Jul 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 47 references
Computer Science
TL;DR
The study supports the potential usefulness of combining contact-based models with explainable graph neural networks for scenario-based epidemic analysis and suggests that the k-GCN model captures relevant temporal and structural dependencies in the simulated graph-organized data.
Since mid-2023, Shenzhen has experienced continuous local transmission of monkeypox, primarily among men who have sex with men (MSM). Understanding how the virus spreads within this high-risk group is essential for designing targeted public health interventions. This study develops a Susceptible-Exposed-Infectious-Removed (SEIR) compartmental model to analyze monkeypox transmission dynamics in Shenzhen. Key epidemiological parameters, including the basic reproduction number (R₀), transmission rate, and recovery rate, are derived from real-world data from Shenzhen's 2023 outbreak, as reported in published epidemiological investigations and genomic surveillance studies. The model incorporates the unique characteristics of monkeypox, such as an incubation period of approximately 7-14 days and sexual contact as the primary transmission route within the MSM population. Numerical simulations conducted using MATLAB examine three intervention scenarios: no intervention, moderate public health response, and strong intervention. The findings indicate that without effective control measures, the basic reproduction number exceeds the critical threshold, leading to sustained transmission. However, early caseidentification combined with targeted public awareness campaigns can reduce R₀ below unity, effectively containing the outbreak. These results provide evidence-based guidance for monkeypox prevention and control strategies in Shenzhen and other metropolitan areas facing similar risks.
Yicheng Li· Applied and Computational En...· 0 citations
BACKGROUND
Yellow fever virus (YFV) has caused substantial human disease in Brazil, driven by spillover from outbreaks in nearby non-human primates (NHPs). As disease surveillance in NHPs is challenging, the impact of environmental conditions on YFV outbreaks in NHPs remains poorly understood. In this study, we aimed to use joint inference modelling to overcome these obstacles by integrating diverse data streams and to estimate YFV transmission dynamics in NHPs to enhance understanding of yellow fever disease ecology.
METHODS
We applied a multistage analysis using epidemiological, phylogenetic, demographic, and meteorological data in conjunction with mechanistic and statistical modelling. We used EpiFusion joint inference models to infer YFV infection dynamics in NHPs from 2015 to 2019, and statistical generalised additive models to test hypothesised associations between NHP infections and environmental variables and human cases.
FINDINGS
The effective reproduction number (Rt) of YFV in NHPs was approximately 1·0, indicating that small changes in transmissibility can enable explosive sylvatic outbreaks. We also found the possibility of large, entirely unobserved outbreaks in NHPs. El Niño sea surface temperature anomalies at medium-term and long-term lags were important predictors of YFV infection dynamics in NHPs. Finally, we found that the risk of YFV spillover to humans, measured in terms of the effect of NHP infections on human cases, was correlated with the number of NHP infections, with a 7-day to 21-day lag.
INTERPRETATION
YFV might be establishing recurrent transmission in NHP populations in southeast Brazil, indicating that more comprehensive and consistent surveillance is needed. Identifying key environmental drivers and their lags with human YFV cases can be used to develop early warning systems to guide reactive vaccination and public health campaigns to prevent future outbreaks.
FUNDING
None.
C. Judge, F. Iani, E. Finch et al.· Lancet Planetary Health· 0 citations
Dengue fever has emerged as one of the most devastating vector-borne diseases in Bangladesh. Understanding the mechanisms driving transmission is therefore a scientific and public-health priority. This study presents a comprehensive and methodologically robust analysis of the 2023 dengue epidemic in Bangladesh by integrating epidemiological data with advanced numerical modeling. It formulates the transmission dynamics using the classical SIR (Susceptible–Infected–Recovered) framework and benchmark its performance against the logistic growth model, thereby revealing the fundamental differences between mechanistic and phenomenological approaches. Recognizing that the SIR system lacks a closed-form analytical solution, it employs a suite of high-accuracy numerical solvers—including Taylor’s Series Method, RK2, and RK4 to faithfully capture the nonlinear transmission process. Using real-world infection and mortality data from IEDCR (2023), it simulates reproduce the full epidemic arc with high fidelity, identifying the critical peak and the subsequent downturn induced by susceptible depletion and rising immunity. Comparative evaluation demonstrates that the logistic model, while useful for approximating cumulative trends, is structurally incapable of capturing core epidemic mechanisms. In contrast, the numerically solved SIR model delivers superior predictive realism, mechanistic transparency, and epidemiological interpretability. This work underscores the indispensable role of rigorous mathematical modeling in guiding dengue preparedness, optimizing control strategies, and strengthening epidemic response capacity in resource-limited settings.
