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(a) Global average temperature anomaly (1860–2014), breakpoints (1878, 1909, 1942, 1975, and 2004), partial tendencies, in °C per decade, and the linear trend (solid black line). (b) Same as (a), but for detrended global average temperature anomaly using the method piecewise linear regression.

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.

Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide. Using land use, socioeconomic, and natural hazards data, we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005, 2010, and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations. We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index (EVI), and constructed the EVI using a principal component analysis-based entropy method. A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability. The results show that: (1) for each of the study years, prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan, whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability; (2) Sichuan’s ecological vulnerability increased significantly (p = 0.011) during 2005–2010; (3) anthropogenic activities were the main factors driving variations in ecological vulnerability. These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction.