Background: Opioid overdose incidence in Myanmar has not been systematically measured. We quantified individual risk factors of overdose, overdose incidence and mortality, and examined predictors of pre‑overdose engagement with harm reduction services.

Methods: Electronic registers from 21 Asian Harm Reduction Network–Best Shelter (AHRN–BS) sites in Sagaing, Kachin, and northern Shan (2014–2025) were linked. Incidence and mortality were calculated with the exact Poisson method. A retrospective cohort of 155,875 clients—65,089 people who inject drugs—was analyzed with modified Poisson regression to estimate adjusted prevalence ratios (aPR) for overdose; cases occurring in 2022–2025 were further analyzed for predictors of past‑year service engagement.

Results: Registers captured 3472 verified opioid overdoses in 2913 persons. Among people who inject drugs, pooled incidence was 16.8/1000 person‑years (95 % CI 16.2–17.5) and overdose mortality 0.5/1000 person‑years (0.4–0.7). Current injecting without opioid‑agonist therapy (OAT) carried an aPR 8.1 (95 % CI 6.1–10.6) relative to recent non‑injecting use, rising to aPR 10.7 (95 % CI 7.8–14.8) among those who also received OAT. Three‑quarters of overdoses (2598) occurred in persons not enrolled in harm reduction programs; incidence among enrolled clients was 5.7/1000 person-years (95 % CI 5.2–6.2). Migrants were less likely to have accessed services in the year preceding overdose (aPR 0.82, 95 % CI 0.68–0.98). Alcohol was reported in 70.5 % of events and stimulants in 7.2 %.

Discussion: Opioid overdose is common and disproportionately affects people outside existing programs. Expanding peer‑led and mobile outreach, tailoring referral pathways for migrants, broadening community naloxone distribution, and securing sustained domestic financing are urgent priorities.

Quantizing netwoifying the significance of ties in preserving network connectivity is crucial for identifying weak ties, which often serve as bridges between communities, and for detecting community structures. However, accurately characterrk connectivity and formalizing the relationship between weak ties and communities remain challenging. In this study, we introduce hierarchy-based link centrality (HLC), a novel metric based on the dissimilarity between the original network and its contracted version, where the terminal nodes of links merge and connect to all their neighbors. This dissimilarity is quantified by variations in the network hierarchy, specifically the nodal distance distributions. In addition to the experiments on weak tie identification and link-based network disintegration, we develop a link-based community detection (LCD) approach that focuses on optimal link ranking to elucidate community structures. Experiments across various networks demonstrate that HLC excels in identifying weak ties, achieving a 2.9% higher accuracy than the second-best metric. It also outperforms others in detecting critical link combinations for network disintegration, reducing the average size of the giant connected component by 7.2% compared to the suboptimal counterpart. Furthermore, HLC enhances community detection, achieving optimal partitioning with an average 5.7% improvement in modularity over five other indices. These results highlight the effectiveness of HLC in quantifying weak ties and suggest broad applications for this innovative approach in network analysis.

While link redundancy has long been acknowledged as a critical factor in network robustness, current approaches frequently neglect the inherent heterogeneity of structure, thereby falling short of optimal network disintegration. This study introduces the novel Neighborhood Dissimilarity Community Heterogeneity (NDCH) framework, which systematically investigates redundancy elimination from a new perspective of neighborhood dissimilarity, while concurrently integrating structural heterogeneity derived from community structure. Extensive experiments on both synthetic and diverse real-world networks reveal that strategies developed under NDCH framework markedly outperform existing state-of-the-art methods in network disintegration, with performance improvements reaching up to 60.151% and 31.000% for Schneider R and the critical removal fraction f_c, respectively. Notably, additional analysis consistently indicates low correlations (Kendall’s Tau < 0.6) and distinct value distributions between NDCH-based strategies and the state-of-the-art approaches. In summary, the novel framework underscores the significance of both redundancy and structural heterogeneity when devising network disintegration strategies, offering a substantial leap forward in enhancing network robustness against malicious attacks and various disruptions, especially in infrastructure networks.

Understanding the dynamics of spreading and diffusion on networks is of critical importance for a variety of processes in real life. However, predicting the temporal evolution of diffusion on networks remains challenging as the process is shaped by network topology, spreading non-linearities, and heterogeneous adaptation behavior. In this study, we propose the ‘spindle vector’, a new network topological feature, which shapes nodes according to the distance from the root node. The spindle vector captures the relative order of nodes in diffusion propagation, thus allowing us to approximate the spatiotemporal evolution of diffusion dynamics on networks. The approximation simplifies the detailed connections of node pairs by only focusing on the nodal count within individual layers and the interlayer connections, seeking a compromise between efficiency and complexity. Through experiments on various networks, we show that our method outperforms the state-of-the-art on BA networks with an average improvement of 38.6% on the mean absolute error. Additionally, the predictive accuracy of our method exhibits a notable convergence with the pairwise approximation approach with the increasing presence of quadrangles and pentagons in WS networks. The new metric provides a general and computationally efficient approach to predict network diffusion problems and is of potential for a large range of network applications.

