Staff profile

Major Grants:

2022: Generating Socially Realistic Synthetic Networks (2023-2026)

• Funder: MRC (£528,850), Principal Investigator
• Develop methods to build synthetic social networks that reproduce real world structural properties, which are important in simulations about epidemics, adoption of behaviour, and other network diffusion processes.

I joined the Department of Sociology as Assistant Professor in Social Data Science in 2021 following postdoctoral positions modelling health behaviour at University of Surrey, Queen's University Belfast, and Durham University. Prior to entering academia, I was a senior health policy advisor in Australia, developing government and industry positions on topics such as electronic health records and private health insurance funding.

Much of my research concerns the ways in which social structure shapes the transmission of ideas, disease or behaviour. I both use and extend computational methods that are drawn from complexity science, such as agent-based modelling and network analysis. I am also interested in methods as a topic of study rather than simply a tool, particularly how models are used in policy processes and how people relate to models and data.

• Computational methods for complex systems
• Network diffusion
• Public engagement
• Social network structure

Chapter in book

• Groff, E. R., & Badham, J. (2020). Examining guardianship against theft. In C. Gerritsen, & H. Elffers (Eds.), Agent-Based Modelling for Criminological Theory Testing and Development (71-103). Routledge. https://doi.org/10.4324/9780429277177

Journal Article

• Giabbanelli, P. J., & Badham, J. (2023). Data Science in Health Services. Information, 14(6), Article 344. https://doi.org/10.3390/info14060344
• Elsenbroich, C., & Badham, J. (2023). Negotiating a Future that is not like the Past. International Journal of Social Research Methodology, 26(2), 207-213. https://doi.org/10.1080/13645579.2022.2137935
• Badham, J., Barbrook-Johnson, P., Caiado, C., & Castellani, B. (2021). Justified Stories with Agent-Based Modelling for Local COVID-19 Planning. Journal of Artificial Societies and Social Simulation, 24(1), Article 8. https://doi.org/10.18564/jasss.4532
• Befani, B., Elsenbroich, C., & Badham, J. (2021). Diagnostic evaluation with simulated probabilities. Evaluation, 27(1), 102-115. https://doi.org/10.1177/1356389020980476
• Best, P., Badham, J., McConnell, T., & Hunter, R. F. (2021). Participatory theme elicitation: open card sorting for user led qualitative data analysis. International Journal of Social Research Methodology, 25(2), 213-231 . https://doi.org/10.1080/13645579.2021.1876616
• Montgomery, S. C., Donnelly, M., Badham, J., Kee, F., Dunne, L., & Hunter, R. F. (2021). A multi-method exploration into the social networks of young teenagers and their physical activity behavior. BMC Public Health, 21(1), Article 77. https://doi.org/10.1186/s12889-020-10081-0
• Badham, J., Kee, F., & Hunter, R. F. (2021). Network structure influence on simulated network interventions for behaviour change. Social Networks, 64, 55-62. https://doi.org/10.1016/j.socnet.2020.08.003
• Yap, J., McCartan, C., Davidson, G., White, C., Bradley, L., Webb, P., Badham, J., Breslin, G., & Best, P. (2020). An exercise intervention for people with serious mental illness: Findings from a qualitative data analysis using participatory theme elicitation. Health Expectations, 23(6), 1579-1593. https://doi.org/10.1111/hex.13141
• Hamilton, S. H., Fu, B., Guillaume, J. H., Badham, J., Elsawah, S., Gober, P., Hunt, R. J., Iwanaga, T., Jakeman, A. J., Ames, D. P., Curtis, A., Hill, M. C., Pierce, S. A., & Zare, F. (2019). A framework for characterising and evaluating the effectiveness of environmental modelling. Environmental Modelling and Software, 118, 83-98. https://doi.org/10.1016/j.envsoft.2019.04.008
• Badham, J., Elsawah, S., Guillaume, J. H., Hamilton, S. H., Hunt, R. J., Jakeman, A. J., Pierce, S. A., Snow, V. O., Babbar-Sebens, M., Fu, B., Gober, P., Hill, M. C., Iwanaga, T., Loucks, D. P., Merritt, W. S., Peckham, S. D., Richmond, A. K., Zare, F., Ames, D., & Bammer, G. (2019). Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities. Environmental Modelling and Software, 116, 40-56. https://doi.org/10.1016/j.envsoft.2019.02.013
• Badham, J., Kee, F., & Hunter, R. F. (2019). Effectiveness variation in simulated school-based network interventions. Applied Network Science, 4(1), Article 70. https://doi.org/10.1007/s41109-019-0168-6
• Badham, J., Chattoe-Brown, E., Gilbert, N., Chalabi, Z., Kee, F., & Hunter, R. F. (2018). Developing agent-based models of complex health behaviour. Health & Place, 54, 170-177. https://doi.org/10.1016/j.healthplace.2018.08.022
• Badham, J., Kee, F., & Hunter, R. F. (2018). Simulating network intervention strategies: Implications for adoption of behaviour. Network Science, 6(2), 265-280. https://doi.org/10.1017/nws.2018.4
• Badham, J., Jansen, C., Shardlow, N., & French, T. (2017). Calibrating with Multiple Criteria: A Demonstration of Dominance. Journal of Artificial Societies and Social Simulation, 20(2), Article 11. https://doi.org/10.18564/jasss.3212
• Barbrook-Johnson, P., Badham, J., & Gilbert, N. (2017). Uses of Agent-Based Modeling for Health Communication: the TELL ME Case Study. Health Communication, 32(8), 939-944. https://doi.org/10.1080/10410236.2016.1196414
• Elsenbroich, C., & Badham, J. (2016). The Extortion Relationship: A Computational Analysis. Journal of Artificial Societies and Social Simulation, 19(4), Article 8. https://doi.org/10.18564/jasss.3223