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Harnessing Algorithms for Swift and Effective Biosecurity

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In our interconnected and globally integrated world, the emergence of various pathogens is just a plane or ship journey away, and their impact can be substantial on both local and global economies. In light of this issue, Professor of Data Science, Alex Gavryushkin, is co-leading the new research exploring the algorithm to respond to a biosecurity outbreak swiftly and effectively.

Associate Professor Gavryushkin said that agriculture is vital for Aotearoa New Zealand’s economy and is the basis of global exports. The agricultural sector produces 40% of New Zealand’s exports. Agriculture is the backbone of rural economies, providing employment opportunities for farmers and farmworkers and supporting industries such as agribusinesses, equipment manufacturers, and food processing companies.

However, agriculture is not only limited as a significant source of employment in several rural areas. Its performance also influences the success of urban areas and many secondary industries which depend on it, increasing economic well-being and sustainability, influencing their growth, infrastructure development, and quality of life.

As we can see, agriculture brings a significant impact on New Zealand itself. People must take it seriously to prevent highly contagious viral infections such as foot and mouth disease (FMD). If agriculture were to be affected by such an outbreak, it could potentially throw the national economy into a recession, causing losses upward of NZ$16 billion.

Professor Gavryushkin strongly emphasises the significance of making highly accurate predictions regarding the potential spread of an outbreak. This accuracy is crucial in facilitating policymakers to foster their decision-making processes and implement effective measures to mitigate and control the outbreak’s impact.

By utilising advanced algorithms capable of dynamically updating results in real time, the research seeks to provide policymakers with up-to-date and reliable information about the evolving nature of the outbreak.

It includes predicting the areas at the highest risk of transmission and identifying potential hotspots, enabling policymakers to allocate resources strategically and implement targeted interventions to limit the spread of the disease

This research has the potential to significantly enhance the ability to respond swiftly and effectively to outbreaks, thereby safeguarding communities and facilitating a more efficient and proactive approach to public health management.

Afterwards, he is embarking on collaborating with the University of Auckland’s Dr Remco Bouckaert and partners from Massey University and the Ministry for Primary Industries (MPI) for doing research in terms of developing a new type of algorithm to improve outbreak response by providing more precision and accurate results.

The objective of the research is to create algorithms that can dynamically update results in real-time, eliminating the need to restart computations from the beginning when large volumes of new data are received. Instead, the algorithms will revise previous calculations and adjust predictions as necessary.

The current algorithm system presents policymakers with only one scenario, based on it being statistically the most probable. Its transmission tracing lacks the ability to effectively handle the continuous influx of new data, which is common during an ongoing outbreak when the transmission tree rapidly expands in size.

According to Associate Professor Gavryushkin, establishing a solid infrastructure for biosecurity algorithms will greatly enhance their ability to proactively address potential issues in the future. By conducting intricate and time-intensive pre-computations well in advance, including prior to outbreaks and concurrently with them, they can significantly mitigate challenges that may arise down the line. This proactive approach ensures that comprehensive preparations are in place, enabling a more efficient and effective response to biosecurity threats.


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