Transboundary and emerging diseases
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The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causing coronavirus disease-2019 (COVID-19) likely has evolutionary origins in other animals than humans based on genetically related viruses existing in rhinolophid bats and pangolins. Similar to other animal coronaviruses, SARS-CoV-2 contains a functional furin cleavage site in its spike protein, which may broaden the SARS-CoV-2 host range and affect pathogenesis. Whether ongoing zoonotic infections are possible in addition to efficient human-to-human transmission remains unclear. ⋯ Airborne transmission of SARS-CoV-2 between experimentally infected cats additionally substantiates the possibility of cat-to-human transmission. To evaluate the COVID-19 risk represented by domestic and farmed carnivores, experimental assessments should include surveillance and health assessment of domestic and farmed carnivores, characterization of the immune interplay between SARS-CoV-2 and carnivore coronaviruses, determination of the SARS-CoV-2 host range beyond carnivores and identification of human risk groups such as veterinarians and farm workers. Strategies to mitigate the risk of zoonotic SARS-CoV-2 infections may have to be developed in a One Health framework and non-pharmaceutical interventions may have to consider free-roaming animals and the animal farming industry.
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Transbound Emerg Dis · Jul 2021
Epidemic analysis of COVID -19 in Italy based on spatiotemporal geographic information and Google Trends.
Since the first two novel coronavirus cases appeared in January of 2020, the outbreak of the COVID-19 epidemic seriously threatens the public health of Italy. In this article, the distribution characteristics and spreading of COVID-19 in various regions of Italy were analysed by heat maps. Meanwhile, spatial autocorrelation, spatiotemporal clustering analysis and kernel density method were also applied to analyse the spatial clustering of COVID-19. ⋯ By using Adaboost algorithm for single-factor modelling,the results show that the AUC of these six features (mask, pneumonia, thermometer, ISS, disinfection and disposable gloves) are all >0.9, indicating that these features have a large contribution to the prediction model. It is also implied that the public's attention to the epidemic is increasing as well as the awareness of the need for protective measures. This increased awareness of the epidemic will prompt the public to pay more attention to protective measures, thereby reducing the risk of coronavirus infection.