Christopher Drovandi

Queensland University of Technology

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Kerrie Mengersen

Queensland University of Technology

Mohammad Javad Davoudabadi

Queensland University of Technology

Davide Fontanarosa

Queensland University of Technology

Dan Pagendam

Commonwealth Scientific and Industrial Research Organisation

Jeff Baldock

Commonwealth Scientific and Industrial Research Organisation

Kelly Wilson‐Stewart

Queensland University of Technology

Jamie Trapp

Queensland University of Technology

Gentry White

Queensland University of Technology

Rebecca Anderson

Greenslopes Private Hospital

David B. Pyne

University of Canberra

Cooperative Institutions

Queensland University of Technology

ARC Centre of Excellence for Mathematical and Statistical Frontiers

University of Queensland

Australian Research Council

University of Oxford

Commonwealth Scientific and Industrial Research Organisation

James Cook University

Australian Institute of Marine Science

Monash University

University of Melbourne

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Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora

PeerJ (2017)

Carla Chen David G. Bourne Christopher Drovandi Kerrie Mengersen Bette L. Willis

Seawater temperature anomalies associated with warming climate have been linked to increases in coral disease outbreaks that have contributed to coral reef declines globally. However, little is known about how seasonal scale variations in environmental factors influence disease dynamics at the level of individual coral colonies. In this study, we applied a multi-state Markov model (MSM) to investigate the dynamics of black band disease (BBD) developing from apparently healthy corals and/or a precursor-stage, termed ‘cyanobacterial patches’ (CP), in relation to seasonal variation in light and seawater temperature at two reef sites around Pelorus Island in the central sector of the Great Barrier Reef. The model predicted that the proportion of colonies transitioning from BBD to Healthy states within three months was approximately 57%, but 5.6% of BBD cases resulted in whole colony mortality. According to our modelling, healthy coral colonies were more susceptible to BBD during summer months when light levels were at their maxima and seawater temperatures were either rising or at their maxima. In contrast, CP mostly occurred during spring, when both light and seawater temperatures were rising. This suggests that environmental drivers for healthy coral colonies transitioning into a CP state are different from those driving transitions into BBD. Our model predicts that (1) the transition from healthy to CP state is best explained by increasing light, (2) the transition between Healthy to BBD occurs more frequently from early to late summer, (3) 20% of CP infected corals developed BBD, although light and temperature appeared to have limited impact on this state transition, and (4) the number of transitions from Healthy to BBD differed significantly between the two study sites, potentially reflecting differences in localised wave action regimes.

Coral bleaching

10.7717/peerj.3438

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Citations (12)

Profile likelihood analysis for a stochastic model of diffusion in heterogeneous media

Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (2021)

Matthew J. Simpson Alexander P. Browning Christopher Drovandi Elliot J. Carr Oliver J. Maclaren

We compute profile likelihoods for a stochastic model of diffusive transport motivated by experimental observations of heat conduction in layered skin tissues. This process is modelled as a random walk in a layered one-dimensional material, where each layer has a distinct particle hopping rate. Particles are released at some location, and the duration of time taken for each particle to reach an absorbing boundary is recorded. To explore whether these data can be used to identify the hopping rates in each layer, we compute various profile likelihoods using two methods: first, an exact likelihood is evaluated using a relatively expensive Markov chain approach; and, second, we form an approximate likelihood by assuming the distribution of exit times is given by a Gamma distribution whose first two moments match the moments from the continuum limit description of the stochastic model. Using the exact and approximate likelihoods, we construct various profile likelihoods for a range of problems. In cases where parameter values are not identifiable, we make progress by re-interpreting those data with a reduced model with a smaller number of layers.

Stochastic modelling

10.1098/rspa.2021.0214

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Citations (18)

Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data

Science Advances (2022)

Gloria M. Monsalve-Bravo Brodie Lawson Christopher Drovandi Kevin Burrage Kevin Brown

This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.

Experimental data

10.1126/sciadv.abm5952

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Citations (26)

Predicting performance in 4 x 200-m freestyle swimming relay events

PLoS ONE (2021)

Paul Wu Toktam Babaei Michael O’Shea Kerrie Mengersen Christopher Drovandi

Aim The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy. Data and methods Race data for 716 relay finals (4 x 200-m freestyle) from 14 international competitions between 2010–2018 were analysed. Individual 200-m freestyle season best time for the same year was located for each swimmer. Linear regression and machine learning was applied to 4 x 200-m swimming freestyle relay events. Results Compared to the individual event, the lowest ranked swimmer in the team (-0.62 s, CI = [−0.94, −0.30]) and American swimmers (−0.48 s [−0.89, −0.08]) typically swam faster 200-m times in relay events. Random forest models predicted gold, silver, bronze and non-medal with 100%, up to 41%, up to 63%, and 93% sensitivity, respectively. Discussion Team finishing position was strongly associated with the differential time to the fastest team (mean decrease in Gini (MDG) when this variable was omitted = 31.3), world rankings of team members (average ranking MDG of 18.9), and the order of swimmers (MDG = 6.9). Differential times are based on the sum of individual swimmer’s season’s best times, and along with world rankings, reflect team strength. In contrast, the order of swimmers reflects strategy. This type of analysis could assist coaches and support staff in selecting swimmers and team orders for relay events to enhance the likelihood of success.

