Social Behavior and COVID-19: Analysis of the Social Factors behind Compliance with Interventions across the United States.

Since its emergence, COVID-19 has caused a great impact in health and social terms. Governments and health authorities have attempted to minimize this impact by enforcing different mandates. Recent studies have addressed the relationship between various socioeconomic variables and compliance level to these interventions. However, little attention has been paid to what constitutes people's response and whether people behave differently when faced with different interventions. Data collected from different sources show very significant regional differences across the United States. In this paper, we attempt to shed light on the fact that a response may be different depending on the health system capacity and each individuals' social status. For that, we analyze the correlation between different societal (i.e., education, income levels, population density, etc.) and healthcare capacity-related variables (i.e., hospital occupancy rates, percentage of essential workers, etc.) in relation to people's level of compliance with three main governmental mandates in the United States: mobility restrictions, mask adoption, and vaccine participation. Our aim was to isolate the most influential variables impacting behavior in response to these policies. We found that there was a significant relationship between individuals' educational levels and political preferences with respect to compliance with each of these mandates.

Scale, context, and heterogeneity: the complexity of the social space.

The social space refers to physical or virtual places where people interact with one another. It decisively influences the emergence of human behaviors. However, little is known about the nature and complexity of the social space, nor its relationship to context and spatial scale. Recently, the science of complex systems has bridged between fields of knowledge to provide quantitative responses to fundamental sociological questions. In this paper, we analyze the shifting behavior of social space in terms of human interactions and wealth distribution across multiple scales using fine-grained data collected from both official (US Census Bureau) and unofficial data sources (social media). We use these data to unveil how patterns strongly depend upon the observation scale. Therefore, it is crucial for any analysis to be framed within the appropriate context to avoid biased results and/or misleading conclusions. Biased data analysis may lead to the adoption of fragile and poor decisions. Including context and a proper understanding of the spatial scale are essential nowadays, especially with the pervasive role of data-driven tools in decision-making processes.

Modular vehicle technology for emergency medical services.

While advancements in vehicular and wireless communication technologies are shaping the future of our transportation system, emergency medical services (EMS) are not receiving enough research attention. Their operations are still plagued by response delays that can often be life-threatening. Dispatching and redeployment systems identify the best practices regarding the allocation of the resources to emergencies and stations. Yet, the existing systems are unfortunately insufficient, and there is a growing need to embrace new technological solutions. This research introduces a smart system for EMS by leveraging the modular vehicle technology initially developed for transit systems. The proposed system relies on the design of vehicular modules that can couple and decouple to transfer patients from one module to another during transport. A fleet of medical transport vehicles is deployed to cooperate with the life support vehicles by providing, for example, transport and hospital admission tasks, thus allowing life support vehicles to answer pending emergency calls earlier. This is especially useful when there is a large demand for EMS (e.g. under the COVID-19 pandemic or other disasters such as the recent explosion in Beirut). This paper introduces a mathematical programming model to determine the optimal assignment decisions in a deterministic setting. This work is a proof of concept that demonstrates the applicability of the modular vehicle technology to EMS, evaluating the upper bound EMS performance that can be ultimately reached. A sensitivity analysis is conducted to provide insights and recommendations that are useful when selecting the weighting coefficients for the optimization function, to ensure a more efficient implementation of the modular vehicle technology for EMS. Also, the results of a comparative analysis show that the proposed system can adapt and offer larger benefits, in terms of response times and times to hospital, as demand increases and/or resources become more limited.