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New Zealand’s economy is deeply rooted in agriculture, with food and fibre exports accounting for 82% of national exports and 11% of GDP. Cattle farming plays a pivotal role, with dairy and meat alone comprising nearly two-thirds of the sector’s export value.

In 2017, the country faced a serious threat: the detection of Mycoplasma bovis, a bacterial disease affecting dairy and beef cattle. Left unchecked, M. bovis causes severe animal health issues and significant economic losses. In response, the New Zealand Government’s Ministry for Primary Industries (MPI) launched a ten-year eradication programme, projected to cost NZ$886 million (£470 million). The alternative—doing nothing—was estimated to cost the nation NZ$1.3 billion.

To track, understand, and contain the spread of the disease, New Zealand needed a robust digital tool capable of integrating diverse sources of outbreak data, accommodating uncertainty, and supporting urgent public health decisions.

The solution

Enter BORIS—Bayesian Outbreak Reconstruction Inference & Simulation—a computer model initially developed by Associate Professor Simon Firestone at the University of Melbourne to help map and understand disease transmission routes. However, it was the underlying algorithm, created by �ֲ���ý’s Dr Max Lau, along with Professors Gavin Gibson and George Streftaris, that became the backbone of BORIS’s predictive power.

The team’s breakthrough work at �ֲ���ý and the Maxwell Institute for Mathematical Sciences, in collaboration with Biomathematics & Statistics Scotland, had previously developed an advanced modelling framework that uniquely combined genetic information with epidemiological data to reconstruct outbreak pathways.

Dr Lau and Assoc Prof Firestone adapted the algorithm for use in R, a widely adopted statistical programming language, and expanded its capabilities to handle partial and incomplete data—a common reality in outbreak investigations.

This innovation was pivotal: where traditional models faltered due to missing links or uncertain data, �ֲ���ý’s algorithm allowed these gaps to be incorporated as variables within the model, producing results that acknowledged uncertainty but still offered robust, actionable insights.

Impact and outcomes

The BORIS model played a critical role in helping New Zealand navigate the M. bovis crisis. By December 2022:

• 272 farms had been cleared of M. bovis, with only six remaining infected
• Over NZ$230 million had been paid in compensation across 2,829 claims
• Decision-makers used BORIS alongside testing and tracing to identify hidden transmission routes, supporting targeted interventions

The work received acclaim from researchers and government officials alike:

Global legacy and future applications

What began as a cattle disease control initiative has since evolved into a globally relevant disease modelling platform. The BORIS framework, and the �ֲ���ý algorithm that powers it, has been repurposed and applied to a range of other high-profile outbreaks:

• COVID-19 modelling in Australia, informing national response efforts
• Foot-and-mouth disease in Japan, supporting outbreak reconstruction
• Ongoing development of expanded tools under an Australian Research Council-funded COVID-19 project

The collaboration continues with partners like Massey University in New Zealand, using BORIS to provide highly detailed, transmission-focused insights in conjunction with national response programmes.

This case study exemplifies how �ֲ���ý’s mathematical and data science research can power critical global infrastructure—helping governments, industries, and communities respond to urgent challenges with confidence, agility, and scientific precision.