Diagnosis of COVID-19 has been challenging owing to the need for
mass testing and for combining distinct types of detection to
cover the different stages of the infection. In this review, we
have surveyed the most used methodologies for diagnosis of
COVID-19, which can be basically categorized into
genetic-material detection and immunoassays. Detection of
genetic material with real-time polymerase chain reaction
(RT-PCR) and similar techniques has been achieved with high
accuracy, but these methods are expensive and require
time-consuming protocols which are not widely available,
especially in less developed countries. Immunoassays for
detecting a few antibodies, on the other hand, have been used
for rapid, less expensive tests, but their accuracy in
diagnosing infected individuals has been limited. We have
therefore discussed the strengths and limitations of all of
these methodologies, particularly in light of the required
combination of tests owing to the long incubation periods. We
identified the bottlenecks that prevented mass testing in many
countries, and proposed strategies for further action, which are
mostly associated with materials science and chemistry. Of
special relevance are the methodologies which can be integrated
into point-of-care (POC) devices and the use of artificial
intelligence that do not require products from a well-developed
biotech industry.