Browsing by Author "Rulli MC"

Now showing 1 - 3 of 3
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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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    Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots
    (Springer Nature Limited., 2023-05-30) Muylaert R; Wilkinson D; Kingston T; D’Odorico P; Rulli MC; Galli N; John RS; Alviola P; Hayman DTS
    The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers—landscape change, host distribution, and human exposure—associated with the risk of spillover of SARS-like coronaviruses to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs before examining how access to healthcare varies within clusters and scenarios. We found 19 clusters with differing risk factor contributions within a single country (N=9) or transboundary (N=10). High-risk areas were mainly closer (11-20%) rather than far (<1%) from healthcare. Areas far from healthcare reveal healthcare access inequalities, especially Scenario 3, which includes wild mammals as secondary hosts. China (N=2) and Indonesia (N=1) had clusters with the highest risk. Our findings can help stakeholders in land use planning integrating healthcare implementation and One Health actions.
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    Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots.
    (Springer Nature Limited, 2023-10-27) Muylaert RL; Wilkinson DA; Kingston T; D'Odorico P; Rulli MC; Galli N; John RS; Alviola P; Hayman DTS
    The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers-landscape change, host distribution, and human exposure-associated with the risk of spillover of zoonotic SARS-like coronaviruses to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs before examining how access to healthcare varies within clusters and scenarios. We found 19 clusters with differing risk factor contributions within a single country (N = 9) or transboundary (N = 10). High-risk areas were mainly closer (11-20%) rather than far ( < 1%) from healthcare. Areas far from healthcare reveal healthcare access inequalities, especially Scenario 3, which includes wild mammals and not livestock as secondary hosts. China (N = 2) and Indonesia (N = 1) had clusters with the highest risk. Our findings can help stakeholders in land use planning, integrating healthcare implementation and One Health actions.