Predictive and Proactive Socio-Health Modelling of Navaratri Festival Activities Across India, Pakistan, Sri Lanka, Nepal and Bangladesh An Interdisciplinary Mathematical Framework for Mental Health, Physical Well-Being, Social Cohesion, Commercial Activity and Festival Management
Keywords:
Navaratri, predictive modelling, proactive modelling, artificial intelligence, machine learning, fuzzy logic, mental health, physical health, social well-being, commercial modelling, managerial decision-making, South Asia, India, Pakistan, Sri Lanka, Nepal, Bangladesh.Abstract
Navaratri is a major South Asian festival associated with religious observance, community participation, music, dance, fasting, social interaction, cultural expression and substantial commercial activity. Although the intensity and form of celebration vary considerably across regions and communities, large-scale festival participation can simultaneously generate positive outcomes—such as physical activity, social connectedness, cultural identity, emotional well-being and local economic stimulation—and potential risks, including sleep disruption, dehydration, nutritional imbalance, fatigue, noise exposure, crowd-related stress, traffic congestion and excessive expenditure. This paper proposes a Predictive-Proactive Navaratri Socio-Health Management Model (PPNSHMM) integrating mathematical modelling, artificial intelligence, machine learning, fuzzy logic, behavioural analytics, socioeconomic indicators and managerial decision systems. The framework considers five interconnected dimensions: physical health, mental health, social well-being, commercial/economic activity and managerial sustainability. A comparative South Asian extension is proposed for India, Pakistan, Sri Lanka, Nepal and Bangladesh, while recognizing that Navaratri is not celebrated with identical cultural intensity or significance across these countries and communities. The proposed model transforms festival-related observations into a multidimensional Festival Well-Being and Risk Index (FWRI) and a Predictive Festival Management Index (PFMI). The system can forecast crowd pressure, fatigue, dehydration risk, psychosocial stress, social participation, commercial demand and resource requirements. Fuzzy and machine-learning components allow uncertainty, linguistic perceptions and nonlinear interactions to be incorporated. The framework is intended as a decision-support system rather than a diagnostic medical instrument. Its principal objective is to promote culturally respectful, evidence-based and preventive festival management.
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