Dr Daniel Thompson

Research Highlights

1. Thompson, D., Karunarathna, H., Reeve, D., & Thompson, D. (2016). Comparison between wave generation methods for numerical simulation of bimodal seas. Water Science and Engineering, 9(1), 3-13.

   https://doi.org/10.1016/j.wse.2016.02.005, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa53707
2. Fry, R., Hollinghurst, J., Stagg, H., Thompson, D., Fronterre, C., Orton, C., Lyons, R., Ford, D., Sheikh, A., & Diggle, P. (2021). Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic. International Journal of Medical Informatics, 149, 104400

   https://doi.org/10.1016/j.ijmedinf.2021.104400, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa55271
3. Lyons, J., Akbari, A., Torabi, F., Davies, G., North, L., Griffiths, R., Bailey, R., Hollinghurst, J., Fry, R., Turner, S., Thompson, D., Rafferty, J., Mizen, A., Orton, C., Ellwood-Thompson, S., Au-Yeung, L., Cross, L., Gravenor, M., Brophy, S...., & Lyons, R. (2020). Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions. BMJ Open, 10(10), e043010

   https://doi.org/10.1136/bmjopen-2020-043010, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa55501
4. Rodgers, S., Rowney, F., Thompson, D., Mizen, A., White, M., Lovell, R., Fry, R., Watkins, A., Wheeler, B., Akbari, A., Stratton, G., Lyons, R., White, R., Nieuwenhuijsen, M., Geary, R., & Rodgers, S. (2020). Is Wellbeing Associated with Time Spent in Nature?. In International Journal of Population Data ScienceSwansea University.

   https://doi.org/10.23889/ijpds.v5i5.1619, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa55874
5. Johnson, R., Griffiths, L., Hollinghurst, J., Akbari, A., Lee, A., Thompson, D., Lyons, R., Fry, R., Johnson, R., Griffiths, L., Hollinghurst, J., Akbari, A., Lee, A., Thompson, D., & Lyons, R. (2020). Deriving household composition using population-scale Electronic Health Record data – a reproducible methodology. SocArXiv Papers

   https://doi.org/10.31235/osf.io/kctnz, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56152

   http://dx.doi.org/10.31235/osf.io/kctnz
6. Thompson, D., Nieuwenhuijsen, M., White, J., Lovell, R., White, M., Lyons, R., Stratton, G., Akbari, A., Geary, R., Wheeler, B., Watkins, A., Fry, R., Rowney, F., Mizen, A., & Rodgers, S. (2020). Green-Blue Spaces and Mental Health: A Longitudinal Data Linkage Study. International Journal of Population Data Science, 5(5)

   https://doi.org/10.23889/ijpds.v5i5.1616, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56153
7. Thompson, D., Abbasizanjani, H., Fry, R., Marchant, E., Griffiths, L., Akbari, A., Hollinghurst, J., North, L., Lyons, J., Torabi, F., Davies, G., Gravenor, M., & Lyons, R. (2021). Staff-Pupil SARS-CoV-2 Infection Pathways in Schools: A Population Level Linked Data Approach. medRxiv

   https://doi.org/10.1101/2021.02.04.21251087, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56212
8. Johnson, R., Griffiths, L., Hollinghurst, J., Akbari, A., Lee, A., Thompson, D., Lyons, R., & Fry, R. (2021). Deriving household composition using population-scale electronic health record data—A reproducible methodology. PLOS ONE, 16(3), e0248195

   https://doi.org/10.1371/journal.pone.0248195, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56617
9. Thompson, D., Abbasizanjani, H., Fry, R., Marchant, E., Griffiths, L., Akbari, A., Hollinghurst, J., North, L., Lyons, J., Torabi, F., Davies, G., Gravenor, M., & Lyons, R. (2021). Staff–pupil SARS-CoV-2 infection pathways in schools in Wales: a population-level linked data approach. BMJ Paediatrics Open, 5(1), e001049

   https://doi.org/10.1136/bmjpo-2021-001049, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56848

All Research

• PMIM502J Health Data Visualisation

  Health data scientists often have to present their findings to a non-academic/expert audience. The objective of this module is to enable students to choose and produce appropriate visualisations of health related data using a range of media. This is a core / compulsory module and worth 20 Masters level credits.