Machine learning maps research needs in COVID-19 literature

The impact of the COVID-19 pandemic has led scientists to produce a vast quantity of research aimed at understanding, monitoring, and containing the disease; however, it remains unclear whether the research that has been produced to date sufficiently addresses existing knowledge gaps. We use artificial intelligence (AI)/machine learning techniques to analyze this massive amount of information at scale. We find key discrepancies between literature about COVID-19 and what we would expect based on research on other coronaviruses. These discrepancies—namely, the lack of basic microbiological research, which is often expensive and time-consuming—may negatively impact efforts to mitigate the pandemic and raise questions regarding the research community's ability to quickly respond to future crises. Continually measuring what is being produced, both now and in the future, is key to making better resource allocation and goal prioritization decisions as a society moving forward.