Conference paper Open Access
The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings
Torres-Salinas, Daniel;
Robinson-Garcia, Nicolas;
van Schalkwyk, François;
Nane, Gabriela F.;
Castillo-Valdivieso, Pedro
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.
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- Publication date:
- January 29, 2021
- DOI:
-
- Keyword(s):
- forecast covid-19 scientific literature scientometrics scientific growth ARIMA
- License (for files):
- Creative Commons Attribution 4.0 International