, Rashid, T., Bennett, J., Paciorek, C., Doyle, Y., Pearson-Stuttard, J., Flaxman, S., Fecht, D., Toledano, M., Li, G., Daby, H., Johnson, E., Davies, B., Ezzati, M. 1 Nov 2021, In: The Lancet Public Health
  • Observing Data-Driven Approaches to Covid-19: Reflections from a Distributed, Remote, Interdisciplinary Research Project, Allsopp, R., Bessant, C., Ditcham, K., Janjeva, A., Li, G., Oswald, M., Warner, M. 17 Aug 2021, In: Journal of Legal Research Methodology

  • Mark Hancock Mapping the uncertain future of longevity: an ensemble approach for forecasting mortality Start Date: 01/10/2017 End Date: 19/07/2021
  • Glory Atilola Bayesian Modelling and Mapping of Health Outcomes in Space and Time using Complex National Surveys Start Date: 24/02/2020 End Date: 25/03/2022

  • Statistics PhD April 01 2008
  • Information not provided Royal Statistical Society (RSS) 2013

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