Search Results (161)

Since the beginning of the 21st century, major epidemics and pandemics such as SARS, H1N1pdm09, Ebola, and COVID-19 have repeatedly challenged global systems of disease diagnostics and control. These crises exposed the weaknesses of traditional diagnostic models, including long turnaround times, uneven resource distribution, and supply chain bottlenecks. As a result, there is an urgent need for more advanced diagnostic technologies and integrated diagnostics strategies. Our review summarizes key lessons learned from four recent major outbreaks and highlights advances in diagnostic technologies. Among these, molecular techniques such as loop-mediated isothermal amplification (LAMP), transcription-mediated amplification (TMA), recombinase polymerase amplification (RPA), and droplet digital polymerase chain reaction (ddPCR) have demonstrated significant advantages and are increasingly becoming core components of the detection framework. Antigen testing plays a critical role in rapid screening, particularly in settings such as schools, workplaces, and communities. Serological assays provide unique value for retrospective outbreak analysis and assessing population immunity. Next-generation sequencing (NGS) has become a powerful tool for identifying novel pathogens and monitoring viral mutations. Furthermore, point-of-care testing (POCT), enhanced by miniaturization, biosensing, and artificial intelligence (AI), has extended diagnostic capacity to the front lines of epidemic control. In summary, the future of epidemic and pandemic response will not depend on a single technology, but rather on a multi-layered and complementary system. By combining laboratory diagnostics, distributed screening, and real-time monitoring, this system will form a global diagnostic network capable of rapid response, ensuring preparedness for the next global health crisis. Full article

RNA viruses remain a significant public health concern due to their rapid evolution, genetic variability, and capacity to trigger recurrent epidemics and pandemics. Over the last decade, natural products have gained attention as a valuable source of antiviral candidates, offering structural diversity, accessibility, and favorable safety profiles. This review highlights key replication mechanisms of RNA viruses and their associated therapeutic targets, including RNA-dependent RNA polymerase, viral proteases, and structural proteins mediating entry and maturation. We summarize recent advances in the identification of bioactive compounds such as flavonoids, alkaloids, terpenes, lectins, and polysaccharides that exhibit inhibitory activity against clinically relevant pathogens, including the Influenza A virus (IAV), human immunodeficiency viruses (HIV), dengue virus (DENV), Zika virus (ZIKV), and Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Special emphasis is placed on the integration of in silico screening, in vitro validation, and nanotechnology-based delivery systems that address challenges of stability, bioavailability, and specificity. Furthermore, the growing role of artificial intelligence, drug repurposing strategies, and curated antiviral databases is discussed as a means to accelerate therapeutic discovery. Despite persistent limitations in clinical translation and standardization, natural products represent a promising and sustainable platform for the development of next-generation antivirals against RNA viruses. Full article

Infection with Burkholderia pseudomallei, the causative agent of melioidosis, is uncommon in the United States (U.S.), leading to delays in pathogen identification and clinical diagnosis which can often lead to laboratory exposures. The indirect hemagglutination assay (IHA) is the primary serological test for confirming exposure to B. pseudomallei. In the U.S., a titer of ≥1:40 suggests exposure to B. pseudomallei or a closely related species, and a 4-fold rise in IHA titer ≥1:40 with clinically compatible illness is considered diagnostically probable. A retrospective analysis of 160 voluntarily reported laboratory exposure events to B. pseudomallei across 29 U.S. jurisdictions and 5 countries between 2008–2024 was conducted. This analysis included post-exposure management data and IHA results for 855 exposed laboratory personnel who had serological monitoring performed at the U.S. Centers for Disease Control and Prevention (CDC). Among exposed laboratory personnel, 105 (12%) had a seropositive titer. Of these, ninety-one (87%) laboratory personnel remained seropositive (≥1:40) at their last IHA test. Five (1%) people had a 4-fold rise in titers, though none developed melioidosis. This report underscores the need for prospective studies to evaluate seropositive laboratory personnel and to update risk guidance for laboratory exposures in non-endemic areas. Full article

