Tiwari Agnes, So Mike K P, Chong Andy C Y, Chan Jacky N L, Chu Amanda M Y
LKS Faculty of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong, China; School of Nursing, Hong Kong Sanatorium & Hospital, Hong Kong, China.
Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Int J Infect Dis. 2021 Feb;103:97-101. doi: 10.1016/j.ijid.2020.11.143. Epub 2020 Nov 16.
The United States has become the country with the largest number of COVID-19 reported cases and deaths. This study aims to analyze the pandemic risk of COVID-19 outbreak in the US.
Time series plots of the network density, together with the daily reported confirmed COVID-19 cases and flight frequency in the five states in the US with the largest numbers of COVID-19 cases were developed to discover the trends and patterns of the pandemic connectedness of COVID-19 among the five states.
The research findings suggest that the pandemic risk of the outbreak in the US could be detected as early as the beginning of March. The signal was prior to the rapid increase of reported COVID-19 cases and flight reduction measures. Travel restriction can be strengthened at an early stage of the outbreak while more focus of local public health measures can be addressed after community spread.
The study demonstrates the application of network density on detection of pandemic risk and its relationship with air travel restriction in order to provide useful information for policymakers to better optimize timely containment strategies to mitigate the outbreak of infectious diseases.
美国已成为报告新冠病毒病例和死亡人数最多的国家。本研究旨在分析美国新冠疫情爆发的大流行风险。
绘制了美国新冠病例数最多的五个州的网络密度时间序列图，以及每日报告的新冠确诊病例数和航班频次，以发现这五个州之间新冠疫情关联性的趋势和模式。
研究结果表明，早在3月初就能检测到美国疫情爆发的大流行风险。该信号早于报告的新冠病例数快速增加和航班减少措施。在疫情爆发的早期阶段可以加强旅行限制，而在社区传播之后可以更多地关注当地公共卫生措施。
该研究展示了网络密度在检测大流行风险中的应用及其与航空旅行限制的关系，以便为政策制定者提供有用信息，以更好地优化及时的遏制策略，减轻传染病的爆发。
