I am an MRC career development award fellow at Imperial College London and an honorary lecturer at the University of Bristol. My background is in control and information engineering but I have increasingly worked on statistical and methodological problems in phylodynamics and epidemic modelling.
My research programme focuses on adapting and leveraging concepts from engineering to obtain a sharper and more rigorous understanding of biological processes. My current interests lie in (i) probing the explainability and predictability limits of statistical models across disease transmission scales and (ii) designing new algorithms and tools for improving infectious disease outbreak surveillance and suppression by using feedback control strategies.
See the links below for more information and find me on twitter @krisparag1.
Parag K, Thompson R, Donnelly C, 2022, Are epidemic growth rates more informative than reproduction numbers?, Journal of the Royal Statistical Society Series A: Statistics in Society, ISSN:0964-1998
Parag KV, Donnelly CA, Zarebski AE, 2022, Quantifying the information in noisy epidemic curves
Parag K, Donnelly C, 2022, Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers, Plos Computational Biology, Vol:18, ISSN:1553-734X
et al., 2022, Genomic Epidemiology of Early SARS-CoV-2 Transmission Dynamics, Gujarat, India, Emerging Infectious Diseases, Vol:28, ISSN:1080-6040, Pages:751-758
et al., 2022, A computationally tractable birth-death model that combines phylogenetic and epidemiological data., Plos Comput Biol, Vol:18