Contact tracing reveals community transmission of COVID-19 in New York City

Publication Details

Authors: S Pei, S Kandula, J Cascante Vega, W Yang, JK Varma, et al.

Year: 2022

Source: Nature Communications

Publisher: nature.com

Citations: 18

Citations per Year: 6.0

Google Scholar Rank: 64

Author Count: 5

Abstract

Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission patterns has been challenging due to limited data availability. This comprehensive analysis uses contact tracing data to reveal community transmission patterns of COVID-19 in New York City, providing critical insights into transmission dynamics, superspreading events, and community-level spread patterns that informed public health policy and intervention strategies during the pandemic.

Key Findings

• Novel use of contact tracing data to analyze community transmission patterns
• Identification of superspreading events and transmission cluster characteristics
• Community-level transmission dynamics and risk factor analysis
• Evidence-based insights for tailoring public health interventions to local context
• Methodological framework for analyzing contact tracing data in pandemic response

Research Impact

This innovative study (18 citations) provided critical insights into COVID-19 community transmission patterns using contact tracing data, contributing to understanding of transmission dynamics and informing targeted public health interventions during the pandemic.

Publication Access

Full Text: Nature Communications

Related Articles: Related Research

Citation Information: Google Scholar Citations

Related context: the COVID-19 hub and the contact tracing glossary entry.