Data Analytics for Pandemic Management using MapReduce and Apriori Algorithm

Abstract

Amidst the profound impact of the COVID-19 pandemic on global economies and healthcare systems, effective data analysis has become paramount. Our research paper, titled ”Data Analytics for Pandemic Management Using MapReduce and Apriori Algo- rithm,” presents a comprehensive framework to analyze pandemic data. We harness the power of the MapReduce and Apriori algorithms, with a parallel processing model achieving an average speedup of 50%. This approach involves data collection, pre- processing, and algorithmic application to extract valuable insights from pandemic-related data. Notably, our findings reveal a substantial 22.29% support rate for ”n95 masks,” indicating high demand. Additionally, we identify strong co-occurrence patterns, exemplified by the perfect support rate of 1.00 between ”n95 masks” and ”chloroquine,” highlighting their interconnectedness. Our framework goes beyond data analysis, enhancing personalized marketing, and optimizing inventory management for efficient resource allocation during crises. We also uncover robust associations, such as the 0.220 confidence in joint purchases of ”butyl rubber gloves” and ”surgical masks”.Hence, the research aligns seamlessly with the pressing need for effective pandemic data management and response strategies. By validating our approach through numerical insights, we aim to contribute to mitigating pandemic-related challenges and support global efforts to better prepare for and manage future health emergencies.