My research is highly multidisciplinary and uses ideas from statistics and control engineering to obtain a sharper and more rigorous understanding of biological processes. In particular, I have applied stochastic, information, queueing, estimation and control theory to uncover new insights into molecular biology, epidemiology and invertebrate neuroscience. My current work looks at developing new models for epidemic transmission and control, which justifiably trade complexity with reliability.
Parag K, Du Plessis L, Pybus O, Jointly inferring the dynamics of population size and sampling intensity from molecular sequences, Molecular Biology and Evolution, ISSN:0737-4038
Parag KV, Donnelly CA, 2019, Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation
Parag KV, 2019, On signalling and estimation limits for molecular birth-processes, Journal of Theoretical Biology, Vol:480, ISSN:0022-5193, Pages:262-273
Parag KV, Pybus OG, 2019, Robust Design for Coalescent Model Inference, Systematic Biology, Vol:68, ISSN:1063-5157, Pages:730-743
Parag KV, Donnelly CA, 2019, Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models