ACEMS Research Briefs

These are short summary descriptions of our latest, just-published research.

ACEMS Research Briefs Projects

Identifiability analysis for stochastic differential equation models in systems biology

ACEMS researchers bring together existing tools that are designed for one type of model and show how they can be used for paramater identifiability in stochastic models of biological processes.

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Using Bayesian statistics and mathematical models to assess the global response to COVID-19. This analysis uses data from 158-countries.

Advanced Bayesian approaches for state-space models with a case study on soil carbon sequestration

The methods presented in this paper are useful for farmers and landowners in estimating carbon stocks in agricultural production systems.

Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours

What if farmers could tell if one of their livestock was sick, without even being around the animal? Farmers are now using inertial motion sensors to study grazing behaviour. But the challenge is interpreting all the data those sensors provide. New research led by ACEMS PhD candidate Shuwen Hu reveals which machine learning methods are best suited to handle the problem.

Multivariate Bayesian meta‑analysis: joint modelling of multiple cancer types using summary statistics

This research provides a tool so that people can use readily available data from online cancer atlases, and understand patterns for multiple cancer types together.

Elucidating user behaviours in a digital health surveillance system to correct prevalence estimates

ACEMS researchers at The University of Adelaide develop a statistical model to get a better estimate of the spread of influenza-like-illness, accounting for human behaviour in online health surveillance systems.

Particle Methods for Stochastic Differential Equation Mixed Effects Models

ACEMS Researchers at QUT and UNSW introduce three novel parameter estimation methods for stochastic differential equation mixed effects models (SDEMEMs).

Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data

How do you measure something you can’t see?

That’s the aim of new research just published by ACEMS’ researchers in the Journal of Big Data. Led by Jacinta Holloway Brown from ACEMS at QUT, the researchers developed a new statistical method to predict forest cover in satellite images where portions of the image are blocked by cloud cover. Not only that, the new method also calculates a probability to show how confident the prediction is.

Forecasting intensifying disturbance effects on coral reefs

ACEMS research combines state-of-art methodologies for coral reef data collection, remote sensing and statistical modelling to predict the future ecological status of the reefs within the Great Barrier Reef as the incidence of multiple disturbances continues to increase.

Monitoring through many eyes: Integrating datasets to improve monitoring of the Great Barrier Reef

Enormous effort is invested in monitoring the Great Barrier Reef (GBR), but data collection is currently fragmented over dozens of publicly and privately funded organisations, with data collected using different methods and for different purposes. As a result, the data are rarely analysed together.

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These are short summary descriptions of our latest, just-published research.