Background: Thallium is a metal that is ubiquitous in our natural environment. Despite its potential for high toxicity, thallium is understudied and not regulated in food. The California Department of Public Health was alerted to a household cluster of elevated urine thallium levels noted among a mother (peak 5.6 µg/g creatinine; adult reference: ≤0.4 µg/g creatinine) and her three young children (peak 10.5 µg/g creatinine; child reference: ≤0.8 µg/g creatinine). Objectives: This case report identifies questions raised after a public health investigation linked a household’s thallium exposure to a commercially available food product. We provide an overview of the public health investigation. We then explore concerns, such as gaps in toxicological data and limited surveillance of thallium in the food supply, which make management of individual and population exposure risks challenging. Methods: We highlight findings from a cross-agency investigation, including a household exposure survey, sampling of possible environmental and dietary exposures (ICP-MS analysis measured thallium in kale chips at 1.98 mg/kg and 2.15 mg/kg), and monitoring of symptoms and urine thallium levels after the source was removed. We use regulatory and research findings to describe the challenges and opportunities in characterizing the scale of thallium in our food supply and effects of dietary exposures on health. Discussion: Thallium can bioaccumulate in our food system, particularly in brassica vegetables like kale. Thallium concentration in foods can also be affected by manufacturing processes, such as dehydration. We have limited surveillance data nationally regarding this metal in our food supply. Dietary reviews internationally show increased thallium intake in toddlers. Limited information is available about low-dose or chronic exposures, particularly among children, although emerging evidence shows that there might be risks associated at lower levels than previously thought. Improved toxicological studies are needed to guide reference doses and food safety standards. Promising action towards enhanced monitoring of thallium is being pursued by food safety agencies internationally, and research is underway to deepen our understanding of thallium toxicity. Full article

Obesity is a global epidemic with substantial health and economic impacts, making scalable weight management strategies essential. A comprehensive synthesis of eHealth interventions for weight management is needed to guide clinical practice. This umbrella review evaluated mobile and web-based interventions for weight loss in adults with overweight or obesity, compared to conventional or non-intervention controls. Systematic reviews were identified across five electronic databases from inception to February 2025. Two reviewers independently selected studies and assessed methodological quality using AMSTAR 2. Pooled estimates were calculated using random-effects models. Eleven systematic reviews (261 primary studies, 62,407 participants) were included. Mobile app interventions yielded a significant reduction in body weight (MD = −1.32 kg; I² = 82%), as did long-term eHealth interventions (MD = −1.13 kg; I² = 76%). Most meta-analyses showed high heterogeneity. Web-based interventions showed no significant effect. In conclusion, mobile apps and long-term eHealth interventions resulted in modest but statistically significant reductions in body weight, body mass index, and waist circumference. The evidence for web-based approaches remains inconclusive. Further research should focus on low-resource settings, primary care, and the integration of emerging technologies such as artificial intelligence. (PROSPERO CRD42025644218). Full article

With the advancement of Agriculture 4.0, intelligent systems and data-driven technologies offer new opportunities for pork supply-demand balance regulation, while also confronting challenges such as production cycle fluctuations and epidemic outbreaks. This paper introduces a knowledge-driven smart system for pork supply-demand regulation, which integrates essential components including a knowledge base, a mathematical-model-based expert system, an enhanced optimization framework, and a real-time feedback mechanism. Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. By harnessing dynamic decision-making capabilities of reinforcement learning (RL), we design an optimization architecture centered on the Q-learning mechanism and dual-strategy pools, which is integrated into the honey badger algorithm to form the RL-enhanced honey badger algorithm (RLEHBA). This innovation achieves an efficient balance between exploration and exploitation in model solving and improves system adaptability. Numerical experiments demonstrate RLEHBA’s superior performance over State-of-the-Art algorithms on the CEC 2017 benchmark. A case study of China’s 2026 pork regulation confirms the system’s practical value in stabilizing the supply-demand balance and optimizing resource allocation. Finally, some targeted managerial insights are proposed. This study constructs a replicable framework for intelligent livestock regulation, and it also holds transformative significance for sustainable and adaptive supply chain management in global agri-food systems. Full article