10.1371/journal.pone.0254538

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Citations (4)

Adaptive experimental design produces superior and more efficient estimates of predator functional response

PLoS ONE (2023)

Nikos E. Papanikolaou Hayden Moffat Argyro A. Fantinou Dionysios P. Perdikis Michael Bode

Ecological dynamics are strongly influenced by the relationship between prey density and predator feeding behavior-that is, the predatory functional response. A useful understanding of this relationship requires us to distinguish between competing models of the functional response, and to robustly estimate the model parameters. Recent advances in this topic have revealed bias in model comparison, as well as in model parameter estimation in functional response studies, mainly attributed to the quality of data. Here, we propose that an adaptive experimental design framework can mitigate these challenges. We then present the first practical demonstration of the improvements it offers over standard experimental design. Our results reveal that adaptive design can efficiently identify the preferred functional response model among the competing models, and can produce much more precise posterior distributions for the estimated functional response parameters. By increasing the efficiency of experimentation, adaptive experimental design will lead to reduced logistical burden.

Adaptive response

Functional design

Response time

10.1371/journal.pone.0288445

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Citations (1)

Author Correction: Taller staff occupationally exposed to less radiation to the temple in cardiac procedures, but risk higher doses during vascular cases

Scientific Reports (2020)

Kelly Wilson‐Stewart Davide Fontanarosa Dan Li Christopher Drovandi Rebecca Anderson

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Temple

10.1038/s41598-020-80349-3

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Citations (0)

Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology

Science Advances (2018)

Brodie Lawson Christopher Drovandi Nicole Cusimano Pamela Burrage Blanca Rodríguez

We describe a statistically informed calibration of in silico populations to explore variability in complex systems.

Data set

Atrial action potential

Experimental data

Identification

Cardiac Electrophysiology

10.1126/sciadv.1701676

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Citations (73)

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2023)

Joshua J. Bon Adam Bretherton Katie Buchhorn Susanna Cramb Christopher Drovandi

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Approximate Bayesian Computation

10.1098/rsta.2022.0156

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Citations (11)

Strategic model reduction by analysing model sloppiness: A case study in coral calcification

Environmental Modelling & Software (2022)

Sarah A. Vollert Christopher Drovandi Gloria M. Monsalve-Bravo Matthew Adams

Identification

Predictive power

10.1016/j.envsoft.2022.105578

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Citations (10)

Joint-level energetics differentiate isoinertial from speed-power resistance training—a Bayesian analysis

PeerJ (2018)

Bernard X. W. Liew Christopher Drovandi Samuel Clifford Justin Keogh Susan Morris

Background There is convincing evidence for the benefits of resistance training on vertical jump improvements, but little evidence to guide optimal training prescription. The inability to detect small between modality effects may partially reflect the use of ANOVA statistics. This study represents the results of a sub-study from a larger project investigating the effects of two resistance training methods on load carriage running energetics. Bayesian statistics were used to compare the effectiveness of isoinertial resistance against speed-power training to change countermovement jump (CMJ) and squat jump (SJ) height, and joint energetics. Methods Active adults were randomly allocated to either a six-week isoinertial ( n = 16; calf raises, leg press, and lunge), or a speed-power training program ( n = 14; countermovement jumps, hopping, with hip flexor training to target pre-swing running energetics). Primary outcome variables included jump height and joint power. Bayesian mixed modelling and Functional Data Analysis were used, where significance was determined by a non-zero crossing of the 95% Bayesian Credible Interval (CrI). Results The gain in CMJ height after isoinertial training was 1.95 cm (95% CrI [0.85–3.04] cm) greater than the gain after speed-power training, but the gain in SJ height was similar between groups. In the CMJ, isoinertial training produced a larger increase in power absorption at the hip by a mean 0.018% (equivalent to 35 W) (95% CrI [0.007–0.03]), knee by 0.014% (equivalent to 27 W) (95% CrI [0.006–0.02]) and foot by 0.011% (equivalent to 21 W) (95% CrI [0.005–0.02]) compared to speed-power training. Discussion Short-term isoinertial training improved CMJ height more than speed-power training. The principle adaptive difference between training modalities was at the level of hip, knee and foot power absorption.

Countermovement

Plyometrics

Squat

Energetics

10.7717/peerj.4620

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Citations (1)