BackgroundRift Valley fever (RVF) is a zoonotic mosquito-borne disease, causing high livestock morbidity and mortality, with potential human spillover. Rwanda has experienced repeated outbreaks, including significant ones in 2018 and 2022. In August 2024, a smaller localized outbreak was reported in Ngoma District, Eastern Province, providing insights into rapid detection, response, and recovery.MethodsFollowing confirmation of the index case by RT-PCR, 4062 blood samples were collected through active community and slaughterhouse surveillance. Epidemiological and demographic data were analyzed, and supportive treatment was provided to confirmed cases. A One Health response was implemented, including livestock vaccination, vector control, and coordinated surveillance.ResultsAmong sampled animals, 28 (0.69%) tested positive: 14 cattle, 9 goats, and 5 sheep. Sheep showed the highest infection rate (5.4%). Three animals died, yielding a case fatality rate of 10.7%, while 25 recovered after treatment. Positive cases clustered in six sectors near marshlands and the Akagera River. A total of 112,110 animals were vaccinated. No human cases were reported, and the outbreak was contained within 51 days.ConclusionsRapid detection, targeted treatment, and mass vaccination, implemented through a multisectoral One Health response, successfully contained the outbreak and prevented human spillover. Sustained surveillance and cross-border coordination remain essential to mitigate future RVF threats.

Despite their critical role as reservoir hosts for many zoonotic diseases, the impact of land-use and land-cover changes (LCLUC) on flying foxes' interactions with humans remains unclear, posing a potential public health risk. To address this, we apply optimal foraging theory and individual-based modelling to simulate flying-fox movement and population dynamics under various LCLUC scenarios. After validating our model against available data, we analyze the effects of agriculturalization, urbanization, forest fragmentation, and reforestation on flying-fox densities across synthetic landscapes of urban, forest, orchard, and water-body habitats. Our findings indicate that habitat disruption—particularly fragmentation through urbanization—significantly increases the risk of zoonotic spillover events by increasing contacts between species. Scenarios of forest degradation reveal that ecologically degraded forest environments can further exacerbate this risk. Additionally, we find that reforestation can alleviate spillover risk. These results underscore the importance of conservation and habitat restoration as critical strategies for mitigating zoonotic disease transmission.

The identification of novel tick-borne viruses, such as Mukawa virus (MKWV), underscores a growing need to assess their potential public health risks. In this study, we isolated the MKWV strain HLJ1 from Ixodes persulcatus ticks. While this initial isolate demonstrated limited replication in mammalian cell lines and mice, it productively infected human primary cell-derived 3D spheroids. Serial passaging in this model significantly enhanced viral titers, suggesting adaptive evolution. The resulting adapted strain exhibited increased virulence, causing pronounced cytopathic effects in Vero cells, infecting diverse mammalian cell types, and leading to 100% mortality in suckling mice, with associated liver inflammation and damage. These pathogenic outcomes were recapitulated in the 3D human liver spheroids, which showed impaired cellular synthetic functions, cell death, and heightened inflammatory responses following infection. Epidemiological screening of 145 serum samples from tick-bitten patients in Northeastern China revealed low but detectable exposure, with 1.4% positive for MKWV RNA, 4.8% for IgG antibodies, and 3.4% for neutralizing antibodies. Collectively, our findings integrate a novel human-relevant 3D culture system with field surveillance to highlight the potential risks of MKWV to human health and provide a model framework for evaluating emerging tick-borne viruses.

Highly pathogenic avian influenza (HPAI), particularly of the H5 subtype, remains a persistent threat to poultry, wildlife, and public health across Asia. This study quantifies the influence of the El Niño–Southern Oscillation (ENSO), using the Multivariate ENSO Index (MEI) as the primary predictor, on the climate-driven dynamics of H5 HPAI through region- and host-stratified generalized additive models (GAMs). Seven region–host strata across Asia were modeled separately, revealing pronounced heterogeneity in event frequency. A clear negative correlation with MEI was identified in domestic poultry across East and South Asia, where higher MEI values, corresponding to El Niño conditions, were linked to reduced event frequencies. In contrast, wild bird populations in East and South Asia displayed irregular, multimodal response patterns to MEI, suggesting phase-specific sensitivities to climate variability. A recurrent neural network (RNN) was further employed to forecast MEI trends, which were then incorporated into the GAMs to predict event dynamics. The forecasts highlighted continued epidemic pressure in East Asia's wild birds, in contrast to stable or declining trends elsewhere. Given the zoonotic potential of H5 viruses, these climate-informed risk forecasts could help inform timely interventions to prevent animal-to-human transmission and support integrated One Health preparedness frameworks. This integrative statistical–deep learning framework offers valuable support for short-term early warning and regionally targeted prevention strategies for H5 HPAI preparedness across Asia.