Dataset Information

Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China.

ABSTRACT:

Objectives

In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China.

Methods

From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces.

Results

School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic.

Conclusions

Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic.

SUBMITTER: Fu H

PROVIDER: S-EPMC7603985 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China.

Fu Han H Wang Haowei H Xi Xiaoyue X Boonyasiri Adhiratha A Wang Yuanrong Y Hinsley Wes W Fraser Keith J KJ McCabe Ruth R Olivera Mesa Daniela D Skarp Janetta J Ledda Alice A Dewé Tamsin T Dighe Amy A Winskill Peter P van Elsland Sabine L SL Ainslie Kylie E C KEC Baguelin Marc M Bhatt Samir S Boyd Olivia O Brazeau Nicholas F NF Cattarino Lorenzo L Charles Giovanni G Coupland Helen H Cucunuba Zulma M ZM Cuomo-Dannenburg Gina G Donnelly Christl A CA Dorigatti Ilaria I Eales Oliver D OD FitzJohn Richard G RG Flaxman Seth S Gaythorpe Katy A M KAM Ghani Azra C AC Green William D WD Hamlet Arran A Hauck Katharina K Haw David J DJ Jeffrey Benjamin B Laydon Daniel J DJ Lees John A JA Mellan Thomas T Mishra Swapnil S Nedjati-Gilani Gemma G Nouvellet Pierre P Okell Lucy L Parag Kris V KV Ragonnet-Cronin Manon M Riley Steven S Schmit Nora N Thompson Hayley A HA Unwin H Juliette T HJT Verity Robert R Vollmer Michaela A C MAC Volz Erik E Walker Patrick G T PGT Walters Caroline E CE Watson Oliver J OJ Whittaker Charles C Whittles Lilith K LK Imai Natsuko N Bhatia Sangeeta S Ferguson Neil M NM

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 20201031

<h4>Objectives</h4>In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China.<h4>Methods</h4>From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive anal ...[more]

PMID: 33130212

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Project description:BackgroundAs of March 18, 2020, 13 415 confirmed cases and 120 deaths related to coronavirus disease 2019 (COVID-19) in mainland China, outside Hubei province-the epicentre of the outbreak-had been reported. Since late January, massive public health interventions have been implemented nationwide to contain the outbreak. We provide an impact assessment of the transmissibility and severity of COVID-19 during the first wave in mainland Chinese locations outside Hubei.MethodsWe estimated the instantaneous reproduction number (Rt) of COVID-19 in Beijing, Shanghai, Shenzhen, Wenzhou, and the ten Chinese provinces that had the highest number of confirmed COVID-19 cases; and the confirmed case-fatality risk (cCFR) in Beijing, Shanghai, Shenzhen, and Wenzhou, and all 31 Chinese provinces. We used a susceptible-infectious-recovered model to show the potential effects of relaxing containment measures after the first wave of infection, in anticipation of a possible second wave.FindingsIn all selected cities and provinces, the Rt decreased substantially since Jan 23, when control measures were implemented, and have since remained below 1. The cCFR outside Hubei was 0·98% (95% CI 0·82-1·16), which was almost five times lower than that in Hubei (5·91%, 5·73-6·09). Relaxing the interventions (resulting in Rt >1) when the epidemic size was still small would increase the cumulative case count exponentially as a function of relaxation duration, even if aggressive interventions could subsequently push disease prevalence back to the baseline level.InterpretationThe first wave of COVID-19 outside of Hubei has abated because of aggressive non-pharmaceutical interventions. However, given the substantial risk of viral reintroduction, particularly from overseas importation, close monitoring of Rt and cCFR is needed to inform strategies against a potential second wave to achieve an optimal balance between health and economic protection.FundingHealth and Medical Research Fund, Hong Kong, China.

Project description:BackgroundSince the outbreak of COVID-19 in December 2019 in Wuhan, Hubei Province, China, frequent interregional contacts and the high rate of infection spread have catalyzed the formation of an epidemic network.ObjectiveThe aim of this study was to identify influential nodes and highlight the hidden structural properties of the COVID-19 epidemic network, which we believe is central to prevention and control of the epidemic.MethodsWe first constructed a network of the COVID-19 epidemic among 31 provinces in mainland China; after some basic characteristics were revealed by the degree distribution, the k-core decomposition method was employed to provide static and dynamic evidence to determine the influential nodes and hierarchical structure. We then exhibited the influence power of the above nodes and the evolution of this power.ResultsOnly a small fraction of the provinces studied showed relatively strong outward or inward epidemic transmission effects. The three provinces of Hubei, Beijing, and Guangzhou showed the highest out-degrees, and the three highest in-degrees were observed for the provinces of Beijing, Henan, and Liaoning. In terms of the hierarchical structure of the COVID-19 epidemic network over the whole period, more than half of the 31 provinces were located in the innermost core. Considering the correlation of the characteristics and coreness of each province, we identified some significant negative and positive factors. Specific to the dynamic transmission process of the COVID-19 epidemic, three provinces of Anhui, Beijing, and Guangdong always showed the highest coreness from the third to the sixth week; meanwhile, Hubei Province maintained the highest coreness until the fifth week and then suddenly dropped to the lowest in the sixth week. We also found that the out-strengths of the innermost nodes were greater than their in-strengths before January 27, 2020, at which point a reversal occurred.ConclusionsIncreasing our understanding of how epidemic networks form and function may help reduce the damaging effects of COVID-19 in China as well as in other countries and territories worldwide.

Dataset Information

Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China.

Objectives

Methods

Results

Conclusions

Publications

Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China.

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