The spread of infectious diseases is inherently linked to human social behavior, characterized by complexity, diversity, and openness. Intelligent agents in computer science provide a powerful framework for capturing such dynamics, enabling complex epidemic patterns to emerge from simple local rules. These agents exhibit self-organization, adaptability, and self-optimization, making them well suited for individual-level modeling. Agent-based models (ABMs) have shown promising results in epidemic simulation and policy evaluation. However, current implementations often suffer from simplistic behavioral assumptions and rigid interaction mechanisms, limiting their realism and flexibility. This paper first reviews the current landscape of epidemic modeling approaches. It then analyzes the underlying mechanisms of advanced intelligent agents, highlighting their modeling capabilities. The study focuses on four key advantages of intelligent agent-based modeling and elaborates on three critical roles these agents play in evaluating and optimizing intervention strategies. Full article

Background: Emerging and re-emerging infectious diseases (EIDs and Re-EIDs) cause significant economic crises and public health problems worldwide. Epidemics appear to be more frequent, complex, and harder to prevent. Early warning systems can significantly reduce outbreak response times, contributing to better patient outcomes. Improving early warning systems and methods might be one of the most effective responses. This study employs a bibliometric analysis to dissect the global research hotspots and evolutionary trends in the field of infectious disease early warning, with the aim of providing guidance for optimizing public health emergency management strategies. Methods: Publications related to the role of early warning systems in detecting and responding to infectious disease outbreaks from 1999 to 2024 were retrieved from the Web of Science Core Collection (WoSCC) database. CiteSpace software was used to analyze the datasets and generate knowledge visualization maps. Results: A total of 798 relevant publications are included. The number of annual publications has sharply increased since 2000. The USA produced the highest number of publications and established the most extensive cooperation relationships. The Chinese Center for Disease Control & Prevention was the most productive institution. Drake, John M was the most prolific author, while the World Health Organization and AHMED W were the most cited authors. The top two cited references mainly focused on wastewater surveillance of SARS-CoV-2. The most common keywords were “infectious disease”, “outbreak”, “transmission”, “virus”, and “climate change”. The basic keyword “climate” ranked the first and long duration with the strongest citation burst. “SARS-CoV-2”, “One Health”, “early warning system”, “artificial intelligence (AI)”, and “wastewater-based epidemiology (WBE)” were emerging research foci. Conclusions: Over the past two decades, research on early warning of infectious diseases has focused on climate change, influenza, SARS, virus, machine learning, warning signals and systems, artificial intelligence, and so on. Current research hotspots include wastewater-based epidemiology, sewage, One Health, and artificial intelligence, as well as the early warning and monitoring of COVID-19. Research foci in this area have evolved from focusing on climate–disease interactions to pathogen monitoring systems, and ultimately to the “One Health” integrated framework. Our research findings underscore the imperative for public health policymakers to prioritize investments in real-time surveillance infrastructure, particularly wastewater-based epidemiology and AI-driven predictive models, and strengthen interdisciplinary collaboration frameworks under the One Health paradigm. Developing an integrated human–animal–environment monitoring system will serve as a critical development direction for early warning systems for epidemics. Full article

