Suchergebnisse

Different countries have adopted various control measures for the COVID-19 pandemic in different periods, and as the virus continues to mutate, the progression of the pandemic and preventive measures adopted have varied dynamically over time. Thus, quantitative analysis of the dynamic impact of different factors such as vaccination, mutant virus, social isolation, etc., on transmission and predicting pandemic progress has become a difficult task. To overcome the challenges above and enable governments to formulate reasonable countermeasures against the ongoing COVID-19 pandemic, we integrate several mathematical methods and propose a new adaptive multifactorial and geographically diverse epidemiological model based on a modified version of the classical susceptible-exposed-infectious-recovered (SEIR) model. Based on public datasets, a multi-center study was carried out considering 21 regions. First, a retrospective study was conducted to predict the number of infections over the next 30 days in 13 representative pandemic areas worldwide with an accuracy of 87.53%, confirming the robustness of the proposed model. Second, the impact of three scenarios on COVID-19 was quantified based on the scalability of the model: two different vaccination regimens were analyzed, and it was found that the number of infections would progressively decrease over time after vaccination; variant virus caused a 301.55% increase in infections in the United Kingdom; and 3-tier social lockdown in the United Kingdom reduced the infections by 47.01%. Third, we made short-term prospective predictions for the next 15 and 30 days for six countries with severe COVID-19 transmission and the predicted trend is accurate. This study is expected to inform public health responses. Code and data are publicly available at https://github.com/yuanyuanpei7/covid-19.

Abstract. Ground subsidence is regarded as one of the most common
geohazards, accompanied with the rapid urban expansion in recent years. In
the last 2 decades, Wuhan, located in the alluvial Jianghan Plain, has
experienced great urban expansion with increased subsidence issues, i.e.,
soft foundation subsidence and karst collapse. Here we investigated
subsidence rates in Wuhan with 2015–2019 Sentinel-1 synthetic aperture radar (SAR) images. We found that the overall subsidence over the Wuhan region is significantly correlated with the distribution of engineering geological subregions (EGSs). We further validated the interferometric SAR (InSAR) measurements with better than 5 mm accuracy by comparing with leveling measurements. Subsidence centers in Qingling–Jiangdi, Houhu, Qingshan, and Dongxihu were identified with
displacement rates of approximately 30 mm/yr. Our results demonstrated that the dominant driving factor is ongoing construction, and the fact that the subsidence centers shifted with construction intensities. The Qingling–Jiangdi area in our study is a well-known site of karst collapse. We find that the nonlinear subsidence of this area is correlated with the seasonal rainfall.