Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Carlson CJSubject: search.filters.subject.Humans

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    Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health
    (The Royal Society, 2022-05-25) Muylaert RL; Kingston T; Luo J; Vancine MH; Galli N; Carlson CJ; John RS; Rulli MC; Hayman DTS
    Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that several closely related viruses have been discovered and sarbecovirus-host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modelled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2°C hotter in a fossil-fuelled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.
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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?