Bedilu Alamirie Ejigu

Bedilu Alamirie Ejigu is an assistant professor of statistics at Addis Ababa University. He received his Ph.D. in statistics from the Addis Ababa University with first-class honor. In his dissertation, a novel modeling approach which takes into account both the spatial and environmental effect in the analysis of environmentally-mediated outcome variable contributed.

He is a principal investigator of the “Epidemic Prediction of COVID-19 in Ethiopia Under different Measures” project funded by Addis Ababa University. His work experience at different higher education institutions in Ethiopia as an instructor, researcher, and consultant, and as a researcher at Flemish research Institute and Hasselt University, Belgium provided him on the job experience in using large and complex datasets to recommend to different stakeholders.  He was awarded the prestigious “VLIR-UOS” International Master program Fellowship for studying his Master’s degree in Biostatistics at Hasselt University, Belgium.  His research interests involve the development of statistical methods for analyzing spatial data and their applications in health sciences, and modeling infectious disease modeling. Dr. Bedilu is the co-author of the “Basic statistics” book and published different research outcomes in peer-reviewed journals as a lead author.

Selected Publications

  1. Ejigu BA., and Wencheko E., (2020). Introducing Covariate Dependent Weighting matrices in Fitting Autoregressive Models and Measuring Environmental Autocorrelation. Spatial Statistics, 38 (100454).
  2. Ejigu BA., Wencheko E., Moraga P., and Giorgi E., (2019). Geostatistical Methods for Modeling Nonstationary Patterns in Disease Risk. Spatial Statistics, 35 (100397).
  3. Ejigu BA., Wencheko E., and Berhane K., (2018). Spatial pattern and determinants of anaemia in Ethiopia. PLoS ONE, 13(5):e0197171.
  4. Ejigu BA., Valkenborg D., Baggerman G., Vanaerschot M., Witters E., Dujardin JC., Burzykowski T., Berg M. (2013). Evaluation of normalization methods to pave the way towards large-scale LC- MS-based metabolomics profiling experiments. OMICS, 17(9), 473-485.