The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings Torres Salinas, Daniel Robinson García, Nicolás van Schalkwy, François F. Nane, Gabriela Castillo Valdivieso, Pedro Ángel growth models bibliometrics COVID-19 Paper submitted to the ISSI Conference 2021. We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings. 2021-02-01T09:46:04Z 2021-02-01T09:46:04Z 2021-02-01 info:eu-repo/semantics/conferenceObject http://hdl.handle.net/10481/66162 10.5281/zenodo.4478278 eng info:eu-repo/semantics/openAccess