The role of Contact-Tracing Mobile Apps in pandemic prevention: A multidisciplinary perspective on health beliefs, social, and technological factors.

During the recent COVID-19 pandemic, governments implemented mobile applications for contact tracing as a rapid and effective solution to mitigate the spread of the virus. However, these seemingly straightforward solutions did not achieve their intended objectives. In line with previous research, this paper aims to investigate the factors that influence the acceptance and usage of contact-tracing mobile apps (CTMAs) in the context of disease control. The research model in this paper integrates the Unified Theory of Acceptance and Use of Technology and the Health Belief Model (HBM). The present study involved a diverse sample of 770 French participants of all genders, ages, occupations, and regions. Critical elements from the Health Belief Model, technological factors related to the app, and social factors, including the centrality of religiosity, were assessed using well-established measurement scales. The research's findings demonstrate that several factors, such as perceived benefits and perceived severity, social influence, health motivation, and centrality of religiosity, significantly impact the intention to use a CTMA. These findings suggest that CTMAs hold promise as valuable tools for managing future epidemics. However, addressing challenges, revising implementation strategies, and potentially collaborating with specialized industry partners under regulatory frameworks are crucial. This practical insight can guide policymakers and public health officials in their decision-making.

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning.

This study aims to predict oil prices during the 2019 novel coronavirus (COVID-19) pandemic by looking into green energy resources, global environmental indexes (ESG), and stock markets. The study employs advanced machine learning, such as the LightGBM, CatBoost, XGBoost, Random Forest (RF), and neural network models. An accurate forecasting framework can effectively capture the trend of the changes in oil prices and reduce the impact of the COVID-19 pandemic on such prices. Additionally, a large dataset with different asset classes was used to investigate the crash period. The research also introduced SHapely Additive exPlanations (SHAP) values for model analysis and interpretability. The empirical results indicate the superiority of the RF and LightGBM over traditional models. Moreover, this new framework provides favorable explanations of the model performance using the efficient SHAP algorithm. It also highlights the core features of predicting oil prices. The study found that high values of GER and ESG lead to lower crude oil prices. Our results are crucial for investors and policymakers in promoting climate change mitigation and sustained economic prosperity through green energy resources.