In the past few years, there have been many studies addressing the simulation of COVID-19’s spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on the individual’s behavioral logic leading to changes in the overall epidemic transmission trend at larger scales. In this study, we applied Fuzzy Cognitive Maps (FCMs) to modeling individual behavioral logistics, combined with Agent-Based Modeling (ABM) to perform “Susceptible—Exposed—Infectious—Removed” (SEIR) simulation of the independent individual behavior affecting the overall trend change. Our objective was to simulate the spatiotemporal spread of diseases using the Bengaluru Urban District, India as a case study. The results show that the simulation results are highly consistent with the observed reality, in terms of trends, with a Root Mean Square Error (RMSE) value of 0.39. Notably, our approach reveals a subtle link between individual motivation and infection-recovery dynamics, highlighting how individual behavior can significantly impact broader patterns of transmission. These insights have potential implications for epidemiologic strategies and public health interventions, providing data-driven insights into behavioral impacts on epidemic spread. By integrating behavioral modeling with epidemic simulation, our study underscores the importance of considering individual and collective behavior in designing sustainable public health policies and interventions.

Management Strategy Evaluation (MSE) tools are inspired by the need for transparency, efficiency, and collaboration in harvest control rule (HCR) management. MSEs provide quantified metrics of the HCR performances and indicate the goodness in multiple dimensions, but providing HCR rankings based on such criteria is uncommon or use a simple Weight Sum Method (WSM). Acknowledging some theoretical limitations of the WSM, we propose using Technique for Order Preference by Similarity to Ideal Solution Method (TOPSIS) as an efficient alternative algorithm for recommending HCRs and conduct a sensitivity analysis of management objectives under the two frameworks, one based on simulated history and the other on the history of North Pacific Albacore (NPALB). Two conclusions are drawn based on the computation of the HCR ranking differences generated with the WSM and TOPSIS: (1) The alteration in the overall ranking of HCRs is visible, and its influence could vary substantially with user preference with theoretical merits. (2) It is common to notice shifts in the ranking for top HCRs, which potentially contributes valuable insights for practical decision making.

In February 2021, Winter Storm Uri caused widespread power outages across Texas, affecting over 5 million people and resulting in an estimated $190 billion in damages. To support extreme weather outage resilience, this study introduces the Wide-and-Deep-Based Time Sequence Algorithm (WDTSA) for predicting power outage severity. The model combines a deep bidirectional LSTM for time-lagged weather and outage history with a wide pathway for weakly temporal features, enabling synergistic integration of heterogeneous inputs. This dual pathway design significantly outperforms standard baselines, achieving 0.99 accuracy at coarse resolution (K=3) and 0.84 at fine granularity (K=15), utilizing fewer parameters than expanded LSTM alternatives. Ablation and comparative analyses confirm that the performance gains arise from specialized feature routing and non-additive synergy between input groups, growingly so under complex classification tasks. County-level visualizations during Winter Storm Uri are provided to illustrate the model’s ability to anticipate outage progression, offering actionable forecasts for emergency planning. While current validation focuses on extreme events and does not offer spatial dependency modeling, the framework provides a compact and flexible foundation for resilient grid operations and targeted response in potential weather-induced disruptions.