In this work, we contribute the first visual open-source empirical study on
human behaviour during the COVID-19 pandemic, in order to investigate how
compliant a general population is to mask-wearing-related public-health policy.
Object-detection-based convolutional neural networks, regression analysis and
multilayer perceptrons are combined to analyse visual data of the Viennese
public during 2020. We find that mask-wearing-related government regulations
and public-transport announcements encouraged correct mask-wearing-behaviours
during the COVID-19 pandemic. Importantly, changes in announcement and
regulation contents led to heterogeneous effects on people's behaviour.
Comparing the predictive power of regression analysis and neural networks, we
demonstrate that the latter produces more accurate predictions of population
reactions during the COVID-19 pandemic. Our use of regression modelling also
allows us to unearth possible causal pathways underlying societal behaviour.
Since our findings highlight the importance of appropriate communication
contents, our results will facilitate more effective non-pharmaceutical
interventions to be developed in future. Adding to the literature, we
demonstrate that regression modelling and neural networks are not mutually
exclusive but instead complement each other.

我们通过对 2020 年维也纳公众的视觉数据进行物体检测、卷积神经网络、回归分析和多层感知机的综合分析，发现口罩相关的政府法规和公共交通公告鼓励在 COVID-19 大流行期间正确佩戴口罩的行为，而公告和法规内容的变化对人们的行为产生了不均匀的影响；通过比较回归分析和神经网络的预测能力，我们证明后者能更准确地预测 COVID-19 大流行期间的人口反应，并且回归建模还使我们能够揭示社会行为之间可能存在的因果关系路径；由于我们的发现强调了适当的沟通内容的重要性，我们的研究结果将有助于未来制定更有效的非药物干预措施；此外，我们还证明了回归建模和神经网络并不是互斥的，而是相互补充的。