Journal Articles

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

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Author: search.filters.author.Muylaert RLSubject: search.filters.subject.Disease Reservoirs

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    Hantavirus Expansion Trends in Natural Host Populations in Brazil.
    (MDPI (Basel, Switzerland), 2024-07-17) Mello JHF; Muylaert RL; Grelle CEV; Kemenesi G; Kurucz K
    Hantaviruses are zoonotic agents responsible for causing Hantavirus Cardiopulmonary Syndrome (HCPS) in the Americas, with Brazil ranking first in number of confirmed HCPS cases in South America. In this study, we simulate the monthly spread of highly lethal hantavirus in natural hosts by conjugating a Kermack-McCormick SIR model with a cellular automata model (CA), therefore simultaneously evaluating both in-cell and between-cell infection dynamics in host populations, using recently compiled data on main host species abundances and confirmed deaths by hantavirus infection. For both host species, our models predict an increase in the area of infection, with 22 municipalities where no cases have been confirmed to date expected to have at least one case in the next decade, and a reduction in infection in 11 municipalities. Our findings support existing research and reveal new areas where hantavirus is likely to spread within recognized epicenters. Highlighting spatial-temporal trends and potential expansion, we emphasize the increased risk due to pervasive habitat fragmentation and agricultural expansion. Consistent prevention efforts and One Health actions are crucial, especially in newly identified high-risk municipalities.
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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?