COVID-19 was one of the most serious global public health emergencies in recent years, and its extremely fast spreading speed had a profound negative impact on society. A comprehensive analysis and prediction of COVID-19 could lay a theoretical foundation for monitoring and early warning systems. Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and capturing complex nonlinear transmission patterns. We systematically reviewed COVID-19 ML prediction models developed under the background of the epidemic using the PRISMA method. We used the selected keywords to screen the relevant literature of COVID-19 prediction using ML technology from 2020 to 2023 in the Web of Science, Springer and Elsevier databases. Based on predetermined inclusion and exclusion criteria, 136 eligible studies were ultimately selected from 5731 preliminarily screened publications, and the datasets, data preprocessing, ML models, and evaluation metrics used in these studies were assessed. By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. In addition, we compared the performance of ML models with other models in the COVID-19 prediction task. The results showed that the propagation of COVID-19 is affected by multiple factors, including meteorological and socio-economic conditions. Compared to traditional methods, ML methods demonstrated significant advantages in COVID-19 prediction, especially hybrid modelling strategies, which showed great potential in optimizing accuracy. However, these techniques face challenges and limitations despite their strong performance. By reviewing existing research on COVID-19 prediction, this study provided systematic theoretical support for AI applications in infectious disease prediction and promoted technological innovation in public health. Full article

School-aged children are vulnerable to infectious diseases due to their developing immune systems and frequent social interactions. The COVID-19 pandemic underscored the importance of non-pharmaceutical interventions, like hand hygiene (HH). This study evaluated the changes achieved through a school-based intervention to Guatemalan primary school students’ HH knowledge, attitudes, and self-reported practices while collecting teacher feedback to inform future efforts. The intervention included handwashing festivals, environmental nudges, and the regular delivery of soap and alcohol-based hand rub (ABHR). Knowledge, attitudes, and practices (KAP) surveys were conducted pre- and post-intervention with 109 and 144 students, respectively. Six teachers participated in interviews to provide perspectives. Significant improvements were observed in students’ knowledge of HH’s role in preventing disease (pre: 84.4%; post: 96.5; p < 0.01) and recognition of critical moments (pre: 84.4%; post: 92.4%; p < 0.05). Self-reported practices also improved, with more students reporting washing their hands for 20 s or more (pre: 68.8%; post: 79.9%; p < 0.05). Fewer students reported liking ABHR after the intervention (pre: 89%; post: 78.5%; p < 0.05). Teachers reported increased HH practices and provided feedback to enhance interventions. These findings highlight the effectiveness of school-based interventions and emphasize the importance of addressing knowledge gaps and incorporating teacher insights for sustained public health benefits. Full article

As artificial intelligence (AI) technology continues to advance and iterate, various industries have undergone intelligent reformation. China’s animal husbandry industry, given its importance for people’s livelihoods, is no exception to this transformation. Using AI technology in this field is becoming increasingly common since it not only improves production efficiency but also revolutionizes traditional business models. Animal science is a fundamental discipline that drives the progress of animal husbandry by studying the growth, breeding, nutritional needs, and feeding management of livestock and poultry. This discipline also explores advanced veterinary theories and technologies for epidemic prevention and control. The ultimate objective of this discipline is to ensure the production of high-quality and sufficient animal products to fulfill the demands of both production and daily life. It is predicted that the deep integration of AI technology into animal science will bring unprecedented opportunities to the animal husbandry industry. This study aims to explore the impact of artificial intelligence (AI) on students’ learning experiences and future educational directions. By situating the research within the context of current developments in educational technology, we hope to provide valuable insights for educators and policymakers and employ a questionnaire survey to explore the perceptions and attitudes of students majoring in animal science from various agricultural institutions in China toward this integration. The results of the study provide valuable and practical references for the cultivation and development of artificial intelligence talent in China’s livestock industry. Full article

