Abstract
The spread of infectious diseases is highly related to the structure of human networks. Analyzing the contact network of patients can help clarify the path of virus transmission. Based on confirmed cases of COVID-19 in two major tourist provinces in southern China (Hainan and Yunnan), this study analyzed the epidemiological characteristics and dynamic contact network structure of patients in these two places. Results show that: (1) There are more female patients than males in these two districts and most are imported cases, with an average age of 45 years. Medical measures were given in less than 3 days after symptoms appeared. (2) The whole contact network of the two areas is disconnected. There are a small number of transmission chains in the network. The average values of degree centrality, betweenness centrality, and PageRank index are small. Few patients have a relatively high contact number. There is no superspreader in the network.
Highlights
Coronavirus Disease 2019 (Covid-19) has broken out in many countries around the world and drawn widespread global attention since 2019 [1, 2]
On January 30, 2020, the World Health Organization declared the epidemic a Public Health Emergency of International Concern. This virus spreads via respiratory droplets, close proximity interactions (CPIs), and airborne transmission [3, 4], with an incubation period of 1–14 days
According to the joint investigation report of COVID-19 released by China and World Health Organization in February 2020, there is little or no pre-existing immunity in most populations [5]
Summary
Coronavirus Disease 2019 (Covid-19) has broken out in many countries around the world and drawn widespread global attention since 2019 [1, 2]. On January 30, 2020, the World Health Organization declared the epidemic a Public Health Emergency of International Concern This virus spreads via respiratory droplets, close proximity interactions (CPIs), and airborne transmission [3, 4], with an incubation period of 1–14 days. This study collects detailed information about patients diagnosed with COVID-19 that are reported by the Health Commission of Hainan and Yunnan Provinces of China in Janauary and February, 2020. These two regions are major tourism provinces in China, so the epidemic has a greater impact on the local economy and society. We extract patients’ demographics and characteristics by text mining method, and construct the contact network based on patient movement tracks and contact records
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