The first phase of the novel coronavirus disease (COVID-19) that emerged at the end of 2019 has been brought under control in the mainland of China in March, while it is still spreading globally. When the pandemic will end is a question of great concern. A logistic model that depicts the growth rules of infected and recovered cases in China's mainland may shed some light on this question. This model well explained the data by 13 April from 31 countries that have been experiencing serious COVID-2019 outbreaks (R 2 C 0.95). Based on this model, the semi-saturation period (SSP) of infected cases in those countries ranges from 3 March to 18 June. According to the linear relationship between the growth rules for infected and for recovered cases identified from the Chinese data, we predicted that the SSP of the recovered cases outside China ranges from 22 March to 8 July. More importantly, we found a strong positive correlation between the SSP of infected cases and the timing of a government's response. Finally, this model was also applied to four regions that went through other coronavirus or Ebola virus epidemics (R 2 C 0.95). There is a negative correlation between the death rate and the logistic growth rate. These findings provide strong evidence for the effectiveness of rapid epidemic control measures in various countries.
The typhoon is one major threat to human societies and natural ecosystems, and its risk perception is crucial for contextualizing and managing disaster risks in different social settings. Social media data are a new data source for studying risk perception, because such data are timely, widely distributed, and sensitive to emergencies. However, few studies have focused on crowd sensitivity variation in social media data-based typhoon risk perception. Based on the regional disaster system theory, a framework of analysis for crowd risk perception was established to explore the feasibility of using social media data for typhoon risk perception analysis and crowd sensitivity variation. The goal was to quantitatively analyze the impact of hazard intensity and social and geographical environments on risk perception and its variation among population groups. Taking the Sina Weibo data during Typhoon Lekima of 2019 as an example, we found that: (1) Typhoon Lekima-related Weibo public attention changed in accordance with the evolution of the typhoon track and the number of Weibo posts shows a significantly positive correlation with disaster losses, while socioeconomic factors, including population, gross domestic product, and land area, are not explanatory factors of the spatial distribution of disaster-related Weibo posts; (2) Females, nonlocals with travel plans, and people living in areas with high hazard intensity, low elevation, or near waterbodies affected by Lekima paid more attention to the typhoon disaster; and (3) Descriptions of rainfall intensity by females are closer to the meteorological observation data.
Intensified tropical cyclones (TCs) threaten the socioeconomic development of coastal cities. The coupling of strong wind and precipitation with the TC process usually amplifies the destructive effects of storms. Currently, an integrated analytical framework for TC hazard assessment at the city level that combines the joint statistical characteristics of multiple TC-induced hazards and local environmental features does not exist. In this study, we developed a novel hazard assessment framework with a high spatiotemporal resolution that includes a fine-tuned K-means algorithm for clustering TC tracks and a Copula model to depict the wind–precipitation joint probability distribution of different TC categories. High-resolution wind and precipitation data were used to conduct an empirical study in Shenzhen, a coastal megacity in Guangdong Province, China. The results show that the probabilities of TC-induced wind speed and precipitation exhibit significant spatial heterogeneity in Shenzhen, which can be explained by the characteristics of TC tracks and terrain environment factors. In general, the hazard intensity of TCs landing from the west side is higher than that from the east side, and the greatest TC intensity appears on the southeast coast of Shenzhen, implying that more disaster prevention efforts are needed. The proposed TC hazard assessment method provides a solid base for highly precise risk assessment at the city level.
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