DOI: https://doi.org/10.63345/ijrmp.org.v9.i3.1
Divya Nair
Independent Researcher
Kerela, India
Abstract
Pandemics strain healthcare systems and often trigger critical drug shortages that can jeopardize patient care. Predictive modeling has emerged as a vital tool in forecasting demand, identifying potential supply chain disruptions, and optimizing inventory management. This manuscript explores the integration of predictive analytics into drug shortage management during pandemics, reviewing literature published up to 2019 and presenting a statistical analysis of key predictive indicators. Through a mixed-methods approach, the study examines methodological frameworks, reports on simulated outcomes, and evaluates the effectiveness of predictive models in real-world pandemic scenarios. The findings suggest that incorporating predictive modeling not only improves preparedness but also facilitates proactive interventions that mitigate the impact of shortages. The discussion concludes with a reflection on the scope of predictive modeling and its inherent limitations, highlighting areas for future research.
Keywords
Predictive modeling; drug shortages; pandemics; supply chain management; healthcare analytics; forecasting; inventory optimization.
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