The growth of COVID-19 scientific literature

A forecast analysis of different daily time series in specific settings

Conference Paper (2021)
Author(s)

Daniel Torres-Salinas (Universidad de Granada)

Nicolás Robinson-García (TU Delft - Applied Probability)

François van Schalkwyk (Private Bag X1)

G. F. Nane (TU Delft - Applied Probability)

Pedro Castillo-Valdivieso (Universidad de Granada)

Research Group
Applied Probability
More Info
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Publication Year
2021
Language
English
Research Group
Applied Probability
Pages (from-to)
1121-1130
ISBN (electronic)
9789080328228

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 the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: type of access (Open Access), NLM source (PubMed and PMC), and domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings.

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