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.

人群是 COVID-19 传播的传播源。争用和缓解措施的重点是减少人群聚集。这些努力导致经济下滑。疫苗在世界范围内的应用对人类来说是一个巨大的挑战，即使在几个月内也不太可能完成。与此同时，我们仍然需要工具让人们融入他们的日常生活，降低感染风险。在此背景下，本文提出了一种人群管理解决方案。目的是监控和管理室内场所或兴趣点 (POI) 的人群水平，尤其是购物中心或商店。该解决方案基于 POI 推荐系统，该系统根据用户访问特定 POI 的请求建议最近的安全选项。在这个意义上，它推荐用户位置附近估计人群最少的地方。推荐算法使用 top-K 方法和行为博弈论来预测用户的选择并估计所请求 POI 的人群级别。为了根据可能感染 COVID-19 的潜在接触人数和推荐质量来评估这种技术干预的效率，我们开发了一种基于代理的模型 (ABM)。新技术的采用程度可能与最终用户的体验和对此类技术的信任有关。当最终用户遵循导致不拥挤的地方的建议时，最终用户体验和信任度都会增加。我们使用 OCEAN 人格模型研究和模拟这个过程。研究场景的结果表明，随着信任因子从 0.5（初始设定值）增加到 0.76，所提出的解决方案被代理广泛采用。人流方面得到有效管理，平均减少40%。移动接触减少了 40%，降低了感染 COVID-19 的风险。已设计一个 APP 来支持所描述的人群管理和接触者追踪功能。这个应用程序在 GitHub 上可用。