Accurately modelling diffusion dynamics in complex networks is essential for improving medical outcomes, guiding pandemic preparedness, and optimizing resource allocation in public health. However, existing approaches often face a trade-off between predictive performance and model interpretability, limiting their utility for clinical decision-making and strategic planning. This study presents a modular computational methodology that integrates classical compartmental models with graph neural networks (GNNs) and explainable artificial intelligence (XAI) to simulate, analyse, and interpret the spread of contagion across heterogeneous network topologies. The approach captures both structural and temporal dimensions of diffusion processes, enabling granular insights into transmission pathways. Simulations are applied to critical public health scenarios, including the identification of super-spreaders and the assessment of targeted containment strategies. By combining mechanistic models with data-driven learning and explainability techniques, the methodology supports outcome forecasting, scenario comparison, and the interpretation of network-based risk factors. Results demonstrate the ability to predict diffusion trajectories with high accuracy while preserving transparency in decision-relevant variables. The approach is intended as a generalizable tool to support medical modelling and simulation with applications ranging from epidemic control to personalized risk assessment and cost-effective intervention planning.

Orthohantavirus infections are classically associated with hemorrhagic fever with renal syndrome (HFRS) in Eurasia and hantavirus cardiopulmonary syndrome (HCPS) in the Americas. However, accumulating evidence indicates that the clinical spectrum is considerably broader, with frequent involvement of organ systems beyond the kidney and lung. Hepatic manifestations, in particular, may mimic acute viral hepatitis, leading to diagnostic challenges and underrecognition. This paper synthesizes published evidence on hepatic involvement in orthohantavirus infection, with a focus on clinical presentation, pathogenic mechanisms, differential diagnosis, biomarkers, and public health implications. Relevant literature was identified through searches of peer-reviewed articles, with emphasis on studies reporting hypertransaminasemia, hepatitis-like illness, and liver injury in confirmed hantavirus infections. Mild to moderate elevations in aminotransferases are common during acute orthohantavirus infection, and in some patients the clinical picture may be dominated by fever, thrombocytopenia, and hepatitis-like abnormalities, closely resembling dengue, leptospirosis, or classical viral hepatitis. Hepatic injury appears to result primarily from systemic endothelial dysfunction, immune-mediated inflammation, and microvascular leakage rather than direct hepatocytopathic effects. Emerging biomarkers of severity, including thrombocytopenia, neutrophil-to-lymphocyte ratio, soluble thrombomodulin, and IL-6 trans-signaling, reflect widespread vascular and inflammatory activation. Diagnostic delays are frequent, particularly in non-endemic regions, due to low clinical awareness and overlapping features with more common febrile hepatotropic syndromes. Orthohantavirus infection should be considered in the differential diagnosis of acute febrile illness with unexplained hypertransaminasemia and thrombocytopenia, especially when epidemiological clues suggest rodent exposure or compatible environmental contexts. Recognizing hepatic involvement as part of a systemic endothelial syndrome may improve diagnostic accuracy, reduce underreporting, and facilitate earlier supportive management. Increased awareness among hepatologists, infectious disease specialists, and emergency physicians is warranted.

Since the introduction of combined antiretroviral therapy, acquired immune deficiency syndrome (AIDS)-related lymphomas account for a growing proportion of deaths among people living with human immunodeficiency virus (PLWHIV). In addition to the immune deficiency caused by AIDS and other cofactors, it has been shown that circulating HIV-1 proteins play a critical role in lymphoma development. The HIV-1 matrix protein p17 (refp17) is released from infected cells and accumulates in lymph nodes of PLWHIV, even during effective pharmacological control of viral replication. Circulating refp17 deregulates the biological activity of different immune cells. Moreover, p17 variants (vp17s) characterized by peculiar amino acid insertions occurring in the C-terminal region of the protein, differently from the refp17, also induce B-cell growth and clonogenicity. Notably, vp17s were found at a significantly higher prevalence in PLWHIV with than without lymphoma. HIV-1 mutants expressing clonogenic vp17s are actively spreading, and their prevalence is globally increasing worldwide. RNA viruses exist as a population of quasi-species, transmitted from one host to another, which ultimately leads to viral evolution by generating new master sequences. Here, we developed a next-generation sequence approach to evaluate the frequency of vp17 quasi-species in PLWHIV upon time and demonstrated that the incidence of vp17s also increases at quasi-species levels. Additionally, we established a regression model capable of predicting the insertions with higher probability to be fixed, further highlighting the evolutionary relevance of the C-terminal region in the adaptation of p17 to the human host.

