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COVID-19 serial interval estimates based on confirmed cases in public reports from 86 Chinese cities

Zhanwei Du1, Xiaoke Xu2, Ye Wu3,4

  • 1The University of Texas at Austin, Austin, Texas 78712, The United States of America.

Medrxiv : the Preprint Server for Health Sciences
|June 9, 2020
PubMed

Insights

This study analyzes the serial intervals of 339 COVID-19 cases in China. The findings offer crucial data for understanding coronavirus disease 2019 transmission dynamics.

Area of Science:

  • Epidemiology
  • Infectious Diseases
  • Public Health

Background:

  • The global spread of COVID-19 necessitates understanding its transmission characteristics.
  • Serial interval data is critical for modeling and controlling infectious disease outbreaks.

Approach:

  • Analysis of serial intervals from 339 confirmed COVID-19 cases across 264 cities in mainland China.
  • Data collected from cases identified prior to February 19, 2020.
  • Dataset provided in English and Chinese to facilitate global research.

Key Points:

  • The serial interval is the time between symptom onset in an infector and infectee.
  • This research quantifies the serial interval for a significant cohort of early COVID-19 cases.
  • The dataset supports epidemiological modeling and public health interventions.

Conclusions:

  • Accurate serial interval data is essential for effective COVID-19 pandemic response.
  • This study contributes valuable, publicly available data for further research.
  • Understanding transmission intervals aids in predicting and mitigating disease spread.

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