Physics

In this paper we propose an epidemiological model for the spread of COVID-19. The dynamics of the spread is based on four fundamental categories of people in a population: Tested and infected, Non-Tested but infected, Tested but not infected, and non-Tested and not infected. The model is based on two levels of dynamics of spread in the population: at local level and at the global level. The local level growth is described with data and parameters which include testing statistics for COVID-19, preventive measures such as nationwide lockdown, and the migration of people across neighboring locations. In the context of India, the local locations are considered as districts and migration or traffic flow across districts are defined by normalized edge weight of the metapopulation network of districts which are infected with COVID-19. Based on this local growth, state level predictions for number of people tested with COVID-19 positive are made. Further, considering the local locations as states, prediction is made for the country level. The values of the model parameters are determined using grid search and minimizing an error function while training the model with real data. The predictions are made based on the present statistics of testing, and certain linear and log-linear growth of testing at state and country level. Finally, it is shown that the spread can be contained if number of testing can be increased linearly or log-linearly by certain factors along with the preventive measures in near future. This is also necessary to prevent the sharp growth in the count of infected and to get rid of the second wave of pandemic.

We study how international flights can facilitate the spread of an epidemic to a worldwide scale. We combine an infrastructure network of flight connections with a population density dataset to derive the mobility network, and then define an epidemic framework to model the spread of disease. Our approach combines a compartmental SEIRS model with a graph diffusion model to capture the clusteredness of the distribution of the population. The resulting model is characterised by the dynamics of a metapopulation SEIRS, with amplification or reduction of the infection rate which is determined by the mobility of individuals. Then, we use simulations to characterise and study a variety of realistic scenarios that resemble the recent spread of COVID-19. Crucially, we define a formal framework that can be used to design epidemic mitigation strategies: we propose an optimisation approach based on genetic algorithms that can be used to identify an optimal airport closure strategy, and that can be employed to aid decision making for the mitigation of the epidemic, in a timely manner.

The term `resilience' is increasingly being used in the domain of social-technical-environmental systems science and related fields. However, the diversity of resilience concepts and a certain (sometimes intended) openness of proposed definitions can lead to misunderstandings and impede their application to systems modelling. We propose an approach that aims to ease communication as well as to support systematic development of research questions and models in the context of resilience. It can be applied independently of the modelling framework or underlying theory of choice. At the heart of this guideline is a checklist consisting of four questions to be answered: (i) Resilience of what? (ii) Resilience regarding what? (iii) Resilience against what? (iv) Resilience how? We refer to the answers to these resilience questions as the "system", the "sustainant", the "adverse influence", and the "response options". The term `sustainant' is a neologism describing the feature of the system (state, structure, function, pathway etc.) that should be maintained (or restored quickly enough) in order to call the system resilient. The use of this proposed guideline is demonstrated for two application examples: fisheries, and the Amazon rainforest. The examples illustrate the diversity of possible answers to the checklist's questions as well as their benefits in structuring the modelling process. The guideline supports the modeller in communicating precisely what is actually meant by `resilience' in a specific context. This combination of freedom and precision could help to advance the resilience discourse by building a bridge between those demanding unambiguous definitions and those stressing the benefits of generality and flexibility of the resilience concept.

This article presents a new, holistic model for the air traffic management system, built during the Vista project. The model is an agent-driven simulator, featuring various stakeholders such as the Network Manager and airlines. It is a microscopic model based on individual passenger itineraries in Europe during one day of operations. The article focuses on the technical description of the model, including data and calibration issues, and presents selected key results for 2035 and 2050. In particular, we show clear trends regarding emissions, delay reduction, uncertainty, and increasing airline schedule buffers.

The Axelrod model of cultural dissemination has been widely studied in the field of statistical mechanics. The traditional version of this agent-based model is to assign a cultural vector of F components to each agent, where each component can take one of Q cultural trait. In this work, we introduce a novel set of mean field master equations to describe the model for F=2 and F=3 in complete graphs where all indirect interactions are explicitly calculated. We find that the transition between different macroscopic states is driven by initial conditions (set by parameter Q ) and the size of the system N , who measures the balance between linear and cubic terms in master equations. We also find that this analytical approach fully agrees with simulations where the system does not break up during the dynamics and a scaling relation related to missing links reestablishes the agreement when this happens.