Hantaviruses are emerging zoonotic pathogens responsible for two severe clinical syndromes: (i) haemorrhagic fever with renal syndrome (HFRS) and (ii) hantavirus cardiopulmonary syndrome (HCPS), collectively causing more than 200,000 human cases annually worldwide. Despite their public-health importance, the molecular mechanisms governing the host response and the population-level dynamics of rodent-to-human spillover remain incompletely characterised. The timeliness of this framework is underscored by the April–May 2026 outbreak of Andes orthohantavirus aboard the MV Hondius cruise ship, the first such cluster in a maritime setting, with three deaths reported across multiple countries. This event revealed critical gaps in existing models that treat humans solely as dead-end spillover hosts. Our coupled Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model assumes no human-to-human transmission and is therefore designed for hantavirus strains where spillover does not lead to secondary human cases, specifically Hantaan virus (HTNV), Puumala virus (PUUV), Sin Nombre virus (SNV), and Dobrava-Belgrade virus (DOBV). The Andes virus (ANDV) outbreak aboard the MV Hondius is used as a real-world case study to assess the boundaries of our model and to motivate future extensions, not as a direct validation target for its quantitative predictions. Here, we present an integrated computational study combining three complementary analyses. First, we performed a preliminary phylogenetic analysis of the viral sequence, identifying Orthohantavirus andesense as the likely etiological agent responsible for the vessel-associated outbreak. Second, we carried out a downstream transcriptomic analysis of Hantaan virus (HTNV)-infected human umbilical vein endothelial cells (HUVECs), using publicly available RNA-seq data (GEO accession GSE133751, n=3 per group). This analysis identified 184 upregulated and 19 downregulated genes, highlighting a transcriptional response dominated by interferon-stimulated genes (ISGs), including CXCL10, CXCL11, MX2, DDX58, IRF7, STAT1, OASL, and CMPK2. We then constructed a protein–protein interaction (PPI) network using STRING, comprising 176 nodes and 3210 edges, and applied a composite network centrality score to rank putative regulatory hubs. This analysis identified ISG15, IRF1, CXCL10, STAT1, and DDX58 as the most central nodes. Pathway enrichment analysis confirmed a strong activation of interferon signalling (Reactome, p=1.3×10−63), antiviral defence mechanisms (Gene Ontology, p=3.8×10−58), and NF-κB-related pathways, together with a concurrent suppression of ribosomal translation. Finally, we developed a coupled SEIRD epidemiological model that explicitly represents rodent-to-rodent and rodent-to-human transmission with logistic rodent population growth. Preliminary simulation analysis demonstrates that reducing human exposure to rodent excreta is substantially more effective than rodent population control alone for reducing human disease burden, and that rodent control in isolation can paradoxically increase human cases through a dilution-like effect. The integrated framework provides molecular and epidemiological insights relevant to hantavirus surveillance, therapeutic target identification, and public-health intervention design.

In November 2024, a highly mutated descendant of the Omicron BA.3 subvariant, designated BA.3.2, emerged in South Africa carrying 39 spike mutations, two large N-terminal domain (NTD) deletions and a novel four-amino acid insertion. A key feature of BA.3.2 is extensive NTD remodeling, including a major deletion spanning residues 135–148 affecting the β-hairpin region and contributing to the loss of most of the N1 loop. This study compares the evolutionary dynamics and structural features of BA.3.2 with BA.3. Phylodynamic analyses show that BA.3 underwent early demographic stability followed by a decline in genetic diversity, consistent with limited circulation, whereas BA.3.2 displays recent emergence and a progressive reduction in effective population size without rapid expansion. Selection analyses indicate BA.3 evolution is mainly driven by changes in the receptor-binding domain, while BA.3.2 shows dispersed signals across spike regions, including codon 1162. Structural and molecular dynamic analyses reveal increased flexibility and a broader conformational landscape in the BA.3.2 NTD, driven by the deletion and resulting loss of stabilizing interactions. Overall, BA.3.2 follows a distinct evolutionary trajectory characterized by antigenic remodeling of the spike NTD, underlining the need for continued surveillance of emerging SARS-CoV-2 descendant lineages.

West Nile virus (WNV) has become an important public health concern in Europe. Italy is one of the most affected countries, yet our understanding of WNV epidemiology, genomics, and dispersal across hosts and geographic regions is incomplete. AIM: To reveal the history of WNV in Italy by integrating epidemiological, genomic, and environmental data into descriptive and quantitative assessments of its past spatio-temporal surveillance and expansion. We collated vertebrate and mosquito WNV records from national surveillance and the scientific literature spanning multiple decades. Historical serological and molecular data were summarized by host and region, climatic associations with case trends were assessed using regression models, and phylodynamic and phylogeographic analyses reconstructed viral introductions and dispersal within Italy.WNV circulation in Italy has changed markedly over time, with increasing human case reporting and expansion beyond historically affected northern regions. Climate-informed regression models explained recent reporting trends, supporting an environmental contribution to transmission. Phylodynamic analyses identified multiple independent introductions and sustained local transmission with increasing regional connectivity. Wavefront analyses revealed lineage-specific dispersal patterns associated with seasonal climatic gradients. Discrepancies between epidemiological records and genomic sampling highlighted uneven surveillance across regions and host species.WNV emergence in Italy reflects repeated viral introductions, local persistence, heterogeneous surveillance, and environmentally associated dispersal dynamics. Strengthening integrated surveillance combining epidemiological, environmental, and genomic data will improve early detection, the monitoring of transmission dynamics, and public health preparedness under ongoing environmental change. ### Competing Interest Statement The authors have declared no competing interest. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript

