Abstract
The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases’ degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.
Highlights
IntroductionTo understand the evolution, prevention, and control of the epidemic, it is vital to analyze the spatial and temporal co-occurrence of confirmed cases
The contact network analysis based on real-world transmission data can effectively capture the dynamic transmission process of viruses among individuals and reflect the development of the epidemic through visualization and network modeling [6,7,8,9,10]
The number of family member infections showed a significant increase in February 2020, probably because many cases had already been infected with COVID-19 during the Chinese New Year but diagnosed in early
Summary
To understand the evolution, prevention, and control of the epidemic, it is vital to analyze the spatial and temporal co-occurrence of confirmed cases. Using real-world data on disease transmission is crucial during the early stages of an epidemic to ensure the accuracy and efficacy of nonpharmacological interventions. To achieve this goal, the contact network analysis based on real-world transmission data can effectively capture the dynamic transmission process of viruses among individuals and reflect the development of the epidemic through visualization and network modeling [6,7,8,9,10]. The study of contact relationships between patients at the beginning of the outbreak of COVID-19 can help the prevention of possible future outbreaks
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