Small mammals and associated infections in China: a systematic review and spatial modelling analysis.

Background: As natural reservoirs of diverse pathogens, small mammals are considered a key interface for guarding public health due to their wide geographic distribution, high density and frequent interaction with humans.

Methods: All formally recorded natural occurrences of small mammals (Order: Rodentia, Eulipotyphla, Lagomorpha, and Scandentia) and their associated microbial infections in China were searched in the English and Chinese literature spanning from 1950 to 2021 and geolocated. Machine learning models were applied to determine ecological drivers for the distributions of 45 major small mammal species and two common rodent-borne diseases (RBDs), and model-predicted potential risk locations were mapped.

Inter-city movement pattern of notifiable infectious diseases in China: a social network analysis.

Background: Co-existence of efficient transportation networks and geographic imbalance of medical resources greatly facilitated inter-city migration of patients of infectious diseases in China.

Methods: To characterize the migration patterns of major notifiable infectious diseases (NIDs) during 2016-2020 in China, we collected migratory cases, who had illness onset in one city but were diagnosed and reported in another, from the National Notifiable Infectious Disease Reporting System, and conducted a nationwide network analysis of migratory cases of major NIDs at the city (prefecture) level.

Findings: In total, 2,674,892 migratory cases of NIDs were reported in China during 2016-2020.

The global distribution and diversity of wild-bird-associated pathogens: An integrated data analysis and modeling study.

Background: Wild birds are significant vectors in global pathogen transmission, but the diversity and spatial distribution of the pathogens detected in them remain unclear. Understanding the transmission dynamics and hotspots of wild-bird-associated pathogens (WBAPs) is crucial for early disease prevention.

Methods: We compiled an up-to-date dataset encompassing all WBAPs by conducting an extensive search of publications from 1959 to 2022, mapped their diversity and global distribution, and utilized three machine learning algorithms to predict geospatial hotspots where zoonotic and emerging WBAPs were prevalent.