ABSTRACT
Predicting energy consumption is an essential part of energy planning and management. The reliable prediction of regional energy consumption is crucial for the authority in China to formulate policies by with respect to the dual control of its energy consumption and energy intensity. Given that energy consumption is affected by a number of factors, this study proposes a non-homogeneous, discrete, multivariate grey prediction model based on adjacent accumulation to predict the regional energy consumption in China. Interestingly regional GDP was selected by grey relational analysis as the independent variable in the proposed model. The results show that it can outperform the other multivariate grey models considered in terms of predicting regional energy consumption in China. Moreover, we found that economic development and energy consumption of each region in China remain closely related. In the post-COVID-19 period, regional economic development will continue to grow and increase energy consumption.
ABSTRACT
With the recent ongoing autumn/winter 2022 COVID-19 wave and the adjustment of public health control measures, there have been widespread SARS-CoV-2 infections in Chinese mainland. Here we have analyzed 369 viral genomes from recently diagnosed COVID-19 patients in Shanghai, identifying a large number of sublineages of the SARS-CoV-2 Omicron family. Phylogenetic analysis, coupled with contact history tracing, revealed simultaneous community transmission of two Omicron sublineages dominating the infections in some areas of China (BA.5.2 mainly in Guangzhou and Shanghai, and BF.7 mainly in Beijing) and two highly infectious sublineages recently imported from abroad (XBB and BQ.1). Publicly available data from August 31 to November 29, 2022 indicated an overall severe/critical case rate of 0.035% nationwide, while analysis of 5706 symptomatic patients treated at the Shanghai Public Health Center between September 1 and December 26, 2022 showed that 20 cases (0.35%) without comorbidities progressed into severe/critical conditions and 153 cases (2.68%) with COVID-19-exacerbated comorbidities progressed into severe/critical conditions. These observations shall alert healthcare providers to place more resources for the treatment of severe/critical cases. Furthermore, mathematical modeling predicts this autumn/winter wave might pass through major cities in China by the end of the year, whereas some middle and western provinces and rural areas would be hit by the upcoming infection wave in mid-to-late January 2023, and the duration and magnitude of upcoming outbreak could be dramatically enhanced by the extensive travels during the Spring Festival (January 21, 2023). Altogether, these preliminary data highlight the needs to allocate resources to early diagnosis and effective treatment of severe cases and the protection of vulnerable population, especially in the rural areas, to ensure the country's smooth exit from the ongoing pandemic and accelerate socio-economic recovery.
ABSTRACT
The SARS-CoV-2 Omicron outbreak is ongoing in Shanghai, home to 25 million population. Here, we presented a novel mathematical model to evaluate the Omicron spread and Zero-COVID strategy. Our model provided important parameters, the average quarantine ratio, the detection interval from being infected to being tested positive, and the spreading coefficient to understand the epidemic progression better. Moreover, we found that the key to a relatively accurate long-term forecast was to take the variation/relaxation of the parameters into consideration based on the flexible execution of the quarantine policy. This allowed us to propose the criteria for estimating the parameters and outcome for the ending stage that is likely to take place in late May. Altogether, this model helped to give a correct mathematical appraisal of the SARS-CoV-2 Omicron outbreak under the strict Zero-COVID policy in China.