Human adenovirus type 14 variant p1 (HAdV-14p1) has been associated with severe respiratory infections worldwide. However, no cases of HAdV-14p1 infection have been reported in China since 2019. HAdV-14 was unexpectedly identified in 2024 in a pediatric patient hospitalized with bronchopneumonia in Chongqing, China. This study aims to elucidate the genetic characteristics of this strain and determine its phylogenetic relationship with previously circulating domestic and international strains.

Whole-genome sequencing was performed on the HAdV-14 strain Chongqing2024-115, followed by comprehensive phylogenetic and genetic variation analyses using all publicly available global HAdV-14 genome sequences from the GenBank database.

Genome-based phylogenetic analysis classified global HAdV-14 strains into two well-supported clades: the HAdV-14 prototype clade and the variant HAdV-14p1 clade. The latter was further subdivided into three distinct subclades (I–III). Chongqing2024-115 clustered closely with earlier HAdV-14p1 subclade III and isolates from Beijing (2012) and Gansu (2013), showing high sequence identity (99.7%–99.9%). Compared with the genome of the HAdV-14 prototype strain (de Wit, AY803294), all three HAdV-14p1 subclades shared 99 specific nucleotide variations, including a six-nucleotide deletion in the knob domain of the fiber gene (nt751–756). The identification of subclade-specific variations and mutations unique to strain Chongqing2024-115 indicates ongoing microevolution within the HAdV-14p1 clade.

Our results confirm that the contemporary HAdV-14p1 strain shares a common genetic ancestry with earlier Chinese subclade III strains. Considering the association between HAdV-14p1 and severe disease, sustained surveillance is essential to understand better its prevalence, evolution, and impact on public health in China.

Nipah virus (NiV) is a highly lethal zoonotic pathogen. Its re-emergence in India in early 2026 has heightened global public health concerns.

This study systematically assessed the risk of NiV importation into China from five affected countries. India and Bangladesh posed moderate importation risks, whereas Malaysia, the Philippines, and Singapore presented low risks.

The findings emphasize that coupling existing defenses with sustained multi-source surveillance and dynamic risk modeling is essential to mitigate risks of stochastic NiV entry.

Carbapenem-resistant Acinetobacter baumannii (CRAB) is a well-established cause of difficult-to-treat nosocomial infections, particularly in intensive care units (ICUs). This strain is known for its ability to persist in hospital environments, leading to outbreaks.

This retrospective study analyzed five separate CRAB nosocomial infection clusters that occurred in 2016, 2019, and 2021. This revealed the molecular epidemiological links between cases and environmental samples across different years, suggesting the potential for ongoing environmental transmission. Following intensive terminal disinfection, no homologous pathogens have been detected since 2021.

The potential risk of environmental transmission suggests that the current bedside isolation strategies may have deficiencies. Given that the prevailing ICU multibedroom configuration remains unchanged, additional effective disinfection methods must be developed urgently. These techniques should specifically target high-touch irregular surfaces to disrupt persistent CRAB transmission.

The increasing trend of globalization has led to a heightened risk of imported epidemics; however, existing surveillance systems remain fragmented and reliant on laboratory confirmation. We developed an open-source data-driven hybrid modeling system to provide earlier and more reliable alerts, designed to complement China’s multipoint trigger early-warning framework.

This system integrates heterogeneous signals, including official epidemiology, digital traces, mobility, meteorology, and pathogen genomics, using semantic harmonization and a hybrid analytic stack. Seasonality-adjusted baselines with anomaly detection, mobility- and climate-aware SEIR models, and short-horizon learners generated calibrated early-warning scores. Thresholds were constrained by positive predictive value. Pilot studies were conducted for coronavirus disease 2019 (COVID-19) in Yantai and severe fever with thrombocytopenia syndrome virus (SFTSV) in Shandong and Henan, with tuberculosis indicators embedded for programmatic use.

Across deployments, the system achieved 83.3% sensitivity and 76.9% positive predictive value, providing a median lead time of 9.3 days before official confirmation. Forecasting accuracy reached 92.1% for COVID-19 in Yantai, 90.3% for SFTSV in Shandong, and 89.8% for SFTSV in Henan. Early warnings were aligned with subsequent confirmations and supported targeted screening and resource allocation.

An open-source data-driven hybrid modeling system can deliver calibrated and timely alerts across diverse pathogens. By broadening inputs, enabling cross-agency linkage, and offering operator-oriented dashboards, it serves as a practical complement to China’s national early-warning system and has the potential for scaling out with One Health inputs.