Supporting data for “Estimating transmission dynamics and severity of SARS-CoV-2 Omicron following adjustment of zero-COVID policy in Shenzhen, China: a modeling study”

Name: Supporting data for “Estimating transmission dynamics and severity of SARS-CoV-2 Omicron following adjustment of zero-COVID policy in Shenzhen, China: a modeling study”
Published: 2024-12-06T01:23:55+00:00
License: https://creativecommons.org/licenses/by-nc/4.0/
Keywords: public health administration models

dataset

posted on 2024-12-06, 01:23 authored by Yichi LiYichi Li

This dataset is curated to support the research project titled Estimating transmission dynamics and severity of SARS-CoV-2 Omicron following adjustment of zero-COVID policy in Shenzhen, China: a modeling study. The study investigates the unprecedented wave of Omicron infections in mainland China during late 2022, following significant policy adjustments. Surveys from this period suggested that the cumulative infection rate surpassed 80%. However, comprehensive analyses of the transmission dynamics, and disease severity during this outbreak remain limited. By integrating various data streams, this research aims to model and evaluate the transmission dynamics and severity of Omicron infections, using Shenzhen as an example.

The dataset contains the following key components:

1. Reported Case Numbers in Shenzhen (early stage of epidemic): Daily counts of confirmed cases reported in Shenzhen during the initial phase of the outbreak, providing a initialunderstanding of the epidemic's onset and progression.

2. Online Poll Results (mid stage of epidemic): Data collected via online surveys conducted during the epidemic in Shenzhen, capturing self-reported symptoms.

3. Age-Specific Hospitalization Data (during the epidemic): Hospital admission records categorized by three age groups, offering insights into age-related vulnerabilities and the healthcare burden during the peak of the epidemic in Shenzhen.

4. Symptom-to-Hospitalization Time Intervals: Detailed data on the time intervals from symptom onset to hospitalization for patients, highlighting delays in care-seeking behavior and variations in disease progression.

5. Mobility Data for Major Cities: Comprehensive mobility patterns for Beijing, Shanghai, Guangzhou, and Shenzhen from November 2022 to January 2023, providing valuable context for understanding population movement and its potential impact on transmission dynamics.

6. Metro Passenger Volume: Data on metro ridership across more than 20 major cities in China during the same period, reflecting public transit usage trends and offering a proxy for urban mobility during the outbreak.

7. README File: A detailed guide that explains the data sources, processing methods, variable definitions, and file structures to facilitate the dataset's reproducibility and ease of use.

This dataset represents a diverse and multifaceted collection of information critical for evaluating the transmission and severity of Omicron infections during a period of substantial public health transition. It enables rigorous modeling efforts and can inform future studies on epidemic dynamics. Care has been taken to ensure that sensitive or personally identifiable information has been anonymized or removed to safeguard privacy and confidentiality.