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Compared to previous SARS-CoV-2 variants, the Omicron variant exhibited different epidemiological features. The purpose of this study was to assess the wave dynamics of pre-Omicron and Omicron waves in terms of differences and similarities. We investigated the COVID-19 waves since the beginning of the pandemic up to 28 August 2022, 1000 waves in total, as to their effectiveness for flattening the curve, calculated from the first and second time derivative of the daily case data. The average number of Omicron waves per month (42.78) was greater than the one of pre-Omicron waves per month (25.62). Omicron waves steepen and flatten the curve significantly faster, more effectively and with sharper peaks. Omicron waves generated more daily case data than pre-Omicron waves; the pre-Omicron trend showed increasing numbers over time, whereas the Omicron trend showed decreasing numbers. In denser populated countries, pre-Omicron waves were managed more effectively, in contrast to Omicron waves which were managed less effectively (but more effectively in less densely populated countries). This study provides the evidence for a different behaviour of Omicron waves in terms of wave dynamics, and thereby confirms that the Omicron variant is not only genetically different but even more so in terms of epidemiological dynamics. [ABSTRACT FROM AUTHOR]

With rapid urbanization and economic development, artificial lighting at night brings convenience to human life but also causes a considerable urban environmental pollution issue. This study employed the Mann-Kendall non-parametric test, nighttime light indices, and the standard deviation method to investigate the spatio-temporal characteristics of artificial lighting in the Beijing-Tianjin-Hebei region. Moreover, nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System, socioeconomic data, and high-resolution satellite images were combined to comprehensively explore the driving factors of urban artificial lighting change. The results showed the following: (1) Overall, there was an increasing trend in artificial lighting in the Beijing-Tianjin-Hebei region, which accounted for approximately 56.87% of the total study area. (2) The change in artificial lighting in the entire area was relatively stable. The artificial lighting in the northwest area changed faster than that in the southeast area, and the areas where artificial lighting changed the most were Beijing, Tianjin and Tangshan. (3) The fastest growth of artificial lighting was in Chengde and Zhangjiakou, where the rates of increase were 334% and 251%, respectively. The spatial heterogeneity of artificial lighting in economically developed cities was higher than that in economically underdeveloped cities such as Chengde and Zhangjiakou. (4) Multi-source data were combined to analyse the driving factors of urban artificial lighting in the entire area. The Average Population of Districts under City ( R 2 = 0.77) had the strongest effect on artificial lighting. Total Passenger Traffic ( R 2 = 0.54) had the most non-obvious effect. At different city levels, driving factors varied with differences of economy, geographical location, and the industrial structures of cities. Urban expansion, transportation hubs, and industries were the major reasons for the significant change in nighttime light. Urban artificial lighting represents a trend of overuse closely related to nighttime light pollution. This study of artificial lighting contributes to the rational planning of urban lighting systems, the prevention and control of nighttime light pollution, and the creation of liveable and ecologically green cities.