ABSTRACT
Background: Coronavirus disease 2019 (COVID-19) was first reported in Wuhan, Hubei Province, China. We aimed to describe the temporal and spatial distribution and the transmission dynamics of COVID-19 and to assess whether a hybrid model can forecast the trend of COVID-19 in Hubei Province.Method: The data of COVID-19 cases were obtained from the website of the Chinese Center for Disease Control and Prevention, whereas the data on the resident population were obtained from the website of the Hubei Provincial Bureau of Statistics. The temporal and spatial distribution and the transmission dynamics of COVID-19 were described. A combination of an autoregressive integrated moving average (ARIMA) and a support vector machine (SVM) was constructed to forecast the trend of COVID-19.Results: A total of 56,062 confirmed COVID-19 cases, which were mainly concentrated in Wuhan, were reported from 16 January to 16 March 2020 in Hubei Province. The daily number of confirmed cases exponentially increased to 3,156 before 4 February 2020, fluctuated on an upward trend to 4,823 before 13 February 2020, and then markedly decreased to one case after 16 March 2020. The highest mean reproduction number R(t) of 9.48 was recorded on 16 January 2020, after which it decreased to 2.15 on 2 February 2020 and further dropped to less than one on 13 February 2020. In the modelling stage, the mean square error, mean absolute error and mean absolute percentage error of the hybrid ARIMA–SVM model decreased by 98.59%, 89.19% and 89.68%, and those of SVM decreased by 98.58%, 87.71% and 88.94% compared with the ARIMA model. Similar results were obtained in the forecasting stage.Conclusion: Public health interventions resulted in the terminal phase of COVID-19 in Hubei Province. The hybrid ARIMA–SVM model may be a reliable tool for forecasting the trend of the COVID-19 epidemic.
Subject(s)
COVID-19 , Q FeverABSTRACT
COVID-19 has outbreaked in Wuhan city, Hubei province of China since December 30th 2019, and spread nationwide and widely spilled over to other countries around the world that has been declared a public health emergency. However, there is no specific drug for the treatment of the disease. Therefore, identifying effective antiviral drugs to combat the disease is urgently needed. Angiotensin converting enzyme 2 (ACE2) has become the promising target to discovery new antiviral drugs to treat COVID-19, we have attempted to discover novel ACE2 inhibitors through ligand-based virtual screening. Finally, eight compounds were selected and tested ACE2 kinase inhibitory assay using fluorescence assays method. The results showed that four compounds (monoammonium glycyrrhizinate, glycyrrhizic acid methyl ester, ginsenoside Rg6 and ginsenoside F1) from 101 kinds of Chinese medicinal and edible plants which could inhibit ACE2 activity in vitro. Further efforts on chemical modification of these lead compounds are undergoing can lead to discover better agents against COVID-19.