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This research study investigates the efficiency of different informationretrieval (IR) systems in accessing relevant information from the scientificliterature during the COVID-19 pandemic. The study applies the TREC frameworkto the COVID-19 Open Research Dataset (CORD-19) and evaluates BM25, Contriever,and Bag of Embeddings IR frameworks. The objective is to build a testcollection for search engines that tackle the complex information landscapeduring a pandemic. The study uses the CORD-19 dataset to train and evaluate theIR models and compares the results to those manually labeled in the TREC-COVIDIR Challenge. The results indicate that advanced IR models like BERT andContriever better retrieve relevant information during a pandemic. However, thestudy also highlights the challenges in processing large datasets and the needfor strategies to focus on abstracts or summaries. Overall, the researchhighlights the importance of effectively tailored IR systems in dealing withinformation overload during crises like COVID-19 and can guide future researchand development in this field.

IR Models and the COVID-19 Pandemic: A Comparative Study of Performance and Challenges