Structure-based influential nodes identification is a long-term challenge in the study of complex networks. While global centrality-based approaches are generally considered to be more accurate and reliable, the requirements of complete network information and high computational complexity are hard to meet, limiting their applications in many practical scenarios. In addition, recent studies have highlighted the effect of cyclic structures introducing redundant paths in network connectivity and exaggerating the importance of traditional centrality measures. In this work, we develop a new centrality metric, called Multi-Spanning Tree-based Degree Centrality (MSTDC), to quantify node importance with linear complexity by leveraging redundant ties. MSTDC is calculated using the aggregation of degrees of a small number of spanning trees constructed with a few randomly selected root nodes. Experiments on synthetic and empirical networks reveal that MSTDC obtains superior performance than other benchmark network centralities in identifying influential nodes from the perspective of both maintaining network connectivity and maximizing spreading capacity. In addition, we find that MSTDC is extraordinarily effective in networks with high clustering coefficients. Our study provides novel insights into the role of redundant ties in network structural and functional analyses.

Antimicrobial-resistant organisms (AMROs) can colonize people without symptoms for long periods of time, during which these agents can spread unnoticed to other patients in healthcare systems. The accurate identification of asymptomatic spreaders of AMRO in hospital settings is essential for supporting the design of interventions against healthcare-associated infections (HAIs). However, this task remains challenging because of limited observations of colonization and the complicated transmission dynamics occurring within hospitals and the broader community. Here, we study the transmission of methicillin-resistant Staphylococcus aureus (MRSA), a prevalent AMRO, in 66 Swedish hospitals and healthcare facilities with inpatients using a data-driven, agent-based model informed by deidentified real-world hospitalization records. Combining the transmission model, patient-to-patient contact networks, and sparse observations of colonization, we develop and validate an individual-level inference approach that estimates the colonization probability of individual hospitalized patients. For both model-simulated and historical outbreaks, the proposed method supports the more accurate identification of asymptomatic MRSA carriers than other traditional approaches. In addition, in silica control experiments indicate that interventions targeted to inpatients with a high-colonization probability outperform heuristic strategies informed by hospitalization history and contact tracing.

Misreporting is a common source of bias in population surveys involving sensitive topics such as sexual behaviours, abortion or criminal activity. To protect their privacy due to stigmatized or illegal behaviour, respondents tend to avoid fully disclosure of personal information deemed sensitive. This attitude however may compromise the results of survey studies. To circumvent this limitation, this article proposes a novel ego-centric sampling method (ECM) based on the respondent's peer networks to make indirect inferences on sensitive traits anonymously. Other than asking the respondents to report directly on their own behaviour, ECM takes into account the knowledge the respondents have about their social contacts in the target population. By using various scenarios and sensitive analysis on model and real populations, we show the high performance, that is low biases, that can be achieved using our method and the novel estimator. The method is also applied on a real-world survey to study traits of college students. This real-world exercise illustrates that the method is easy-to-implement, requiring few amendments to standard sampling protocols, and provides a high level of confidence on privacy among respondents. The exercise revealed that students tend to under-report their own sensitive and stigmatized traits, such as their sexual orientation. Little or no difference was observed in reporting non-sensitive traits. Altogether, our results indicate that ECM is a promising method able to encourage survey participation and reduce bias due to misreporting of sensitive traits through indirect and anonymous data collection.

Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection. Increasing efforts have been taken to mitigate the epidemics and to avoid potential outbreaks in low endemic settings. Understanding the population dynamics of MRSA is essential to identify the causal mechanisms driving the epidemics and to generalise conclusions to different contexts. Previous studies neglected the temporal structure of contacts between patients and assumed homogeneous behaviour. We developed a high-resolution data-driven contact network model of interactions between 743,182 patients in 485 hospitals during 3,059 days to reproduce the exact contact sequences of the hospital population. Our model captures the exact spatial and temporal human contact behaviour and the dynamics of referrals within and between wards and hospitals at a large scale, revealing highly heterogeneous contact and mobility patterns of individual patients. A simulation exercise of epidemic spread shows that heterogeneous contacts cause the emergence of super-spreader patients, slower than exponential polynomial growth of the prevalence, and fast epidemic spread between wards and hospitals. In our simulated scenarios, screening upon hospital admittance is potentially more effective than reducing infection probability to reduce the final outbreak size. Our findings are useful to understand not only MRSA spread but also other hospital-acquired infections.

During epidemic control, containment of the disease is usually achieved through increasing a devoted resource to reduce the infectiousness. However, the impact of this resource expenditure has not been studied quantitatively. For disease spread, the recovery rate can be positively correlated with the average amount of resource devoted to infected individuals. By incorporating this relation we build a novel model and find that insufficient resource leads to an abrupt increase in the infected population size, which is in marked contrast with the continuous phase transitions believed previously. Counterintuitively, this abrupt phase transition is more pronounced in less contagious diseases. Furthermore, we find that even for a single infection source, the public resource needs to be available in a significant amount, which is proportional to the total population size, to ensure epidemic containment. Our findings provide a theoretical foundation for efficient epidemic containment strategies in the early stage.