Airborne microbiome alterations, an emerging global health concern, have been linked to anthropogenic activities in numerous studies. However, these studies have not reached a consensus. To reveal general trends, we conducted a meta-analysis using 3226 air samples from 42 studies, including 29 samples of our own. We found that samples in anthropogenic activity-related categories showed increased microbial diversity, increased relative abundance of pathogens, increased co-occurrence network complexity, and decreased positive edge proportions in the network compared with the natural environment category. Most of the above conclusions were confirmed using the samples we collected in a particular period with restricted anthropogenic activities. Additionally, unlike most previous studies, we used 15 human-production process factors to quantitatively describe anthropogenic activities. We found that microbial richness was positively correlated with fine particulate matter concentration, NH 3 emissions, and agricultural land proportion and negatively correlated with the gross domestic product per capita. Airborne pathogens showed preferences for different factors, indicating potential health implications. SourceTracker analysis showed that the human body surface was a more likely source of airborne pathogens than other environments. Our results advance the understanding of relationships between anthropogenic activities and airborne bacteria and highlight the role of airborne pathogens in public health.

The SARS-CoV-2 pandemic has raised concerns in the identification of the hosts of the virus since the early stages of the outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting viral genomic features automatically, to predict the host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool, reaching a satisfactory AUC of 0.975 in the five-classification, and could make a reliable prediction for the novel viruses without close neighbors in phylogeny. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existing tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of the COVID-19 pandemic, we inferred that minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, a large-scale genome analysis, based on DeepHoF’s computation for the later pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.