Lachlan HW Hamilton

Lachlan HW Hamilton

PhD Candidate at QIMR Berghofer and UQ, Computational Neuroscience

Publications

Modelling herd immunity requirements in Queensland: impact of vaccination hesitancy and variants of SARS-CoV-2

Published in Philosophical Transactions of the Royal Society A, 2022

Long-term control of SARS-CoV-2 outbreaks depend on widespread coverage of effective vaccines. In Australia, the vaccine rollout achieved >90% two-dose coverage of the adult population. However, during August 2020 to August 2021, hesitancy rates fluctuated dramatically. This left the question whether settings with low naturally-derived immunity like Queensland, where <0.005% of the population is known to have been infected in 2020, could have achieved herd immunity against variants of concern that emerged in 2021. To address this, we used an agent-based model Covasim. We simulated outbreak scenarios (with either Alpha or Delta), and assumed ongoing interventions (testing, tracing, isolation, and quarantine). We modeled the rollout using two approaches, each affording us different levels of realism. Hesitancy was modeled using survey data. We found that with a vaccine effectiveness against infection of 80% it may have been possible to control outbreaks of Alpha, but not Delta. With 90% effectiveness, control of Delta outbreaks may have been possible but only briefly before waning immunity rendered the population susceptible. Even with uncontrolled outbreaks, we estimated that a decrease in hesitancy from 20% to 14% would reduce the number of infections, hospitalizations, and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake have the potential to greatly improve health outcomes.

Recommended citation: Paula Sanz-Leon, Lachlan H. W. Hamilton, Sebastian J. Raison, Anna J. X. Pan, Nathan J. Stevenson, Robyn M. Stuart, Romesh G. Abeysuriya, Cliff C. Kerr, Stephen B. Lambert and James A. Roberts "Modelling herd immunity requirements in Queensland: impact of vaccination hesitancy and variants of SARS-CoV-2", Philosophical Transactions of the Royal Society A, (in press)

Predicting particle properties in optical traps with machine learning

Published in Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 2020

Identifying a particle in an optical trap can be a difficult task, especially for biological samples with low contrast. The relationship of radius and refractive index to the stiffness of optical traps is non-intuitive, motivating a machine learning approach. We demonstrate methods for real-time estimates of the radius and refractive index of particles trapped by optical tweezers. This is achieved by analysing the particle’s position and force with artificial neural networks. Our network achieved binary classification of experimental particles by sampling only milliseconds of force and position values. This demonstrates that real-time particle recognition is achievable with machine learning systems.

Recommended citation: Lachlan H. W. Hamilton, Lauren R. McQueen, Oscar W. Smee, Timo A. Nieminen, Halina Rubinsztein-Dunlop, and Isaac C. D. Lenton "Predicting particle properties in optical traps with machine learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691Z (17 September 2020); https://doi.org/10.1117/12.2581341