ABSTRACT
Covid-19 is a serious disease for human. It can be easily spread between human. In order to model the spread of Covid-19 and determinate the appropriate policy by government, I use the SEIRD model, which is extended from SIR model. In this paper, the SEIRD model studies the transmissibility of Covid-19 in China. This work first gives out the flowchart of the SEIRD model and then I derive the differential equation and find out the disease-free equilibrium based on the flowchart. Then I calculate the generation matrix and basic reproduction number which is directly related to the transmissibility of the virus. At last, the sensitivity analysis analyzes the different impact from different parameter. From that, we can find out the best way to control the transmission. The result is that the parameter that refer to strictness has a great impact on the spread of Covid-19. However, it doesn't have to be as large as possible since the covid can be well controlled with an appropriate value of strictness and smallest negative effect for people. This paper tries to find out the best extent of strictness of policy that is able to control the transmission. © 2023 SPIE.
ABSTRACT
In order to enhance the ability to diagnose and distinguish COVID-19 from ordinary pneumonia, and to assist medical staff in chest X-ray detection of pneumonia patients, this paper proposes a COVID-19 X-ray image detection algorithm based on deep learning network. First of all, a model of deep learning network is set up based on VGG - 16, and then, the network structure and parameter optimization is adjusted, which makes the network model can be applied to COVID - 19 x ray imaging detection task. In the end, through adjusting the image size of the original data set, the input data meets the requirements of the deep learning network. Experimental results show that the proposed algorithm can effectively learn the characteristics of the COVID-19 X-ray image data set and accurately detect three states of COVID-19, common viral pneumonia and non-pneumonia, with a very high detection accuracy of 95.8%. © 2023 SPIE.
ABSTRACT
The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused severe damage to the world. With the support of classic evolutionary theories and population genetics principles, many studies on the origin of SARS-CoV-2 have revealed encouraging results but meanwhile some are still under debate. We are concerned with the validity of applying classic evolutionary theories and formula to the evolution of RNA viruses. We have raised several confounding factors like the RNA replication feature and the RNA modification systems of the hosts, which might jeopardize the validity of the application of classic methods to analyze the SARS-CoV-2 data. FAU - Li, Yue
ABSTRACT
Background: In this study, we preliminarily investigated the mechanism of Yin-Chai-Xiao-Du decoction for the treatment of COVID-19 by the method of network pharmacology. Methods: The potential targets and pathways of Yin-Chai-Xiao-Du decoction for the treatment of COVID-19 were examined using network pharmacology;the ingredient and active targets of Yin-Chai-Xiao-Du decoction were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and PharmMapper databases;the COVID-19-related targets were obtained from the online Mendelian inheritance in man, GeneCards, and GeneMANIA databases;the STRING database and Cytoscape were used to build a protein-protein interaction network, and a Network Analyzer tool was used to perform topology analysis to screen for the key ingredients and targets;the ClueGO and KOBAS 3.0 databases were for the enrichment analysis of gene function (Gene Oncology) and gene pathway (Kyoto Encyclopedia of Genes and Genomes);the herb-ingredient-target-pathway network diagram was constructed by Cytoscape. Results: The core herbs screened by the network pharmacological analysis were Jinyinhua (Lonicerae japonicae flos), Lianqiao (Forsythia suspensa), Chaihu (Bupleuri radix), Huangqin (Scutellariae radix), Yinchen (Herba Artemisiae Scopariae), Guanghuoxiang (Pogostemonis herba), Roudoukou (Semen myristicae) and Qinghao (Artemisiae annuae herba). A total of 293 active ingredients were screened by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, and the key ingredients were quercetin, kaempferol, isorhamnetin, stigmasterol, beta-sitosterol, and luteolin. Yin-Chai-Xiao-Du decoction has 138 COVID-19-related targets, and the key targets were mitogen-activated protein kinase 3, interleukin-6, tumor necrosis factor, vascular endothelial growth factor A, and CC motif ligand 2. Kyoto Encyclopedia of Genes and Genomes analysis revealed 120 enriched gene pathways, and the key pathways were signaling by interleukins, immune system, cytokine signaling in the immune system, and the signaling pathways of interleukin-17, tumor necrosis factor, and relaxin. Conclusion: The core herbs of Yin-Chai-Xiao-Du decoction are Jinyinhua (Lonicerae japonicae flos), Lianqiao (Forsythia suspensa), Chaihu (Bupleuri radix), Huangqin (Scutellariae radix), Yinchen (Herba Artemisiae Scopariae), Guanghuoxiang (Pogostemonis herba), Roudoukou (Semen myristicae) and Qinghao (Artemisiae annuae herba). The key ingredients are quercetin, kaempferol, isorhamnetin, stigmasterol, and beta-sitosterol;the critical targets are luteolin, interleukin-6, mitogen-activated protein kinase 3, tumor necrosis factor, and CC motif ligand 2;and the core signaling pathways are those mediated by interleukin-17, tumor necrosis factor, and relaxin.