Optimal Resource Allocation in Clinical and Public Health Research

Optimising resource allocation is critical for maximising the value of data in clinical trials and epidemiological studies, particularly in resource-constrained settings. Strategic allocation of sampling effort can provide the best information possible for study objectives, within ethical or logistical constraints on available resources.

We have demonstrated this principle across multiple contexts optimal designs for robust inference for pharmacokinetic studies (Jamsen et al., 2011, Jamsen et al. 2013, Price et al., 2018a), bacterial treatment effects (Vlazaki et al., 2020), dose-response studies in the presence of transmission (Price et al., 2018b), and learning transmission characteristics of a pathogen (Price et al. 2016, Lydeamore et al. 2021). Multi-criteria frameworks for surveillance site selection ensures that monitoring resources can be deployed to maximise public health objectives for diseases like Plasmodium knowlesi malaria (Harrison et al., 2024).

We integrate mechanistic understanding of disease dynamics with formal statistical inference and optimisation methods to develop and implement methods for identifying the best study designs to answer critical questions more efficiently and ultimately accelerate quality evidence generation to improve clinical and public health decision-making.

Selected publications

Jamsen, K.M., Duffull, S.B., Tarning, J. et al. (2011) Optimal designs for population pharmacokinetic studies of oral artesunate in patients with uncomplicated falciparum malaria. Malar J 10, 181.

Jamsen, K.M., Duffull, S.B., Tarning, J. et al. (2013) A robust design for identification of the Parasite Clearance Estimator. Malaria Journal, 12(410).

Price, D.J., Bean, N.G., Ross, J.V. & Tuke, J. (2018a). An induced natural selection heuristic for finding optimal Bayesian experimental designs. Computational Statistics & Data Analysis, 126, 112–124.

Vlazaki, M., Price, D.J. & Restif, O. (2020). An experimental design tool to optimize inference precision in data-driven mathematical models of bacterial infections in vivo. Journal of the Royal Society Interface, 17(173), 20200717.

Price, D.J., Bean, N.G., Ross, J.V. & Tuke, J. (2018b). Designing group dose-response studies in the presence of transmission. Mathematical Biosciences, 304, 62–78.

Price, D.J., Bean, N.G., Ross, J.V. & Tuke, J. (2016). On the efficient determination of optimal Bayesian experimental designs using ABC: A case study in optimal observation of epidemics. Journal of Statistical Planning and Inference, 172, 1–15.