ABSTRACT
COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on realworld human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers.
ABSTRACT
The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0;if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods.
ABSTRACT
OBJECTIVE: To summarize the current practice and potential strategy in diagnosing coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: PubMed, Web of Science were systematically searched using terms including "COVID-19", "SARS-CoV-2" and "2019-nCoV". After removing duplicates, we then identified articles, letters and commentaries regarding diagnosing COVID-19. RESULTS: Here we summarized relatively mature diagnostic methods like nuclear acid test and computed tomography. Besides, new aspects regarding these detection methods like suitable specimens for nuclear acid test, possible use of 18F-FDG PET/CT were also reported. Especially, we also presented several novel techniques for diagnosing COVID-19 like lung ultrasound. CONCLUSIONS: Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment (7th edition) by National Health Commission is recommended to follow as it provides detailed diagnostic procedures using currently available tools. We suggest clinicians further explore the saliva's utility as a specimen for nuclear acid test and the use of lung ultrasound.