Jagannath University Journal of Science, Volume 12, Number 1, Jun. 2025, pp. 29−38
Abstract Background The 2022 global mpox epidemic declined before modified Vaccinia Ankara–Bavarian Nordic (MVA-BN) vaccines were widely deployed, contributing to the perception that transmission was self-limiting within a small high-risk population. This may have delayed global vaccine allocation and weakened responses to the resurgence that has disproportionately affected Africa since 2024. The reasons for the 2022 decline remain uncertain, and prior studies have reported widely divergent estimates of vaccination impact. We systematically reviewed and meta-analysed the evolving transmissibility of mpox clades and the effects of interventions. Methods We searched GenBank, PubMed and Embase through 18 May 2025, for mpox virus sequences, reproduction number (R) estimates and modelling studies evaluating intervention effectiveness. Two reviewers independently assessed eligibility, extracted data and evaluated risk of bias using a validated tool. Random-effects meta-analyses were performed. Results Fifty-two studies reporting R estimates and 40 studies evaluating intervention effectiveness met eligibility criteria. For clade I mpox, pooled R increased from 0.71 (95% CI 0.26 to 1.17) during 1970–2017 to 1.23 (1.12–1.33) for subclades Ib/Ia after 2023 (p=0.0303). For clade II mpox, pooled R was 1.11 (0.90–1.32) during 2017–2021 and increased to 2.66 (2.31–3.00) for subclade IIb in 2022–2023 (p<0.0001). During the 2022 subclade IIb outbreak, empirical modelling studies estimated that behaviour change and vaccination together were associated with a substantial reduction in mpox cases (55%, 95% CI 37 to 73; I²=81.2%), although effect sizes varied across settings according to the extent of behaviour change and the timing and coverage of vaccine rollout. Conclusions Behaviour change and vaccination likely played important roles in the decline of the 2022 mpox epidemic in many studied settings. Given the stepwise increase in human-to-human transmissibility associated with the emergence of new subclades, effective mpox epidemic control requires proactive, rapid and equitable vaccine rollout supported by culturally tailored risk communication. PROSPERO registration number CRD420250653072.
Yin-Chien Lin, Tzai-Hung Wen, W. Shih et al.· BMJ Global Health· 0 citations
Highly pathogenic avian influenza (HPAI) is a devastating viral disease causing substantial economic losses in the poultry industry and posing potential zoonotic risks. Located along the East Asian–Australasian Flyway (EAAF), South Korea has experienced recurrent outbreaks of HPAI since 2003. Following the severe 2016–2017 epidemic, the government implemented strengthened control measures, including restrictions on duck farming and organizational restructuring. This study quantitatively evaluated structural changes in the spatiotemporal patterns and transmission dynamics of HPAI before and after the 2017 policy reinforcement, utilizing a complete dataset covering 12 epidemic waves between 2003 and 2025. Our analysis suggests a distinct shift in HPAI occurrence patterns from large‐scale, clustered epidemics to more sporadic occurrences in the post‐2017 period. Pre‐2017 epidemics, particularly the 5th and 6th waves, exhibited intense spatiotemporal clustering and high transmission potential. Conversely, post‐2017 epidemics showed a significant reduction in outbreak density and the disappearance of large‐scale clusters. Notably, the 12th wave displayed a more circular diffusion pattern with outbreaks confined to specific regions, suggesting relatively more geographically contained spread. However, despite the overall reduction in scale, high spatiotemporal interaction intensity was intermittently observed, such as in the 11th wave, indicating that residual risks of explosive local transmission persist even during smaller epidemics. These findings suggest that the post‐2017 pattern was temporally consistent with strengthened control policies aimed at reducing mechanical connectivity between farms, although this ecological analysis cannot separate policy effects from other time‐varying epidemiological and surveillance‐related factors. Nevertheless, a decline in case numbers does not necessarily imply the elimination of local transmission risk, highlighting the need to advance precise and risk‐based surveillance and response strategies to effectively manage residual risks.
Sung Dae Park, J. Son, Dae-Sung Yoo· Transboundary and Emerging D...· 0 citations
Background A dramatic increase in dengue infections has been observed in recent years, raising concerns regarding the potential further spread of dengue. Shenzhen, a major international port city in China, is typically a non-endemic region; however, it faces persistent risks from imported cases. The resurgence of imported risk has made the identification of effective prevention and control strategies a pressing public health priority for the region. Methodology We integrated epidemic, environmental, and intervention data from Shenzhen covering the period from 2015 to 2023. A compartmental mathematical model was developed to characterize the transmission dynamics of dengue triggered by imported cases. We applied sensitivity analysis and developed a quantitative metric to evaluate the relative efficacy of various non-pharmaceutical intervention strategies in this specific urban context. Findings Our sensitivity analysis identified vector control and the reduction of mosquito biting rates as the most critical factors influencing transmission dynamics. These analytical results were further validated through simulations based on a model fitted to historical data, which revealed that prioritizing these strategies significantly mitigated dengue transmission risks. Compared to alternative measures, these targeted interventions demonstrated a substantially higher impact on reducing the risk and scale of local outbreaks. Conclusions This study provides a practical, evidence-based tool for health authorities to prioritize interventions in at-risk hub cities. Our findings underscore that for non-endemic port cities like Shenzhen, focusing on rigorous vector management and biting rate reduction is critical for mitigating the risk of dengue epidemics. These insights offer a strategic framework for guiding future epidemic control efforts against vector-borne diseases in non-endemic urban environments.
Qi Tan, J. Wan, Cong Niu et al.· PLoS Neglected Tropical Dise...· 0 citations
