  We present an overview of the TREC-COVID Challenge, an information retrieval
(IR) shared task to evaluate search on scientific literature related to
COVID-19. The goals of TREC-COVID include the construction of a pandemic search
test collection and the evaluation of IR methods for COVID-19. The challenge
was conducted over five rounds from April to July, 2020, with participation
from 92 unique teams and 556 individual submissions. A total of 50 topics (sets
of related queries) were used in the evaluation, starting at 30 topics for
Round 1 and adding 5 new topics per round to target emerging topics at that
state of the still-emerging pandemic. This paper provides a comprehensive
overview of the structure and results of TREC-COVID. Specifically, the paper
provides details on the background, task structure, topic structure, corpus,
participation, pooling, assessment, judgments, results, top-performing systems,
lessons learned, and benchmark datasets.