COVID-19 epidemics in Japan and Tokyo were analyzed by a fundamental equation of the dynamic phase transition. As a result, the epidemic was found to be in good agreement with the random nucleation and linear growth model suggesting that the epidemic between March 13, 2020 and May 22, 2020 was simply rate-limited by the three constant-parameters: the initial susceptible, domain growth rate, and nucleation decay constant. This model provides a good predictor of the epidemic because it consists of one equation and the initial specific plot is linear.

Estimation of prevalence of undocumented SARS-CoV-2 infections is critical for understanding the overall impact of the Covid-19 disease. In fact, unveiling uncounted cases has fundamental implications for public policy interventions strategies. In the present work, we show a basic yet effective approach to estimate the actual number of people infected by Sars-Cov-2, by using epidemiological raw data reported by official health institutions in the largest EU countries and USA.

A significant number of those killed in traffic accidents annually refers to pedestrians. most of these accidents occur when a pedestrian is going to pass the street. One of the most hazardous areas for pedestrians crossing the street is midblock crosswalks. These areas are often uncontrolled and there is no separated phase for pedestrian crossing. As a result, using created gaps among vehicles is often the only opportunity for pedestrians to cross the street. This research aims to investigate effective factors on pedestrian gap acceptance at uncontrolled mid-block crosswalks. In this regard, several variables such as individual, environmental, and traffic variables were considered and their related data were extracted by filming the location. from video images, they were analyzed using logit regression model and the behavior After extracting the data of pedestrian while he is crossing the street was modeled as the probabilistic function of pedestrian gap acceptance. The results illustrate that conflict flow rate, the temporal gap among vehicles, and driver yielding in the face of vehicles are the most important effective factors on pedestrian behavior. The other one is that the speed and type of vehicles do not affect the pedestrian's decision about rejecting or accepting the gap. The results also show that individual characteristics such as age and gender of pedestrians do not make a significant relationship with pedestrian gap acceptance.

The dynamics of wealth distribution plays a critical role in the economic market, hence an understanding of its nonequilibrium statistical mechanics is of great importance to human society. For this aim, a simple and efficient one-dimensional (1D) lattice gas automaton (LGA) is presented for wealth distribution of agents with or without saving propensity. The LGA comprises two stages, i.e., random propagation and economic transaction. During the former phase, an agent either remains motionless or travels to one of its neighboring empty sites with a certain probability. In the subsequent procedure, an economic transaction takes place between a pair of neighboring agents randomly. It requires at least 4 neighbors to present correct simulation results. The LGA reduces to the simplest model with only random economic transaction if all agents are neighbors and no empty sites exist. The 1D-LGA has a higher computational efficiency than the 2D-LGA and the famous Chakraborti-Chakrabarti economic model. Finally, the LGA is validated with two benchmarks, i.e., the wealth distributions of individual agents and dual-earner families. With the increasing saving fraction, both the Gini coefficient and Kolkata index (for individual agents or two-earner families) reduce, while the deviation degree (defined to measure the difference between the probability distributions with and without saving propensities) increases. It is demonstrated that the wealth distribution is changed significantly by the saving propensity which alleviates wealth inequality.

Nuclear energy technologies have the potential to help mitigate climate change. However, these technologies face many challenges, including high costs, societal concern and opposition, and health, safety, environmental and proliferation risks. Many companies and academic research groups are pursuing advanced designs, both fission and fusion-based, to address both costs and these risks. This Chapter complements these efforts by analyzing how nuclear technologies can address societal concerns through the acquisition of a social license, a nebulous concept that represents "society's consent" and that has been used to facilitate and improve a wide range of publicly and privately funded projects and activities subject to a range of regulatory oversight, including large industrial facilities, controversial genetic engineering research, and environmental management. Suggestions for public engagement and consent-based siting, two aspects of a social license, have been made before. This chapter modernizes these suggestions by briefly reviewing the social license and engagement literature. The Chapter discusses, in the context of how to acquire a social license, the role of government regulation, the role of project proponents and government actors, and the role of four key principles, including engendering trust, transparency, meaningful public engagement, and protection of health, safety and the environment. Further, the Chapter uses the social license concept to explain why some nuclear waste repositories have succeeded while others languish and provides concrete recommendations for the deployment of new nuclear waste repositories and advanced power plants, both fission and fusion-based.