Highlights
The observed dynamics of Covid-19 pandemic in various countries shows that the daily growth rate of new infection cases has tendency to decrease linearly when the quarantine is imposed in a country until it reaches constant value corresponding to the effectiveness of quarantine measures.
Approach to forecast Covid-19 evolution and estimate the effectiveness of quarantine measures is developed on the basis of the daily growth rate of new infection cases.
Various countries are compared in context of the developed approach.
A simplified model of Covid-19 epidemic dynamics under quarantine conditions and method to estimate quarantine effectiveness are developed. The model is based on the growth rate of new infections when total number of infections is significantly smaller than population size of infected country or region. The model is developed on the basis of collected epidemiological data of Covid19 pandemic, which shows that the growth rate of new infections has tendency to decrease linearly when the quarantine is imposed in a country (or a region) until it reaches constant value, which corresponds to the effectiveness of quarantine measures taken in the country. The growth rate of new infections can be used as criteria to estimate quarantine effectiveness.
The discrete element method (DEM) is the method of choice in many cases of simulation and analysis of processes in granular matter, providing the data at the level of individual particles. In most applications and experiments, however, the bulk behavior is of interest; therefore, larger scale structures must be identified from the DEM simulation data. We present the method for detection of particle groups involved in collective motion based on network analysis. Knowing the positions and velocities of individual particles, a “velocity similarity graph” is built, where the graph vertices represent the particles. The vertex pairs are connected by the edge if the distance between the respective particles is small enough. The edge weight is calculated to be inversely proportional to the difference in the respective particle velocities, that is, the vertex pairs representing nearby particles having similar velocities are connected by edges of larger weight. If a group of particles moves in a coordinated matter, the particles in this group will have similar velocities; therefore, the corresponding vertices in the graph will be connected by edges of larger weight in the representing graph. Having produced the velocity similarity graph, identification of particle groups becomes equivalent to the problem of “community detection” in graph analysis. The algorithms and techniques developed for community detection in graphs can be thereby applied for identification of particle groups involved in coordinated motion in granular matter. We illustrate this approach by an example of granular media filled in a rotating cylinder.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.