Zoonotic Dis., Volume 5, Issue 3 (September 2025) – 6 articles

The 2025 avian influenza A(H5N1) outbreak has highlighted the urgent need for rapidly generated health communication materials during public health emergencies. Artificial intelligence (AI) systems offer transformative potential to accelerate content development pipelines while maintaining scientific accuracy and impact. We evaluated an AI-generated health communication material on bird flu precautions among 100 U.S. adults. The material was developed using ChatGPT for text generation based on CDC guidelines and Leonardo.AI for illustrations. Participants rated perceived message effectiveness, quality, realism, relevance, attractiveness, and visual informativeness. The AI-generated health communication material received favorable ratings across all dimensions: perceived message effectiveness (3.83/5, 77%), perceived message quality (3.84/5, 77%), realism (3.72/5, 74%), relevance (3.68/5, 74%), attractiveness (3.62/5, 74%), and visual informativeness (3.35/5 67%). Linear regression analysis revealed that all features significantly predicted perceived message effectiveness in unadjusted and adjusted models (p < 0.0001), e.g., multivariate analysis of outcome on perceived visual informativeness showed β = 0.51, 95% CI: 0.37–0.66, p < 0.0001. Also, mediation analysis revealed that visual informativeness accounted for 23.8% of the relationship between material attractiveness and perceived effectiveness. AI tools can enable real-time adaptation of prevention guidance during epidemiological emergencies while maintaining effective risk communication. Full article

Knowing the specific characteristics and animal tuberculosis risk factors present and applying good practices are crucial points in combating tuberculosis (TB) in a Mediterranean multi-species scenario. The objective of this work is to statistically analyze the association between the existence of TB in areas with a marked game–livestock interface, with various complementary factors found in 30 extensive farms in southern Portugal, such as the number of animals of each large game species present in the territory and the frequency of their sightings. Collecting this information, an inferential statistical analysis was conducted to obtain information on the association type between TB occurrence in the farms and the presence of highlighted factors. The main statistical results show an association between the presence of large game species and TB occurrence in the analyzed areas. Thus, in a multi-species scenario, large game species are a crucial component in TB maintenance, namely when stricter contact occurs. This could be one of the reasons why TB continues to circulate and why the eradication process is so difficult; the risk of zoonotic transmission is evident. It is crucial to apply biosecurity tools to improve the alignment and structure of natural resource management strategies. Full article

In Kenya, Rift Valley fever (RVF) outbreaks pose significant challenges, being one of the most severe climate-sensitive zoonoses. While machine learning (ML) techniques have shown superior performance in time series forecasting, their application in predicting disease outbreaks in Africa remains underexplored. Leveraging data from the International Livestock Research Institute (ILRI) in Kenya, this study pioneers the use of ML techniques to forecast RVF outbreaks by analyzing climate data spanning from 1981 to 2010, including ML models. Through a comprehensive analysis of ML model performance and the influence of environmental factors on RVF outbreaks, this study provides valuable insights into the intricate dynamics of disease transmission. The XGB Classifier emerged as the top-performing model, exhibiting remarkable accuracy in identifying RVF outbreak cases, with an accuracy score of 0.997310. Additionally, positive correlations were observed between various environmental variables, including rainfall, humidity, clay patterns, and RVF cases, underscoring the critical role of climatic conditions in disease spread. These findings have significant implications for public health strategies, particularly in RVF-endemic regions, where targeted surveillance and control measures are imperative. However, this study also acknowledges the limitations in model accuracy, especially in scenarios involving concurrent infections with multiple diseases, highlighting the need for ongoing research and development to address these challenges. Overall, this study contributes valuable insights to the field of disease prediction and management, paving the way for innovative solutions and improved public health outcomes in RVF-endemic areas and beyond. Full article

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne disease caused by the SFTS virus (SFTSV). A rapid and cost-effective point-of-care testing detection system is important for the early diagnosis of SFTS. Herein, we developed a ready-to-use dried reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for the direct detection of SFTSV in clinical samples. The assay enables simple, RNA-extraction-free detection using heat-treated serum or plasma, followed by a 30 min incubation at 65 °C. The results are visually interpreted through the color emitted, which can be observed under LED light. The established assay demonstrated detection sensitivity for SFTSV at 10⁴ copies/µL and was effective in identifying infections in cats. Despite being less sensitive than real-time RT-PCR, this dried RT-LAMP method offers a rapid, cost-effective alternative suitable for point-of-care use, particularly in remote or resource-limited settings. The simplified workflow and visual readout make it a practical tool for the early detection and daily surveillance of SFTSV in animals. Full article

In the wake of the COVID-19 pandemic and amid growing concerns regarding viral threats such as avian influenza, Mpox, and HKU5 bat coronaviruses, the phenomenon of viral zoonotic spillover, when viruses leap from circulation in non-human animals to humans, has garnered unprecedented global attention [...] Full article

Zoonotic diseases are a persistent public health concern, especially in low- and middle-income countries like Brazil. This cross-sectional study evaluated the knowledge and perceptions of 132 high school students (70 public and 62 private) in Goiânia, Brazil, regarding zoonoses, using a structured questionnaire. Statistical analyses (Chi-square and Fisher’s exact tests) revealed significant differences (p < 0.05) between public and private school students in knowledge levels, pet care practices, and the awareness of zoonotic risks. While pet ownership was common in both groups, only 53% of private and 21% of public school students correctly defined “zoonosis.” Rabies, taeniasis, leptospirosis, tuberculosis, cysticercosis, cutaneous larva migrans, and leishmaniasis were the most frequently cited diseases, with private school students demonstrating greater recognition across all categories. However, most participants lacked detailed knowledge about transmission routes and prevention. Misconceptions—such as zoonoses affecting only low-income populations—were also identified. Preventive actions like sanitation, public education, and vaccination were commonly suggested but not consistently linked to zoonoses. These findings highlight critical educational gaps and emphasize the need to incorporate One Health principles into school curricula to improve youth understanding and support public health strategies for zoonosis prevention. Full article