Professor Dankmar Böhning

Dr. rer. nat. (mathematics), Habil. (medical statistics and epidemiology)
Statistical Services Centre Ltd Associate and Professor of Medical Statistics at the University of Southampton, UK 

email: [email protected] 
tel: +44 (0)23 8059 6712
website: Southampton University /personal

Note Statistical Services Centre Ltd Associates are not employees. 
Statistical Services Centre Ltd Associate
Dankmar Böhning is Professor of Medical Statistics within Mathematical Sciences at the University of Southampton, UK.
Background
  • Studied Mathematics and Social Sciences at the University of Bielefeld and the Free University, Berlin.
  • Worked as Assistant Professor in Social Science Statistics at the Free University and became extra-ordinary Professor for Biometry and Epidemiology at the Charité Medical School Berlin from 2000.
  • Was Professor of Applied Statistics at the University of Reading from 2005 to 2011 when he moved to Southampton.
  • Had visiting positions in Department of Statistics at Pennsylvania State University, Institute of Statistics at the LMU in Munich, the Institute for Psychology at the University of Vienna and Mahidol University in Bangkok.
Research Interests
  • Mixture models and their applications.
  • Capture-recapture modelling in the life sciences.
  • Review research and meta-analysis.
Recent Publications
  1. Sahu, S., & Bohning, D. (2021). Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England. Spatial Statistics, [100519]. https://doi.org/10.1016/j.spasta.2021.100519
  2. Stuart, B., Hounkpatin, H., Becque, T., Yao, G., Zhu, S., Alonso-Coello, P., Altiner, A., Arroll, B., Böhning, D., Bostock, J., Bucher, H. C., Chao, J., De La Poza, M., Francis, N., Gillespie, D., Hay, A. D., Kenealy, T., Löffler, C., McCormick, D. P., ... Little, P. (2021). Delayed antibiotic prescribing for respiratory tract infections: Individual patient data meta-analysis. The BMJ, 373, [n808]. https://doi.org/10.1136/bmj.n808
  3. Bohning, D., & Ogden, H. (2021). General flation models for count data. Metrika, 84(2), 245-261. https://doi.org/10.1007/s00184-020-00786-y
  4. Bohning, D., Sangnawakij, P., Holling, H., & Böhning, W. (2021). Investigating heterogeneity in meta-analysis of studies with rare events. Metron.
  5. Böhning, D., Holling, H., Böhning, W., & Sangnawakij, P. (2021). Investigating heterogeneity in meta-analysis of studies with rare events: estimating the amount of heterogeneity. Metron, 79(3), 259-272. https://doi.org/10.1007/s40300-021-00211-y
  6. Bohning, D., Martin, S., Sangnawakij, P., Jansen, K., Böhning, W., & Holling, H. (2021). Nonparametric estimation of effect heterogeneityin rare events meta-analysis: bivariate, discrete mixture model. Lobachevskii Journal of Mathematics, 42(2), 308-317. https://doi.org/10.1134/S1995080221020074
  7. Bohning, D., Sangnawakij, P., Lerdsuwansri, R., Polonsky, J., Keita, M., Ahuka-Mundeke, S., Nsio-Mbeta, J., Aruna Abedi, A., Mossoko, M., Estill, J., Keiser, O., Kaiser, L., Yoti, Z., & Del Rio VFijas, V. (2021). Novel application of capture-recapture methods to estimate the completeness of contact tracing during a large outbreak of Ebola Virus Disease, Democratic Republic of Congo, 2018-2020. Emerging Infectious Diseases.
  8. Bohning, D., & Friedl, H. (2021). Population size estimation based upon zero-truncated, one-inflated and sparse count data: Estimating the number of dice snakes in Graz and flare stars in the Pleiades. Statistical Methods & Applications, 30(4), 1-21. https://doi.org/10.1007/s10260-021-00556-8