  With worldwide concerns surrounding the Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific
literature on the virus. Clinicians, researchers, and policy-makers need to be
able to search these articles effectively. In this work, we present a zero-shot
ranking algorithm that adapts to COVID-related scientific literature. Our
approach filters training data from another collection down to medical-related
queries, uses a neural re-ranking model pre-trained on scientific text
(SciBERT), and filters the target document collection. This approach ranks top
among zero-shot methods on the TREC COVID Round 1 leaderboard, and exhibits a
P@5 of 0.80 and an nDCG@10 of 0.68 when evaluated on both Round 1 and 2
judgments. Despite not relying on TREC-COVID data, our method outperforms
models that do. As one of the first search methods to thoroughly evaluate
COVID-19 search, we hope that this serves as a strong baseline and helps in the
global crisis.
