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Researchers have developed an AI-based transmission model that accurately predicts infection rates in prisons. This model uses real-world data from all facilities in the New South Wales (NSW) prison system, lending empirical support to existing theoretical models. The study, titled “Controlling COVID-19 Outbreaks in the Correctional Setting: A Mathematical Modelling Study,” was published in PLOS ONE this month.
According to Dr Neil Bretaña, the Lead Author of the study from the University of South Australia, the model demonstrates that vaccination significantly reduced virus spread, but additional measures were necessary to halt the disease effectively. He notes that while mathematical models have been extensively used to inform health policy during the COVID-19 pandemic, they often lacked real-world data to validate their accuracy. Previous models within the prison system tended to focus on individual facilities. In contrast, this study incorporated data from the entire NSW prison system, encompassing 33 centres, 13,458 inmates, and 7,487 workers. The model’s accuracy was validated by the major Delta outbreak in August-September 2021, predicting case rates that closely matched the actual figures.
Prisons are highly vulnerable to infectious diseases, with COVID-19 outbreaks reported globally at the pandemic’s peak. Close contact among inmates, a higher prevalence of co-morbidities, and the risk of broader community infection through workers and visitors highlight the need to prioritise prisons in public health strategies. Dr Bretaña’s recent study identified vaccination and prompt lockdowns as the most effective methods to minimise COVID-19 spread in prisons. However, it also emphasises that a combination of additional measures is essential to effectively contain the disease in these confined environments.
Dr Bretaña states that the combination of vaccination and immediate lockdowns upon detecting an infection proved to be the most effective strategy, reducing outbreak size by 62–73%. Other measures contributing to reduced virus spread included quarantining inmates upon entry, isolating confirmed or suspected cases, and utilising personal protective equipment like masks.
The COVID-19 pandemic has highlighted the necessity of public health planning to mitigate disease spread effectively. This study also underscores the importance of collaboration with government agencies to inform evidence-based policy decisions. The findings have already been utilised by Corrective Services NSW as a foundation for their intervention protocols. The model’s framework can be adapted to address emerging COVID-19 variants and other similarly transmissible respiratory pathogens, such as Bird Flu (H5N1), aiding in preparation for future outbreaks.
The global increase in infections is fueling a significant expansion of AI applications in the vaccine development sector worldwide. Numerous companies are turning to artificial intelligence to expedite and refine the vaccine creation process. AI’s contributions span various critical domains, including identifying optimal vaccine targets, conducting laboratory tests, organising research data, and streamlining vaccine distribution channels.
With a market value of $11.57 billion in 2023, the global AI in vaccine development sector is projected to witness remarkable growth, reaching $118.69 billion by 2030, boasting a compound annual growth rate (CAGR) of 39.45% from 2024 to 2030.
In traditional drug and vaccine development, the process is often both costly and time-consuming. However, AI offers a solution by simultaneously analysing multiple variables, thereby predicting the most promising vaccine targets efficiently. Moreover, AI aids in deepening our understanding of viral mechanisms, a crucial aspect of vaccine development.
Furthermore, AI plays a pivotal role in drug development by meticulously examining molecular structures, repurposing existing drugs, and accurately predicting effective targets for new compounds. Pharmaceutical companies are increasingly leveraging AI to streamline drug development processes, predict drug efficacy and potential side effects, and effectively manage vast amounts of research data, thereby accelerating the drug discovery and commercialisation process.
