Jesse Knight
Jesse first joined the lab in April 2018 as a Research Data Scientist – Mathematical Modeller, after training in biomedical engineering (MASc 2017, BEng 2015; University of Guelph). His previous work focused on automated segmentation of brain MRI and analysis of digital histopathology, using a combination of classic image processing techniques and machine learning algorithms. In Sep 2019, Jesse started his PhD (U of T IMS) with the lab, supported by an NSERC Doctoral Award, and completed his PhD with the team in Aug 2023. After his PhD, Jesse completed a six-month term as a full-time research associate before joining Imperial College (UK) where he is currently pursuing his post-doctoral studies with Prof Marie-Claude Boily as a CIHR Research Fellow. Jesse is now a close collaborator with the team, including co-leading and collaborating on new research grants with the lab.
Jesse is interested in improving and validating mathematical models of epidemiologic phenomena within transmission models. Jesse has made tremendous contributions to the field of mathematical modeling. Jesse’s PhD work with the lab examined how common assumptions in compartmental models of HIV transmission can influence the outputs of those models, focusing on sex work in Eswatini. Jesse has explored: how turnover of individuals between risk groups could influence the importance of reaching key populations with HIV/STI prevention and care; what kinds of assumptions have been used in previous models of HIV transmission applied to assess ART scale-up across Sub-Saharan Africa; how assumptions about who is reached by the ART cascade of care can influence the impact of achieving “95-95-95“; how estimates of duration in sex work can be generated from cross-sectional data; and how mathematical models of partnership dynamics can influence which types of partnerships are most important for prevention. Jesse has also supported and collaborated with team members on the influence of serosorting, mixing patterns, considerations related to big data in the HIV response, the transmission population attributable fraction, and mechanisms of false-negative vaccine effectiveness.
Jesse also led modelling projects related to COVID-19 and mpox, including developing new methods for: recovering the period of infectiousness from more commonly available infectious disease data; modelling age-geographic contact patterns associated with recurrent mobility (e.g. to/from work); and illustrating major determinants of efficient vaccine prioritization during an outbreak.
During his PhD, Jesse received numerous awards, including an NSERC Doctoral Award, the Stephen Paulker Outstanding Research Award at the Society for Medical Decision Making, and the Li Ka Shing Knowledge Institute Research Excellence Award.
Mishra Lab 