Community-driven PPE production using additive manufacturing during the COVID-19 pandemic: Survey and lessons learned

This study presents a detailed analysis of the production efforts for personal protective equipment in makerspaces and informal production spaces (i.e., community-driven efforts) in response to the COVID-19 pandemic in the United States. The focus of this study is on additive manufacturing (also known as 3D printing), which was the dominant manufacturing method employed in these production efforts. Production details from a variety of informal production efforts were systematically analyzed to quantify the scale and efficiency of different efforts. Data for this analysis was primarily drawn from detailed survey data from 74 individuals who participated in these different production efforts, as well as from a systematic review of 145 publicly available news stories. This rich dataset enables a comprehensive summary of the community-driven production efforts, with detailed and quantitative comparisons of different efforts. In this study, factors that influenced production efficiency and success were investigated, including choice of PPE designs, production logistics, and additive manufacturing processes employed by makerspaces and universities. From this investigation, several themes emerged including challenges associated with matching production rates to demand, production methods with vastly different production rates, inefficient production due to slow build times and high scrap rates, and difficulty obtaining necessary feedstocks. Despite these challenges, nearly every maker involved in these production efforts categorized their response as successful. Lessons learned and themes derived from this systematic study of these results are compiled and presented to help inform better practices for future community-driven use of additive manufacturing, especially in response to emergencies.     

A literature survey of the robotic technologies during the COVID-19 pandemic

Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack of detailed and systematic review of the robotic research for the pandemic, from the technologies’ perspective. Thus a thorough literature survey is conducted in this research and more than 280 publications have been reviewed, with the focus on robotics during the pandemic. The main contribution of this literature survey is to answer two research questions, i.e. 1) what the main research contributions are to combat the pandemic from the robotic technologies’ perspective, and 2) what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research. The current achievements of robotic technologies are reviewed and discussed in different categories, followed by the identification of the representative work’s technology readiness level. The future research trends and essential technologies are then highlighted, including artificial intelligence, 5 G, big data, wireless sensor network, and human-robot collaboration.     

Structural identifiability and observability of compartmental models of the COVID-19 pandemic

The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights – as well as the possibility of controlling the system – may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.     

Additive manufacturing and the COVID-19 challenges: An in-depth study

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly achieved global pandemic status. The pandemic created huge demand for relevant medical and personal protective equipment (PPE) and put unprecedented pressure on the healthcare system within a very short span of time. Moreover, the supply chain system faced extreme disruption as a result of the frequent and severe lockdowns across the globe. In such a situation, additive manufacturing (AM) becomes a supplementary manufacturing process to meet the explosive demands and to ease the health disaster worldwide. Providing the extensive design customization, a rapid manufacturing route, eliminating lengthy assembly lines and ensuring low manufacturing lead times, the AM route could plug the immediate supply chain gap, whilst mass production routes restarted again. The AM community joined the fight against COVID-19 by producing components for medical equipment such as ventilators, nasopharyngeal swabs and PPE such as face masks and face shields. The aim of this article is to systematically summarize and to critically analyze all major efforts put forward by the AM industry, academics, researchers, users, and individuals. A step-by-step account is given summarizing all major additively manufactured products that were designed, invented, used, and produced during the pandemic in addition to highlighting some of the potential challenges. Such a review will become a historical document for the future as well as a stimulus for the next generation AM community.     

Risk governance during the COVID-19 pandemic: A quantitative content analysis of governors' narratives on twitter

The current study addresses the communication aspect of risk governance during the COVID-19 pandemic by examining whether governors' tweets differ by political party, gender and crisis phase. Drawing on the Centers for Disease Control and Prevention's Crisis Emergency Risk Communication (CERC) model and framing literature, we examined the salience of five CERC's communication objectives, namely acknowledge crisis with empathy, promote protective actions, describe preparedness/response efforts, address rumours and misunderstanding and segment audience. Using a deductive and inductive approach, we analysed 7000 Twitter messages sent by the 50 US state governors during the period of 13 March 2020 to 17 August 2020. Our findings suggest that governors' tweets aligned with CERC's communication objectives to a varying degree. We found main and interaction effects of political party, gender and crisis phase on governors' communication objectives. New emergent communication objectives included attention to mental health, call for social influencers and promoting hope. Implications are discussed.     

