Background: COVID-19 elimination measures, including border closures have been applied in New Zealand. We have modelled the potential effect of vaccination programmes for opening borders.

Code Fragments (CFs) are a new representation for classifier conditions in Learning Classifier Systems (LCSs). CFs are Genetic Programming-like trees that use functions as internal nodes, and data input or previously learned CFs as leaf nodes for feature construction. The XCSCFC system used CFs in rule conditions of XCS, an accuracy-based Michigan-style LCS, to transfer knowledge and thus solve large-scale problems. However, the trade-off for the richness and flexibility that allows CFs to compactly describe decision boundaries results in an undesired increase in the search space of solutions. Therefore, this paper proposes a novel model extension for Online Feature-generation (OF), which enables evolving features (CFs) through an online observed list of CFs. This extension enables a method of estimating the worth of CFs to identifying the patterns in the problem in order to construct applicable high-level features. The experiments show that the XCS with OF (XOF) can solve the benchmark problems in fewer generations compared with XCSCFC in non-transfer learning scenarios. The novel search of CFs successfully built high-level features, which show the rules produced by XOF to be more generalised than previously possible. Consequently, the final solutions contain fewer rules to solve problems as they encode more compact and comprehensive decision boundaries.

Despite being the widely-used gold standard for linking common genetic variations to phenotypes and disease, genome-wide association studies (GWAS) suffer major limitations, partially attributable to the reliance on simple, typically linear, models of genetic effects. More elaborate methods, such as epistasis-aware models, typically struggle with the scale of GWAS data. In this paper, we build on recent advances in neural networks employing Transformer-based architectures to enable such models at a large scale. As a first step towards replacing linear GWAS with a more expressive approximation, we demonstrate prediction of gout, a painful form of inflammatory arthritis arising when monosodium urate crystals form in the joints under high serum urate conditions, from Single Nucleotide Variants (SNVs) using a scalable (long input) variant of the Transformer architecture. Furthermore, we show that sparse SNVs can be efficiently used by these Transformer-based networks without expanding them to a full genome. By appropriately encoding SNVs, we are able to achieve competitive initial performance, with an AUROC of 83% when classifying a balanced test set using genotype and demographic information. Moreover, the confidence with which the network makes its prediction is a good indication of the prediction accuracy. Our results indicate a number of opportunities for extension, enabling full genome-scale data analysis using more complex and accurate genotype-phenotype association models.

Multitask Learning is a learning paradigm that deals with multiple different tasks in parallel and transfers knowledge among them. XOF, a Learning Classifier System using tree-based programs to encode building blocks (meta-features), constructs and collects features with rich discriminative information for classification tasks in an Observed List. This paper seeks to facilitate the automation of feature transferring in between tasks by utilising the Observed List. We hypothesise that the best discriminative features of a classification task carry its characteristics. Therefore, the relatedness between any two tasks can be estimated by comparing their most appropriate patterns. We propose a multiple-XOF system, called mXOF, that can dynamically adapt feature transfer among XOFs. This system utilises the Observed List to estimate the task relatedness. This method enables the automation of transferring features. In terms of knowledge discovery, the resemblance estimation provides insightful relations among multiple data. We experimented mXOF on various scenarios, e.g. representative Hierarchical Boolean problems, classification of distinct classes in the UCI Zoo dataset, and unrelated tasks, to validate its abilities of automatic knowledgetransfer and estimating task relatedness. Results show that mXOF can estimate the relatedness reasonably between multiple tasks to aid the learning performance with the dynamic feature transferring.