Physics

With the Corona Virus Disease 2019 (COVID-19) pandemic spreading across the world, protective measures for containing the virus are essential, especially as long as no vaccine or effective treatment is available. One important measure is the so-called physical distancing or social distancing. In this paper, we propose an agent-based numerical simulation of pedestrian dynamics in order to assess behaviour of pedestrians in public places in the context of contact-transmission of infectious diseases like COVID-19, and to gather insights about exposure times and the overall effectiveness of distancing measures. To abide the minimum distance of 1.5m stipulated by the German government at an infection rate of 2%, our simulation results suggest that a density of one person per 16 m 2 or below is sufficient. The results of this study give insight about how physical distancing as a protective measure can be carried out more efficiently to help reduce the spread of COVID-19.

Transmission of highly infectious respiratory diseases, including SARS-CoV-2 are facilitated by the transport of tiny droplets and aerosols (harboring viruses, bacteria, etc.) that are breathed out by individuals and can remain suspended in air for extended periods of time in confined environments. A passenger car cabin represents one such situation in which there exists an elevated risk of pathogen transmission. Here we present results from numerical simulations of the potential routes of airborne transmission within a model car geometry, for a variety of ventilation configurations representing different combinations of open and closed windows. We estimate relative concentrations and residence times of a non-interacting, passive scalar -- a proxy for infectious pathogenic particles -- that are advected and diffused by the turbulent airflows inside the cabin. Our findings reveal that creating an airflow pattern that travels across the cabin, entering and existing farthest from the occupants can potentially reduce the transmission.

Electric vehicles, noted EV, with smaller environmental footprint than traditional gasoline vehicles or hybrids, are growing rapidly worldwide. Several countries such as Norway and Canada have successfully established their EV networks and achieved a significant progress towards their EV deployment. While the new EV technology is becoming popular in developed countries, emerging countries are lacking behind mainly because of the huge investment hurdle to establish EV networks. This paper provides an efficient mathematical model aiming to minimize the total costs involved in establishing an EV network, using real work data from Morocco. A given set of public institutions having a fleet of EVs are first grouped into zones based on clustering algorithms. Mixed integer linear programming model are developed to optimally select charging station locations within these organizations, with an objective to minimize total cost. This paper support the minimization of the investment needed to establish EV networks. The transition towards EV networks can first take place in cities, especially for public institutions fleet that have a fixed and known operating itinerary and schedule, followed by locations among cities. The mathematical models provided through this paper aim to enhance and foster policy makers' ability in making decisions related to the migration towards EV.

With the rapid increase in ride-hailing (RH) use, a need to better understand and regulate the industry arises. This paper analyzes a year's worth of RH trip data from the Greater Chicago Area to study RH trip patterns. More than 104 million trips were analyzed. For trip rates, the results show that the total number of trips remained stable over the year, with pooled trips steadily decreasing from 20 to 9 percent. People tend to use RH more on weekends compared to weekdays. Specifically, weekend RH trip counts (per day) are, on average, 20 percent higher than weekday trip counts. The results of this work will help policy makers and transportation administrators better understand the nature of RH trips, which in turn allows for the design of a better regulation and guidance system for the ride-hailing industry.

To better understand the process by which humans make navigation decisions when tasked with multiple stopovers, we analyze motion data captured from shoppers in a grocery store. We discover several trends in the data that are consistent with a noisy decision making process for the order of item retrieval, and decompose a shopping trip into a sequence of discrete choices about the next item to retrieve. Our analysis reveals that the likelihood of inverting any two items in the order is monotonically bound to the entropy of the pair-wise ordering task. Based on this analysis, we propose a noisy distance estimation model for predicting the order of item retrieval given a shopping list. We show that our model theoretically reproduces the entropy law seen in the data with high accuracy, and in practice matches the trends in the data when used to simulate the same shopping lists. Our approach has direct applications to improving simulations of human navigation in retail and other settings.

The worldwide outbreak of COVID-19 has posed a dire threat to the public. Human mobility has changed in various ways over the course of the pandemic. Despite current studies on common mobility metrics, research specifically on state-to-state mobility is very limited. By leveraging the mobile phone location data from over 100 million anonymous devices, we estimate the population flow between all states in the United States. We first analyze the temporal pattern and spatial differences of between-state flow from January 1, 2020 to May 15, 2020. Then, with repeated measures ANOVA and post-hoc analysis, we discern different time-course patterns of between-state population flow by pandemic severity groups. A further analysis shows moderate to high correlation between the flow reduction and the pandemic severity, the strength of which varies with different policies. This paper is promising in predicting imported cases.

