Hospitalized patients are particularly susceptible to infections brought on by methicillin-resistant Staphylococcus aureus (MRSA), and these infections usually have a high morbidity and mortality rate. Several strategies can help prevent MRSA from spreading, but the efficacy of each strategy remains unclear, as most past research evaluated multiple interventions together rather than examining each one individually. To assess the effects of hand hygiene and screening, we created a novel compartmental MRSA spread framework for healthcare facilities in Edmonton, Alberta. The model accounts for MRSA patients (hospital-acquired (HA) MRSA or community-acquired (CA) MRSA colonization or infections) and isolated patients. To examine the effects of interventions, we first estimated the posterior mean of the parameters in our model, as well as a sub-model of it, using a Bayesian framework with Markov chain Monte Carlo (MCMC) techniques, utilizing data from hospitals in Edmonton. Next, we incorporated different intervention rates into the model to ascertain their effect on colonized and infected patients. One intervention is practicing hand hygiene among healthcare workers in hospitals to reduce transmission rates between susceptible individuals and MRSA patients. The other intervention is to screen patients for MRSA colonization to place them in isolation rooms swiftly. The two interventions were first examined alone, and then simultaneously. We compared the overall number of HA-MRSA colonization and infections as the intervention rates varied, and the findings show that combined interventions are more effective than individual interventions at limiting MRSA transmission in hospitals.
Tuberculosis (TB) remains a major public health burden in Kyrgyzstan; however, the seasonal transmission dynamics of TB in the country have not been quantitatively characterized. This study aimed to develop a seasonal dynamic model to simulate TB transmission, estimate the basic reproduction number R0, assess transmission intensity, and inform evidence–based prevention and control strategies. Using monthly reported new TB cases from the national TB surveillance and registration system in Kyrgyzstan from 2022 to 2025, we constructed a seasonal SEIR model incorporating periodic transmission and reactivation rates. Model parameters were estimated using a Bayesian Markov chain Monte Carlo (MCMC) method. R0 was calculated using two methods: the periodic time–varying method and the annual average transmission rate method. Local sensitivity analysis was performed to evaluate the impact of the baseline transmission parameter as and the baseline reactivation parameter ae on TB transmission dynamics. The model adequately captured the seasonal fluctuations and overall downward trend in new TB cases, achieving a mean absolute percentage error (MAPE) of 8.66% and a root mean square percentage error (RMSPE) of 9.95%. The estimated periodic R0 was 0.8842 (95% credible interval [CrI]: 0.8811–0.8873), which remained below the threshold of unity. Assuming no change in current prevention and control measures, projections suggest a gradual decline in monthly new TB cases, with the annual number of new cases declining to approximately 2902 by 2126. These results indicate that TB transmission has not been fully interrupted and that elimination is unlikely to be achieved within the projection period. Sensitivity analysis revealed that both as and ae substantially affected the number of new TB cases and R0, and that modest changes in these parameters could drive R0 above the threshold of unity. The seasonal SEIR model provided a good fit to the monthly number of new TB cases in Kyrgyzstan. Although the estimated R0 is below the threshold of unity, indicating a declining epidemic trend, the rate of decline remains relatively slow, highlighting the need to sustain and strengthen prevention and control measures.
The dissemination of epidemic-related information can effectively enhance public awareness of preventive measures and decrease infection risks. However, information dissemination through multiple channels, including both online and offline sources, and the dynamics of information dissemination across these channels are influenced by individual mobility. To address this gap, this study develops a three-layer metapopulation network coupled model that integrates online information dissemination, epidemic spreading, and offline information dissemination. The proposed model systematically investigates the patterns of information dissemination through both online and offline channels, as well as the epidemic reaction–diffusion process driven by individual migration. Furthermore, the social interactions dynamics of both information channels are influenced by migration, manifested as migrated individuals acquire offline information in destination patches and establish online connections with individuals residing there. Theoretical analysis using the Microscopic Markov Chain Approach (MMCA) derives epidemic thresholds, while Monte Carlo (MC) simulations validate the model and examine factors influencing propagation dynamics. Results indicate that migration enhances individuals’ offline information acquisition, and accelerates cross-patch online information dissemination. Both online and offline information interactions exert inhibitory effects on epidemic spread, effectively increasing the infection threshold and reducing the outbreak scale. Furthermore, increasing the connectivity and dissemination rate of online information, particularly the information dissemination via weak relationships between patches, can effectively inhibit epidemic outbreaks. Notably, when the suppression effect of information on the epidemic is weak, population migration facilitates epidemic spread. In contrast, when the suppression effect is strong, migration-driven information dissemination significantly reduces the probability of infection, thereby further suppressing the outbreak.
Accurate influenza forecasting is essential for public health preparedness, yet many models require future covariates, provide limited interpretability, and degrade under post-pandemic regime shifts. We propose a Stage-Aware Multimodal Neural Network (SAMNN) that integrates multimodal temporal features using configurations adapted to heterogeneous transmission regimes and supports season-ahead scenario forecasting using adversarially synthesized covariates. Using 13 years of national A/H1N1 surveillance data from mainland China (2011∼2024), SAMNN was evaluated across three epidemiologically distinct seasons spanning ∼20-fold differences in peak intensity. SAMNN achieved R 2 values of 0.82-0.95, 0.82-0.97, and 0.55-0.97 for 1-week-, 2-week-, and 4-week-ahead forecasting, respectively, and generally maintained competitive performance relative to five baseline models across forecast horizons. SHAP attribution showed that epidemiological signals dominated predictions, with context-dependent contributions from climatic and social-context features. To support prospective scenario forecasting, we used an adversarial synthetic covariate-generation pipeline to produce season-ahead forecasts for 2024/25 using information available at the prespecified forecast origin.
Background:Antimicrobial resistance (AMR) is typically interpreted as a process of gradual accumulation of resistance traits. However, resistance systems may instead undergo structural transitions under changing selective pressures. We investigated whether long-term AMR dynamics in Corynebacterium striatum reflect accumulation or reorganization of a constrained phenotypic state space. Methods:Longitudinal resistance data (2013-2025) were analyzed using segmented regression, multinomial modeling, entropy-based diversity metrics, and Markov transition analysis to characterize temporal dynamics, architecture distributions, and system-level behavior. Results:A significant structural breakpoint was identified around 2020 (Davies' test p < 0.001). The estimated breakpoint location showed limited uncertainty based on model-based confidence interval estimation. Prior to 2020, aminoglycoside-containing resistance architectures were prevalent, whereas after 2020 they collapsed and were replaced by aminoglycoside-negative, fluoroquinolone-associated configurations. Phenotypic diversity declined significantly over time, indicating contraction of the resistance state space. Markov analysis demonstrated convergence toward a stable stationary distribution dominated by two related architectures, with increased dynamical stability after the breakpoint. Conclusions:AMR dynamics in C. striatum reflect a structural transition and restructuring within the simplified state space rather than progressive accumulation of resistance traits. The system converges toward a low-diversity phenotypic attractor, highlighting the importance of dynamical systems approaches in understanding resistance evolution.
