Wildlife susceptibility to infectious diseases at global scales.

Disease transmission prediction across wildlife is crucial for risk assessment of emerging infectious diseases. Susceptibility of host species to pathogens is influenced by the geographic, environmental, and phylogenetic context of the specific system under study. We used machine learning to analyze how such variables influence pathogen incidence for multihost pathogen assemblages, including one of direct transmission (coronaviruses and bats) and two vector-borne systems (West Nile Virus [WNV] and birds, and malaria and birds). Here we show that this methodology is able to provide reliable global spatial susceptibility predictions for the studied host-pathogen systems, even when using a small amount of incidence information (i.e., [Formula: see text] of information in a database). We found that avian malaria was mostly affected by environmental factors and by an interaction between phylogeny and geography, and WNV susceptibility was mostly influenced by phylogeny and by the interaction between geographic and environmental distances, whereas coronavirus susceptibility was mostly affected by geography. This approach will help to direct surveillance and field efforts providing cost-effective decisions on where to invest limited resources.

Traceable surveillance and genetic diversity analysis of coronaviruses in poultry from China in 2019.

Coronavirus disease 2019 (COVID-19), caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first reported in Wuhan, China, and rapidly spread throughout the world. This newly emerging pathogen is highly transmittable and can cause fatal disease. More than 35 million cases have been confirmed, with a fatality rate of about 2.9% to October 9, 2020. However, the original and intermediate hosts of SARS-CoV-2 remain unknown. Here, 3160 poultry samples collected from 14 provinces of China between September and December 2019 were tested for SARS-CoV-2 infection. All the samples were SARS-CoV-2 negative, but 593 avian coronaviruses were detected, including 485 avian infectious bronchitis viruses, 72 duck coronaviruses, and 36 pigeon coronaviruses, with positivity rates of 15.35%, 2.28%, and 1.14%, respectively. Our surveillance demonstrates the diversity of avian coronaviruses in China, with higher prevalence rates in some regions. Furthermore, the possibility that SARS-CoV-2 originated from a known avian-origin coronavirus can be preliminarily ruled out. More surveillance of and research into avian coronaviruses are required to better understand the diversity, distribution, cross-species transmission, and clinical significance of these viruses.