Do we listen to what we are told? An empirical study on human behaviour during the COVID-19 pandemic: neural networks vs. regression analysis
This work addresses public health policy effectiveness for policymakers, though it is incremental in applying existing methods to new data.
The study investigated how compliant the general population was to mask-wearing policies during the COVID-19 pandemic, finding that government regulations and public-transport announcements encouraged correct behaviors, with neural networks providing more accurate predictions than regression analysis.
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.