Background:Africa bears the highest global burden of HIV, with marked regional inequalities in prevalence, incidence and clinical outcomes. Mapping the spatial and temporal evolution of the epidemic is essential to guide targeted interventions and anticipate future trends. Methods:We conducted a retrospective analysis of UNAIDS annual estimates for adults aged 15-49 years across 49 African countries (2014-2023). We described spatiotemporal patterns in HIV prevalence, incidence, adults living with HIV (ALHIV) and AIDS-related deaths, and quantified temporal trends using annual percentage change and linear regression. For the ten highest-burden countries, we forecast prevalence to 2033 using an ensemble of machine learning models. Hierarchical and k-means clustering, supported by principal component analysis, were applied to identify epidemic archetypes based on average prevalence levels and temporal trajectories. Results:Southern Africa remained the epicentre of the epidemic, with mean adult prevalence of 19.97% versus <1.3% in Northern and Western Africa. From 2014 to 2023, prevalence and incidence declined in all regions, with the steepest reductions in Southern (prevalence -19.5%; incidence -68.4%) and Eastern Africa (-22.2% and -65.6%, respectively). Despite falling rates, the absolute number of ALHIV increased in several regions, while AIDS-related deaths decreased by more than 44% in Central and Western Africa. Forecasts for the highest-burden countries indicate a continued, gradual decline in prevalence. Cluster analysis identified a hyperendemic group of six Southern African countries (mean prevalence 15.6%) and a second cluster of 41 countries with moderate-to-low prevalence (2.1%) and mainly stable or declining trajectories. Conclusions:The African HIV epidemic is increasingly heterogeneous and evolving rather than uniformly controlled. Combining machine learning forecasts and clustering with routine surveillance can support differentiated, data-driven strategies that intensify prevention and treatment in hyperendemic settings while sustaining gains elsewhere.

Background and Objective: Accurate and timely malaria diagnosis, including species-level identification of Plasmodium , is essential for guiding effective treatment and disease management. Traditional light microscopy remains the diagnostic gold standard but relies on highly trained personnel, limiting its accessibility in resource-constrained regions. Recent advances in deep learning have enabled automated image-based diagnosis with high performance; however, accurate differentiation among Plasmodium species remains a major challenge. This study aims to evaluate and compare the effectiveness of different deep learning architectures for automated malaria species identification from microscopy images. { Methods : Three architectures were systematically assessed: a convolutional backbone (ResNet-50), a Vision Transformer (ViT), and a hybrid ResNet–ViT framework. All models were trained from scratch, without using pre-trained weights, on a dataset comprising real-world thick blood smear images augmented with publicly available microscopy data from Kaggle. The ResNet module was employed to extract robust local morphological features, while the ViT component captured long-range dependencies and contextual relationships within the images. Results: Across cross-validation experiments, all three architectures demonstrated consistently high diagnostic performance. ResNet‑50 obtained an accuracy of 95.7%, F1‑score 95.2%, and ROC‑AUC 0.997. The ViT model reached an accuracy of 92.9\%, F1‑score 92.6%, and ROC‑AUC 0.992. The hybrid ResNet–ViT achieved an accuracy of 95.2%, F1‑score 94.7%, and ROC‑AUC 0.997. These results confirm that all architectures can reliably distinguish among Plasmodium species, with the hybrid model effectively integrating local and global feature representations. Conclusions: The findings highlight that convolutional, transformer-based, and hybrid deep learning architectures can be successfully trained on real microscopy data for species-level malaria diagnosis. These results support the feasibility of implementing scalable, automated diagnostic systems to enhance accuracy and accessibility of malaria detection, particularly in resource-limited healthcare settings.

Hepatitis C virus (HCV) infection remains a major global health challenge despite the availability of highly effective direct-acting antivirals. In humanitarian and resource-constrained settings, however, HCV epidemiology may provide insights extending beyond the burden of chronic liver disease itself. A narrative review of published literature and public health reports was conducted, using searches of PubMed and Google Scholar up to May 2026, focusing on HCV epidemiology, healthcare-associated transmission, and infection prevention challenges in displaced populations. Particular attention was given to evidence from Rohingya refugee camps in Cox’s Bazar, Bangladesh, where recent studies have reported HCV seroprevalence approaching 30% and active infection rates close to 20%. Emerging data suggest that healthcare-related exposures, including medical injections and surgical procedures, may contribute to transmission in this setting. Nevertheless, direct evidence of unsafe healthcare practices remains limited, and alternative explanations, including historical exposures, informal healthcare pathways, and traditional blood-related practices, should also be considered. It was proposed that unexpectedly high HCV prevalence in populations without clearly documented behavioral risk factors may function as a sentinel epidemiological signal warranting further assessment of healthcare safety and healthcare-associated transmission pathways. In this context, screening programmes may provide information not only for case finding and treatment initiation but also for identifying potential weaknesses in infection prevention and control systems. While test-and-treat strategies remain essential, sustainable progress toward HCV elimination in humanitarian settings will likely require parallel investments in safer healthcare delivery and strengthened infection prevention measures.