Dataset Information

Analysis of the COVID-19 Epidemic Transmission Network in Mainland China: K-Core Decomposition Study.

ABSTRACT: BACKGROUND:Since 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. OBJECTIVE:The 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. METHODS:We 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. RESULTS:Only 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. CONCLUSIONS:Increasing 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.

SUBMITTER: Qin L

PROVIDER: S-EPMC7669363 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Analysis of the COVID-19 Epidemic Transmission Network in Mainland China: K-Core Decomposition Study.

Qin Lei L Wang Yidan Y Sun Qiang Q Zhang Xiaomei X Shia Ben-Chang BC Liu Chengcheng C

JMIR public health and surveillance 20201113 4

<h4>Background</h4>Since 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.<h4>Objective</h4>The 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.<h4>Methods</h4>We first constructed a network of the ...[more]

PMID: 33108309

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Project description:BACKGROUND:When a new infectious disease emerges, appropriate case definitions are important for clinical diagnosis and for public health surveillance. Tracking case numbers over time is important to establish the speed of spread and the effectiveness of interventions. We aimed to assess whether changes in case definitions affected inferences on the transmission dynamics of coronavirus disease 2019 (COVID-19) in China. METHODS:We examined changes in the case definition for COVID-19 in mainland China during the first epidemic wave. We used exponential growth models to estimate how changes in the case definitions affected the number of cases reported each day. We then inferred how the epidemic curve would have appeared if the same case definition had been used throughout the epidemic. FINDINGS:From Jan 15 to March 3, 2020, seven versions of the case definition for COVID-19 were issued by the National Health Commission in China. We estimated that when the case definitions were changed, the proportion of infections being detected as cases increased by 7·1 times (95% credible interval [CrI] 4·8-10·9) from version 1 to 2, 2·8 times (1·9-4·2) from version 2 to 4, and 4·2 times (2·6-7·3) from version 4 to 5. If the fifth version of the case definition had been applied throughout the outbreak with sufficient testing capacity, we estimated that by Feb 20, 2020, there would have been 232?000 (95% CrI 161?000-359?000) confirmed cases in China as opposed to the 55?508 confirmed cases reported. INTERPRETATION:The case definition was initially narrow and was gradually broadened to allow detection of more cases as knowledge increased, particularly milder cases and those without epidemiological links to Wuhan, China, or other known cases. These changes should be taken into account when making inferences on epidemic growth rates and doubling times, and therefore on the reproductive number, to avoid bias. FUNDING:Health and Medical Research Fund, Hong Kong.

Dataset Information

Analysis of the COVID-19 Epidemic Transmission Network in Mainland China: K-Core Decomposition Study.

Publications

Analysis of the COVID-19 Epidemic Transmission Network in Mainland China: K-Core Decomposition Study.

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