Understanding public protective behavioural intention during the COVID-19 pandemic: Moderating effects of proxy efficacy and collective efficacy

Public efficacy beliefs against COVID-19 might affect a person's coping strategy toward infection control. This study presented a synthetic conceptual model based on the Integrative Model of Behavioural Prediction (IMBP). We examined inductively the relationships among media exposure, efficacy beliefs, attribution of responsibilities and recommended protective behavioural intention using a survey of 435 participants who experienced the epidemic in China. Results suggest that traditional media exposure could stably and consistently enhance people's self-efficacy, collective efficacy as well as proxy efficacy, whereas social media exposure only increases the degree of self-efficacy. Furthermore, we detect that protective behavioural intention is directly affected by self-efficacy and indirectly affected by collective efficacy and proxy efficacy via the mediation of self-efficacy. At the same time, the influence of self-efficacy on attribution of responsibilities and protective behaviours can be moderated by collective efficacy and proxy efficacy, respectively.     

The impact of risk and protective factors on online teaching experience in high school Italian teachers during the COVID-19 pandemic

The lockdown due to COVID-19 in Italy resulted in the sudden closure of schools, with a shift from traditional teaching to the online one. Through an online questionnaire, this survey explores teachers' experience of online teaching, the level of risk factors (e.g., stress) and protective factors (e.g., locus of control) and their impact on satisfaction levels during the social distancing. One hundred seven high school teachers from Lombardy, an Italian region very affected by the COVID-19 outbreak, participated. Results show that depression and stress are the main predictors of satisfaction levels for online teaching. In addition, coping, locus of control and self-efficacy emerge as important protective factors. Finally, although there is great satisfaction with the online teaching experience, critical elements emerged. This study is relevant because it describes the critical elements of the online teaching experience, and identifies some protective factors and the main risk factors in teachers operating in an area strongly marked by social restrictions imposed by the pandemic. High school teachers emerge as a sub-group of the general population with specific psychological reactions. Considering the results, it is possible to suggest providing high-quality educational support and crisis-psychological oriented services to teachers, and help to maintain the psychological well-being.     

An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion

Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.     

A social identity perspective on interoperability in the emergency services: Emergency responders' experiences of multiagency working during the COVID-19 response in the UK

Recent research has shown that multiagency emergency response is beset by a range of challenges, calling for a greater understanding of the way in which these teams work together to improve future multiagency working. Social psychological research shows that a shared identity within a group can improve the way in which that group works together and can facilitate effective outcomes. In the present study, 52 semistructured interviews were conducted with 17 strategic and/or tactical responders during the COVID-19 pandemic to understand the possible role of shared identity in the multiagency response to COVID-19 and whether this was linked to factors that facilitated or challenged interoperability. Findings show evidence of a shared identity at a horizontal intergroup level among responders locally. However, there was limited evidence for a shared identity at the vertical intergroup level between local and national responders. Three key factors linked to shared identity appeared to contribute to effective multiagency working. First, pre-existing relationships with other responders facilitated the ease with which responders were able to work together initially. Second, a sense of ‘common fate’ helped bring responders together, and finally, group leaders were able to strategically reinforce a sense of shared identity within the group.     

Situational awareness in a creeping crisis: How the initial phases of the COVID-19 pandemic were handled from a crisis management perspective in the Nursing Home Agency in Oslo

In March 2020, the municipality of Oslo's Nursing Home Agency was hit by Norway's first COVID-19 outbreak. Being responsible for a very vulnerable group, they had to deal with a situation never before encountered and of which they had very limited knowledge. In this study, we explored how situational awareness (SA) changed from a creeping to an urgent crisis. We undertook a case study of the Nursing Home Agency's top management during the initial period of the COVID-19 pandemic (December 2019 through late March 2020). We conducted individual interviews with the management in charge of decisions. Thematic analysis yielded four main categories affecting SA: perception of event development, perception of available time, information, and cooperation and trust. We found that subjective experience of the geographical proximity of the crisis and subjective experience of time were essential in shaping SA. Perception of time was essential to the understanding of urgency, which was an important factor in reacting properly. Further, the perception of space was necessary for the crisis to be interpreted as critical. Time and space are objective factors but are perceived subjectively. Our model showed that the crisis must be perceived as urgent for proper actions to be decided upon.