@misc{10481/66162,
year = {2021},
month = {2},
url = {http://hdl.handle.net/10481/66162},
abstract = {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.},
keywords = {growth models},
keywords = {bibliometrics},
keywords = {COVID-19},
title = {The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings},
doi = {10.5281/zenodo.4478278},
author = {Torres Salinas, Daniel and Robinson García, Nicolás and van Schalkwy, François and F. Nane, Gabriela and Castillo Valdivieso, Pedro Ángel},
}