The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings

TL;DR

This paper forecasts the exponential growth of COVID-19 scientific literature in 2021 using ARIMA models, analyzing data from multiple sources and specific settings to inform infrastructure and decision-making.

Contribution

It introduces a forecasting approach for COVID-19 literature growth using ARIMA on multiple data sources and specific publication settings, providing valuable predictions for resource planning.

Findings

Significant increase in COVID-19 publications expected in 2021.

Open Access and preprint repositories show distinct growth patterns.

Forecasts aid in managing scientific information overload.

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.