ABSTRACT
Soundscapes have been studied by researchers from various disciplines, each with different perspectives, approaches, and terminologies. Consequently, the research field determines the actual concept of a specific soundscape with the associated components and also affects the definition itself. This complicates interdisciplinary communication and comparison of results, especially when research areas are involved which are not directly focused on soundscapes. For this reason, we present a formalization that aims to be independent of the concepts from the various disciplines, with the goal of being able to capture the heterogeneous data structure in one layered model. Our model consists of time-dependent sound sources and geodata that influence the acoustic composition of a soundscape represented by our sensor function. Using a case study, we present the application of our formalization by classifying land use types. For this we analyze soundscapes in the form of recordings from different devices at 23 different locations using three-dimensional convolutional neural networks and frequency correlation matrices. In our results, we present that soundscapes can be grouped into classes, but the given land use categories do not have to correspond to them.
ABSTRACT
Purpose: With the coronavirus 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. Methods: In this paper, we propose an agent-based numerical simulation of pedestrian dynamics in order to assess the behavior 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. Results: To abide by the minimum distance of 1.5 m stipulated by the German government at an infection rate of 2%, our simulation results suggest that a density of one person per 16m2 or below is sufficient. Conclusions: The results of this study give insight into how physical distancing as a protective measure can be carried out more efficiently to help reduce the spread of COVID-19.
ABSTRACT
With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.