As the recent COVID-19 outbreak rapidly expands all over the world, various containment measures have been carried out to fight against the COVID-19 pandemic. In Mainland China, the containment measures consist of three types, i.e., Wuhan travel ban, intra-city quarantine and isolation, and inter-city travel restriction. In order to carry out the measures, local economy and information acquisition play an important role. In this paper, we investigate the correlation of local economy and the information acquisition on the execution of containment measures to fight against the COVID-19 pandemic in Mainland China. First, we use a parsimonious model, i.e., SIR-X model, to estimate the parameters, which represent the execution of intra-city quarantine and isolation in major cities of Mainland China. In order to understand the execution of intra-city quarantine and isolation, we analyze the correlation between the representative parameters including local economy, mobility, and information acquisition. To this end, we collect the data of Gross Domestic Product (GDP), the inflows from Wuhan and outflows, and the COVID-19 related search frequency from a widely-used Web mapping service, i.e., Baidu Maps, and Web search engine, i.e., Baidu Search Engine, in Mainland China. Based on the analysis, we confirm the strong correlation between the local economy and the execution of information acquisition in major cities of Mainland China. We further evidence that, although the cities with high GDP per capita attracts bigger inflows from Wuhan, people are more likely to conduct the quarantine measure and to reduce going out to other cities. Finally, the correlation analysis using search data shows that well-informed individuals are likely to carry out containment measures.

As of July, 2020, acute respiratory syndrome caused by coronavirus COVID-19 is spreading over the world and causing severe economic damages. While minimizing human contact is effective in managing the outbreak, it causes severe economic losses. Strategies solving this dilemma by considering interrelation between the spread of the virus and economic activities are in urgent needs for mitigating the health and economic damage. Here we propose an abstract agent-based model for the outbreak of COVID-19 in which economic activities are taken into account. The computational simulation of the model recapitulated the trade-off between health and economic damage associated with lockdown measures. Based on the simulation results, we discuss how macroscopic dynamics of infection and economy emerge from the individuals' behaviours. We believe our model can serve as a platform for discussing solutions to the abovementioned dilemma.

An increased interdisciplinarity in science projects has been highlighted as crucial to tackle complex real-world challenges, but also as beneficial for the development of disciplines themselves. This paper introduces a parcimonious agent-based model of interdisciplinary relationships in collective entreprises of knowledge discovery, to investigate the impact of scientist-level decisions and preferences on global interdisciplinarity patterns. Under the assumption of simple rules for individual researcher project management, such as trade-offs between invested time overhead and knowledge benefit, model simulations show that individual choices influence the distribution of compromise points between emergent level of disciplinary depth and interdisciplinarity in a non-linear way. Different structures for collaboration networks may also yield various outcomes in terms of global interdisciplinarity. We conclude that independently of the research field, the organization of research, and more particularly the local balancing between vertical and horizontal research, already influences the final positioning of research results and the extent of the knowledge front. This suggests direct applications to research policies with a bottom-up leverage on the interactions between disciplines.

During pandemic events, strategies such as social distancing can be fundamental to curb viral spreading. Such actions can reduce the number of simultaneous infections and mitigate the disease spreading, which is relevant to the risk of a healthcare system collapse. Although these strategies can be suggested, their actual implementation may depend on the population perception of the disease risk. The current COVID-19 crisis, for instance, is showing that some individuals are much more prone than others to remain isolated, avoiding unnecessary contacts. With this motivation, we propose an epidemiological SIR model that uses evolutionary game theory to take into account dynamic individual quarantine strategies, intending to combine in a single process social strategies, individual risk perception, and viral spreading. The disease spreads in a population whose agents can choose between self-isolation and a lifestyle careless of any epidemic risk. The strategy adoption is individual and depends on the perceived disease risk compared to the quarantine cost. The game payoff governs the strategy adoption, while the epidemic process governs the agent's health state. At the same time, the infection rate depends on the agent's strategy while the perceived disease risk depends on the fraction of infected agents. Results show recurrent infection waves, which were seen in previous epidemic scenarios with quarantine. Notably, the risk perception is found to be fundamental for controlling the magnitude of the infection peak, while the final infection size is mainly dictated by the infection rates. Low awareness leads to a single and strong infection peak, while a greater disease risk leads to shorter, although more frequent, peaks. The proposed model spontaneously captures relevant aspects of a pandemic event, highlighting the fundamental role of social strategies.