According to data from the World Health Organization (WHO), lung cancer is becoming a global epidemic. It is particularly high in the list of the leading causes of death not only in developed countries, but also worldwide; furthermore, it holds the leading place in terms of cancer-related mortality. Nevertheless, many breakthroughs have been made the last two decades regarding its management, with one of the most prominent being the implementation of artificial intelligence (AI) in various aspects of disease management. We included 473 papers in this thorough review, most of which have been published during the last 5–10 years, in order to describe these breakthroughs. In screening programs, AI is capable of not only detecting suspicious lung nodules in different imaging modalities—such as chest X-rays, computed tomography (CT), and positron emission tomography (PET) scans—but also discriminating between benign and malignant nodules as well, with success rates comparable to or even better than those of experienced radiologists. Furthermore, AI seems to be able to recognize biomarkers that appear in patients who may develop lung cancer, even years before this event. Moreover, it can also assist pathologists and cytologists in recognizing the type of lung tumor, as well as specific histologic or genetic markers that play a key role in treating the disease. Finally, in the treatment field, AI can guide in the development of personalized options for lung cancer patients, possibly improving their prognosis. Full article

As the epidemic affected everyone across the world, the solution to the epidemic was developed globally. Many applications adopt Internet of Things (IoT) technology to detect epidemics, and effective monitoring systems are developed to monitor air pollution, personal transmission, early detection of serious cases, and remote assessment. However, care facilities in an aging society require effective disinfection and sterilization to prevent viral transmission. We integrated the interactive and real-time features of the Internet of Things (IoT) to design and build an intelligent self-propelled sterilization robot for sterilization. Intelligent sterilization and disinfection planning and task allocation mechanisms were designed for sterilization in clinics. For healthcare facilities, the developed robot can reduce the burden on healthcare professionals, help to manage the disinfection and sterilization process, and ensure patient safety. At the same time, robots promote the development of epidemic prevention industries and prepares for future attacks from harmful air pollutants or new infections. Full article

Product supply chain systems are structurally complex infophysical systems that contain numerous unmodeled dynamics and uncertainties. Drastic fluctuations in user demand and sudden unexpected events—such as epidemics, trade wars, or cyber-attacks—can lead to changes in system structure or parameters or even destabilize the system. Designing changes within the product supply chain is an important strategy to meet user demand and maintain stable system operation. In this paper, we explore the use of artificial intelligence (AI) to enhance the analysis and control of complex product supply chain systems. We design a radial basis function neural network (RBFNN) to address Denial of Service (DoS) attacks. This RBFNN is designed to predict trends in inventory changes following a system attack and to develop optimal control strategies accordingly. First, we construct a mathematical model of the product supply chain system. Second, we leverage the predictive capability of the RBFNN to handle the effects of system changes and cyber-attacks through feed-forward compensatory control. A Linear Quadratic Regulator (LQR) is then designed under the nominal system. Finally, we verify the effectiveness of the proposed method through simulation experiments. Full article

Information on circulating HBV (sub-)genotype, variants, and hepatitis D virus (HDV) coinfection, which vary by geographical area, is crucial for the efficient control and management of HBV. We investigated the genomic characteristics of HBV (with a prevalence of 8.1%) and the prevalence of HDV in Nigeria. We utilised 777 HBV-positive samples and epidemiological data from the two-stage sampled population-based, nationally representative Nigeria HIV/AIDS Indicator and Impact Survey conducted in 2018. We assessed 732 HBV DNA-extracted samples with detectable viral loads (VLs) for (sub-)genotypes and variants by whole-genome pre-amplification, nested PCR of the s-and pol-gene, and BigDye Terminator sequencing. We conducted HDV serology. In total, 19 out of the 36 + 1 states in Nigeria had a high prevalence of HBV (≥8%), with the highest prevalence (10.4%) in the north-central geopolitical zone. Up to 33.2% (95% CI 30.0–36.6) of the participants had detectable VLs of ≥300 copies/mL. The predominant circulating HBV genotype was E with 98.4% (95% CI 97.1–99.1), followed by A with 1.6% (95% CI 0.9–2.9). Drug-resistant associated variants and immune escape variants were detected in 9.3% and 0.4%, respectively. The seroprevalence of HDV was 7.34% (95% CI 5.5–9.2). Nigeria has subtype E as the major genotype with many variants. Full article