CoronaCentral

CoronaCentral aggregates, categorizes, and analyzes scientific publications on SARS-CoV-2, SARS-CoV, and MERS-CoV to identify topics, research trends, and measures of publication impact.

Key Features:

• Literature Categorization: Categorizes research articles into distinct topics and article types to organize coronavirus-related publications.
• Topic Analysis: Identifies and highlights key research areas including therapeutics, disease forecasting, long COVID, and studies on inequality.
• Altmetric Integration: Integrates Altmetric data to assess the dissemination and broader impact of individual publications.
• Trend and Pace Analysis: Analyzes the pace and emphasis of research to reveal trends and emerging areas of inquiry.
• Daily Updates: Updates its corpus daily to incorporate newly published literature and impact metrics.

Scientific Applications:

• Trend Identification: Detects shifts in research focus and emerging topics within coronavirus literature.
• Gap Analysis: Reveals under-studied areas and research gaps to inform prioritization of studies and funding.
• Impact Assessment: Provides measures of publication reach and influence to support evaluation of research dissemination.
• Policy and Clinical Insight: Supplies structured evidence on literature emphasis to inform policy decisions and clinical practice considerations.

Methodology:

Applies machine learning algorithms to process and categorize large volumes of scientific literature and integrates Altmetric data to analyze publication content, pace, and impact.