Epidemic Dynamics in Temporal Clustered Networks with Local-World Structure

Population demography can change the network structure, which further plays an important role in the spreading of infectious disease. In this paper, we study the epidemic dynamics in temporal clustered networks where the local-world structure and clustering are incorporated into the attachment mechanism of new nodes. It is found that increasing the local-world size of new nodes has little influence on the clustering coefficient but increases the degree heterogeneity of networks. Besides, when the network evolves faster, increasing the local-world size of new nodes leads to a faster initial growth rate and a larger steady density of infectious nodes, while it has small impacts on the steady density of infectious disease when the network evolves slowly. Furthermore, if the average degree is fixed, increasing the probability of triad formation p enlarges the clustering coefficient of a network, which reduces the initial growth rate and steady density of infectious nodes in the network. This work could provide a theoretical foundation for the control of infectious disease.

A molecular dynamic investigation of human rhinovirus 3c protease drug target: insights towards the design of potential inhibitors

The 3C protease is distinguished from most proteases due to the presence of cysteine nucleophile that plays an essential role in viral replication. This peculiar structure encompassed with its role in viral replication has promoted 3C protease as an interesting target for therapeutic agents in the treatment of diseases caused by human rhinovirus (HRV). However, the molecular mechanisms surrounding the chirality of inhibitors of HRV 3C protease remain unresolved. Herein using in silico techniques such molecular dynamic simulation and binding free estimations via molecular mechanics poisson-boltzmann surface area (MM/PBSA), we present a comprehensive molecular dynamics study of the comparison of two potent inhibitors, SG85 and rupintrivir, complexed with HRV3C protease. The binding free energy studies revealed a higher binding affinity for SG85 of 58.853 kcal/mol than that for rupintrivir of 54.0873 kcal/mol and this was found to be in correlation with the experimental data. The energy decomposition analysis showed that residues Leu 127, Thr 142, Ser 144, Gly 145, Tyr 146, Cys 147, His 161, Val 162, Gly 163, Gly 164, Asn 165, and Phe 170 largely contributed to the binding of SG85, whereas His 40, Leu 127, and Gly 163 impacted the binding of rupintrivir. The results further showed that His 40, Glu 71, Leu 127, Cys 147, Gly 163, and Gyl 164 were crucial residues that played a key role in ligand-enzyme binding, and amongst these crucial residues, His 40, Glu 71, and Cys 147 appeared to be conserved in the active site of HRV-3C protease when bound by both inhibitors. These findings provided a comprehensive understanding of the dynamics and structural features and would serve as guidance in the design and development of potent novel inhibitors of HRV.

COVID-19 Lockdown Unravels the Complex Interplay between Environmental Conditions and Human Activity

During the COVID-19 epidemic, draconian countermeasures forbidding nonessential human activities have been adopted in several countries worldwide, providing an unprecedented setup for testing and quantifying the current impact of humankind on climate and for driving potential sustainability policies in the postpandemic era from a perspective of complex systems. In this study, we consider heterogeneous sources of environmental and human activity observables, considered as components of a complex socioenvironmental system, and apply information theory, network science, and Bayesian inference to analyze their structural relations and nonlinear dynamics between January 2019 and August 2020 in northern Italy, i.e., before, during, and after the national lockdown. The topological structure of a complex system strongly impacts its collective behavior;therefore, mapping this structure is essential to fully understand the functions of the system as a whole and its fragility to unexpected disruptions or shocks. To this aim, we unravel the causal relationships between the 16 environmental conditions and human activity variables, mapping the backbone of the complex interplay between intervening physical observables—such as NO2 emissions, energy consumption, intervening climate variables, and different flavors of human mobility flows—to a causal network model. To identify a tipping point during the period of observation, denoting the presence of a regime shift between distinct network states (i.e., before and during the shock), we introduce a novel information-theoretic method based on statistical divergence widely used in statistical physics. We find that despite a measurable decrease in NO2 concentration, due to an overall decrease in human activities, locking down a region as a climate change mitigation is an insufficient remedy to reduce emissions. Our results provide a functional characterization of socioenvironmental interdependent systems, and our analytical framework can be used, more generally, to characterize environmental changes and their interdependencies using statistical physics.

Modeling the Impact of Virtual Contact Network with Community Structure on the Epidemic Spreading

The epidemic spreading is closely related to the spread of information, and it will coevolve with the information transmission. Considering that the network structure has a significant impact on network dynamics and the virtual contact networks have obvious community structures in reality, in this article, we built a multiplex network, which contains a community structure to explore the interplay of the coupled spread dynamics. We first use a microscopic Markov chain approach to characterize the coupled disease-awareness dynamics and then analyze the effect of different factors on the coevolution of information dissemination and epidemic spreading based on the Monte Carlo simulation. The simulation results show that promoting the dissemination of information is indeed conducive to suppressing the spread of disease, but changing the process of disease transmission has no obvious effect on the information dissemination. The analysis also reveals that increasing the information transmission rate or decreasing the information recovery rate can promote the spread of information and inhibit the spread of diseases. In addition, taking preventive behaviors or decreasing the long-distance jump also helps slow the epidemic spreading.

System Dynamic and Simulation of Business Model Innovation in Digital Companies: An Open Innovation Approach

Digital companies must improve their business models evolutionarily and innovatively. Therefore, IT investment, especially for revitalizing digital capabilities for operational changes, is important. Most companies are looking to source innovation from outside organizations. Partnership assessment is a crucial problem since it is not easy to integrate internal and external capabilities. The study aims to define business model innovation based on system dynamics with partnership scenarios. Open innovation is needed to evolve to meet market expansion. Partnership and IP strategies are discussed. System Dynamics modeling is utilized to map a system structure to capture its behavior and the relationships between elements, creating a simulation over time. The study develops a BMI to show how OI variables significantly contribute to the engine of growth for digital companies. The simulation reveals that OI has a significant effect on the company’s performance, indicated by significantly growing revenue after two years. At the early stage, patenting IP is not practical since the companies involved are unclear about the detailed IP. The IP success or the partner failure rate does not affect revenue significantly. After two years, companies sharing IP find the best way to benefit from it and lead to sustainable growth.