Crowd management COVID-19.

Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people's mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration into their regular routines reducing the risk of infection. In this context, this paper presents a solution for crowd management. The aim is to monitor and manage crowd levels in interior places or point-of-interests (POI), particularly shopping centers or stores. The solution is based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user. In this sense, it recommends places near the user location with the least estimated crowd. The recommendation algorithm uses a top-K approach and behavioral game theory to predict the user's choice and estimate the crowd level for the requested POI. To evaluate the efficiency of this technological intervention in terms of the potential number of contacts of possible COVID-19 infections and the recommendation quality, we have developed an agent-based model (ABM). The adoption level of new technologies can be related to the end-user experience and trust in such technologies. As the end-user follows a recommendation that leads to uncrowded places, both the end-user experience and trust increased. We study and model this process using the OCEAN model of personality. The results from the studied scenarios showed that the proposed solution is widely adopted by the agents, as the trust factor increased from 0.5 (initial set value) to 0.76. In terms of crowd level, these are effectively managed and reduced on average by 40%. The mobility contacts were reduced by 40%, decreasing the risk of COVID-19 infection. An APP has been designed to support the described crowd management and contact tracing functionality. This APP is available on GitHub.

An agent-based model of the dual causality between individual and collective behaviors in an epidemic.

The evolution of an epidemic is strongly related to the behavior of individuals, and the consideration of cause and effect of social phenomena can extend epidemiological models and allow for better identification, prediction and control of the impacts of containment and mitigation measures. This work proposes an agent-based model to simulate the double causality that exists between individual behaviors, influenced by the cultural orientation of a population, and the evolution of an epidemic, focusing on recent studies on the COVID-19 pandemic. To do this, concepts from the social sciences are used, such as the theory of planned behavior, as well as Bayesian inference to abstract the decision-making processes involved in human behavior. A set of simulation experiments with different populations was developed to demonstrate the role that the cultural orientation of a population plays in the management of an epidemic. The results agree with the revised theory, showing that in populations that have a greater inclination towards collectivism, epidemiological indicators evolve in a better way than in those populations where the culture is individualistic. This work contributes to the field of computational epidemiology by providing a new way of including the social aspects of studied populations in agent-based models to help develop better interventions.

On the Acceptance of "Fake" Histopathology: A Study on Frozen Sections Optimized with Deep Learning.

BACKGROUND: The fast acquisition process of frozen sections allows surgeons to wait for histological findings during the interventions to base intrasurgical decisions on the outcome of the histology. Compared with paraffin sections, however, the quality of frozen sections is often strongly reduced, leading to a lower diagnostic accuracy. Deep neural networks are capable of modifying specific characteristics of digital histological images. Particularly, generative adversarial networks proved to be effective tools to learn about translation between two modalities, based on two unconnected data sets only. The positive effects of such deep learning-based image optimization on computer-aided diagnosis have already been shown. However, since fully automated diagnosis is controversial, the application of enhanced images for visual clinical assessment is currently probably of even higher relevance. METHODS: Three different deep learning-based generative adversarial networks were investigated. The methods were used to translate frozen sections into virtual paraffin sections. Overall, 40 frozen sections were processed. For training, 40 further paraffin sections were available. We investigated how pathologists assess the quality of the different image translation approaches and whether experts are able to distinguish between virtual and real digital pathology. RESULTS: Pathologists' detection accuracy of virtual paraffin sections (from pairs consisting of a frozen and a paraffin section) was between 0.62 and 0.97. Overall, in 59% of images, the virtual section was assessed as more appropriate for a diagnosis. In 53% of images, the deep learning approach was preferred to conventional stain normalization (SN). CONCLUSION: Overall, expert assessment indicated slightly improved visual properties of converted images and a high similarity to real paraffin sections. The observed high variability showed clear differences in personal preferences.