Datta, AmitavaWinkelstein, PeterSen, Surajit
14页查看更多>>摘要：We introduce a novel agent based model where each agent carries an effective viral load that captures the instantaneous state of infection of the agent. We simulate the spread of a pandemic and subsequently validate it by using publicly available COVID-19 data. Our simulation tracks the temporal evolution of a virtual city or community of agents in terms of contracting infection, recovering asymptomatically, or getting hospitalized. The virtual community is divided into family groups with 2-6 individuals in each group. Agents interact with other agents in virtual public places like at grocery stores, on public transportation and in offices. We initially seed the virtual community with a very small number of infected individuals and then monitor the disease spread and hospitalization over a period of fifty days, which is a reasonable time-frame for the initial spread of a pandemic. An uninfected or asymptomatic agent is randomly selected from a random family group in each simulation step for visiting a random public space. Subsequently, an uninfected agent contracts infection if the public place is occupied by other infected agents. We have calibrated our simulation rounds according to the size of the population of the virtual community for simulating realistic exposure of agents to a contagion. Our simulation results are consistent with the publicly available hospitalization and ICU patient data from three distinct regions of varying sizes in New York state. Our model can predict the trend in epidemic spread and hospitalization from a set of simple parameters and could be potentially useful in predicting the disease evolution based on available data and observations about public behavior. Our simulations suggest that relaxing the social distancing measures may increase the hospitalization numbers by some 30% or more. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:
NSTL
Elsevier
Levashov, V. A.Ryltsev, R. E.Chtchelkatchev, N. M.
13页查看更多>>摘要：We investigate the degree of local structural similarity between the parent-liquid and children-crystal states for a model soft-matter system of particles interacting through the harmonic-repulsive pair potential. At different pressures, this simple system crystallizes into several significantly different crystal structures. Therefore, the model is well suited for addressing the question under consideration. In our studies, we carefully analyze the developments of the pair and triple correlation functions for the parent-liquid as the pressure increases. In particular, these considerations allow us to address the similarities in the orientational orderings of the corresponding liquid and solid phases. It is demonstrated that the similarities in the orientational ordering between the two states extend beyond the first and second neighbors. Currently, it is widely accepted that orientational ordering is important for understanding the behaviors of liquids, supercooled liquids, and the development of detailed theories of the crystallization process. Our results suggest that, up to a certain degree, it might be possible to predict the structures of the children-solids from studies of the parent-liquids. Our results raise anew a general question of how much insight into the properties of the liquid-state can be gained from drawing a parallel with the solid-state. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Xie, RongrongDeng, ShengfengDeng, WeibingPato, Mauricio P....
9页查看更多>>摘要：A generalized Poisson ensemble is constructed using the maximum entropy principle based on the non-extensive entropy. It is found that the correlations which are introduced among the eigenvalues lead to statistical distributions with heavy tails. As a consequence, long-range statistics, measured by the number variance, show super-Poissonian behavior and the short-range ones, measured by the nearest-neighbor-distribution show, with respect to Poisson, enhancement at small and large separations. Potential applications were found for the sequence data of protein and DNA, which display good agreement with the model. In particular, the ensuing parameter lambda of the generalized Poisson ensemble can be utilized to facilitate protein classification. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
