Search Results for « Proactive Intervention »

Abstract During the COVID-19 pandemic, outbreak prediction often failed to keep pace with the delivery of medical resources, resulting in patients not receiving timely treatment. To address this, this paper proposes an RM-SIR mathematical model and builds an intelligent emergency response system based on this model, encompassing " forecasting → decision → execution → regulation". This system enables precise intervention before an outbreak begins, ensuring timely delivery of medical supplies to affected individuals. RM-SIR model employs a self-developed urban -rural transmission differentiation decomposition algorithm to separate urban-rural transmission differences (r = 0.77) from provincial epidemic total data, enabling accurate identification of rural risk areas characterized by " low recovery rate + chronic transmission". Based on this model, a scheduling strategy and risk triggering mechanism prioritizing rural areas were designed, achieving 72-hour forward early warning and automatic generation of logistics scheduling instructions. The model innovatively establishes a dynamic function of the recovery rate γ(t)—a functional relationship between the drone delivery decision instruction u(t) and the epidemic parameter γ(t)/Re(t)—linking epidemic prediction with medical resource delivery, transforming logistics from a rudimentary " transportation carrier " into an intelligent "epidemic prevention tool". Simulation results show that the peak of the epidemic in urban areas was reduced by 94%, and the plateau period in rural areas was shortened by 100 days, with a benefit-cost ratio of 374 : 1. Robustness verification shows that even with key parameters fluctuating by ± 40%, the benefit-cost ratio still exceeds 90:1. This paper realizes the transformation from " explaining the epidemic with models " to " reshaping the epidemic with models ", providing a feasible and low-cost solution for the public health emergency response system.

The first quarter of the 21st century has witnessed the synchronous maturation of artificial intelligence (AI), digital sensing technologies, and ubiquitous computing-creating an unprecedented opportunity to transform myopia prevention and control from a reactive, population-based model into a proactive, precision-driven one. Despite mounting evidence that the global myopia burden will reach half the world's population by 2050, conventional approaches-school-based vision screening (Phase 1.0) and evidence-based risk factor management (Phase 2.0)-have proven insufficient to alter this trajectory. These limitations arise from fundamental characteristics: reactive timing that detects myopia only after onset, population-level orientation that applies uniform recommendations to heterogeneous individuals, and the absence of a continuous feedback loop between risk assessment and intervention. We review the emergence of a third paradigm-Myopia Prevention and Control 3.0-defined by the integration of AI across three interconnected domains that form a continuous, closed-loop pipeline. Stage 1 employs AI-driven risk stratification to predict individual-level risk of myopia onset and progression through machine and deep learning models trained on multimodal data. Stage 2 leverages AI-enabled proactive monitoring to continuously track behavioural, environmental, and ocular parameters via wearable sensors, smartphone applications, and school-based screening networks, detecting early signals of change. Stage 3 delivers AI-powered personalised intervention that selects, titrates, and dynamically adjusts treatment strategies through a closed-loop feedback mechanism. We critically evaluate the evidence across each stage, discuss cross-cutting challenges in data quality, model validation, ethical governance, and health equity, and outline future directions including multimodal foundation models, digital twins, and causal machine learning. By framing the 3.0 paradigm, we aim to provide a blueprint for researchers, clinicians, and policymakers seeking to transform myopia prevention into a proactive, precise, and continuously adaptive ecosystem.

So called “toxic behaviors” (e.g., hate speech, harassment, doxxing) are pervasive in online gaming communities, with research consistently documenting significant negative consequences for player wellbeing, community health, and industry revenue. While gaming studios and platforms have developed a range of reactive and proactive moderation strategies to address these harms, most existing approaches remain reactive, focusing on punishment after harm has already occurred. Community codes of conduct represent a foundational yet underutilized proactive intervention that has received comparatively little systematic attention. This literature review synthesizes existing research to address two core questions: (1) What is known about the design, implementation, and accessibility of community codes of conduct in gaming environments? and (2) How do codes of conduct influence player behavior? Drawing on a final corpus of 84 articles identified through systematic search of academic databases and supplemented by transparency reports from major gaming studios, the review maps the current landscape of both reactive and proactive moderation approaches before examining the specific role of community governance documents. Findings reveal significant gaps between current industry practice and evidence-based best practice. More than half of popular multiplayer games either lack accessible codes of conduct or bury them within legal documentation, limiting meaningful player engagement. Where codes do reach players, however, the evidence is promising: well-designed codes are associated with measurable reductions in toxic behavior, increased community engagement, and stronger community norms. Design features that enhance effectiveness include prominent placement and readability, values-centered and game-specific content, community-centered participatory development, transparent enforcement mechanisms, and integration of peer and community social influence. Despite these positive indicators, the evidence base remains limited, with most studies examining discrete platforms and short-term outcomes. This review concludes with evidence-based recommendations for industry practice, synthesized into the EFFECT Framework — a practical tool distilling six core evidence-based design principles for developing community codes of conduct that actively shape community culture. Together, these contributions call for greater research investment in how codes of conduct can be optimized as living community tools (rather than static legal documents) to meaningfully reduce toxicity and build resilient gaming communities.