Browsing by Author "Phelan AL"

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    Addressing the challenges of implementing evidence-based prioritisation in global health.
    (BMJ Publishing Group Ltd, 2023-08-02) Hayman DTS; Barraclough RK; Muglia LJ; McGovern V; Afolabi MO; N'Jai AU; Ambe JR; Atim C; McClelland A; Paterson B; Ijaz K; Lasley J; Ahsan Q; Garfield R; Chittenden K; Phelan AL; Lopez Rivera A; Abimbola S
    Global health requires evidence-based approaches to improve health and decrease inequalities. In a roundtable discussion between health practitioners, funders, academics and policy-makers, we recognised key areas for improvement to deliver better-informed, sustainable and equitable global health practices. These focus on considering information-sharing mechanisms and developing evidence-based frameworks that take an adaptive function-based approach, grounded in the ability to perform and respond to prioritised needs. Increasing social engagement as well as sector and participant diversity in whole-of-society decision-making, and collaborating with and optimising on hyperlocal and global regional entities, will improve prioritisation of global health capabilities. Since the skills required to navigate drivers of pandemics, and the challenges in prioritising, capacity building and response do not sit squarely in the health sector, it is essential to integrate expertise from a broad range of fields to maximise on available knowledge during decision-making and system development. Here, we review the current assessment tools and provide seven discussion points for how improvements to implementation of evidence-based prioritisation can improve global health.
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    The future of zoonotic risk prediction
    (The Royal Society, 2021-11-08) Carlson CJ; Farrell MJ; Grange Z; Han BA; Mollentze N; Phelan AL; Rasmussen AL; Albery GF; Bett B; Brett-Major DM; Cohen LE; Dallas T; Eskew EA; Fagre AC; Forbes KM; Gibb R; Halabi S; Hammer CC; Katz R; Kindrachuk J; Muylaert RL; Nutter FB; Ogola J; Olival KJ; Rourke M; Ryan SJ; Ross N; Seifert SN; Sironen T; Standley CJ; Taylor K; Venter M; Webala PW
    In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges?