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Household responses to COVID-19 in different corners of the world represent the primary health care that communities have relied on for preventing and mitigating symptoms. During a very complex and confusing time, in which public health services in multiple countries have been completely overwhelmed, and in some cases even collapsed, these first-line household responses have been quintessential for building physical, mental, and social resilience, and for improving individual and community health. This editorial discusses the outcomes of a rapid-response preliminary survey during the first phase of the pandemic among social and community contacts in five metropolises heavily affected by the COVID-19 health crisis (Wuhan, Milan, Madrid, New York, and Rio de Janeiro), and in twelve rural areas or countries initially less affected by the pandemic (Appalachia, Jamaica, Bolivia, Romania, Belarus, Lithuania, Poland, Georgia, Turkey, Pakistan, Cambodia, and South Africa). We summarized our perspectives as 17 case studies, observing that people have relied primarily on teas and spices (“food-medicines”) and that there exist clear international plant favorites, popularized by various new media. Urban diasporas and rural households seem to have repurposed homemade plant-based remedies that they use in normal times for treating the flu and other respiratory symptoms or that they simply consider healthy foods. The most remarkable shift in many areas has been the increased consumption of ginger and garlic, followed by onion, turmeric, and lemon. Our preliminary inventory of food medicines serves as a baseline for future systematic ethnobotanical studies and aims to inspire in-depth research on how use patterns of plant-based foods and beverages, both “traditional” and “new”, are changing during and after the COVID-19 pandemic. Our reflections in this editorial call attention to the importance of ethnobiology, ethnomedicine, and ethnogastronomy research into domestic health care strategies for improving community health. Supplementary Information Supplementary information accompanies this paper at 10.1186/s13002-020-00426-9.

Abstract Coronavirus disease 2019 (COVID-19) virus is an infectious disease which has spread globally since 2019, resulting in an ongoing pandemic. Since it is a new virus, it takes some time to develop a vaccine against it. Until then, the best way to prevent the fast spread of the virus is to enable the proper social distancing and isolation or containment to identify potential patients. Since the virus has up to 14 days of the incubation period, it is important to identify all the social interactions during this period and enforce social isolation for such potential patients. However, proper social interaction tracking methods and patient prediction methods based on such data are missing for the moment. This paper focuses on tracking the social interaction of users and predict the infection possibility based on social interactions. We first developed a BLE (Bluetooth Low Energy) and GPS based social interaction tracking system. Then, we developed an algorithm to predict the possibility of being infected with COVID-19 based on the collected data. Finally, a prototype of the system is implemented with a mobile app and a web monitoring tool. In addition, we performed a simulation of the system with a graph-based model to analyze the behaviour of the proposed algorithm and it verifies that self-isolation is important in slowing down the disease progression.

Due to the personal and material restrictions caused by the COVID-19 pandemic, the teaching of robotics has been greatly affected in recent months due to its practical nature. This work aims to explore possibilities for the continuation of this teaching remotely. A simple teaching methodology will be proposed, listed and compared simulators softwares on the most diverse platforms, and the possibility of using remote laboratories will be analyzed. The idea is to present a set of solutions, so that teachers can analyze and select the possibilities that best identify themselves and that best suit the specifics of their class.

This is an Environmental Education (EA) research project based on the 2030 Agenda for Sustainable Development of the United Nations (UN) in its goal 12 - sustainable consumption and production partially developed in 2020 due to the Covid-19 pandemic. It took place at Dr. Crespo de Oliveira Municipal School of Basic Education (EMEB) in the municipality of Uruguaiana, participant of the Environmental Education Program of the Municipal Education Network (PEARME), extension project of the Federal University of Pampa (UNIPAMPA) - campus Uruguayan. EE is a necessary cross-cutting theme to be debated in this school located in the rural area, which experiences the positive and negative effects of agricultural production, its disposal and does not have selective collection in the municipality. The project was planned for eight Physical Education (PE) classes and articulated with the EA. The objectives are: to provide students in the final years with an understanding of the correct disposal of organic and dry products in the proper bins and to demonstrate care with the school's cleanliness, organization and aesthetics. This care is related to educating, with acting thinking about the relationships between living beings and the environments in which they live. Employees, professors and management team participated in the execution of the study. The results were partially achieved in a smaller number of classes, but the research showed the students' adherence to group work and care for the school. The relationship between PE and EE in the construction of knowledge in an integrated manner was evident. Este é um projeto de pesquisa de Educação Ambiental (EA) baseado na Agenda 2030 para o Desenvolvimento Sustentável da Organização das Nações Unidas (ONU) em seu objetivo 12 - consumo e produção sustentáveis desenvolvido parcialmente em 2020 em virtude da pandemia de Covid-19. Realizou-se na Escola Municipal de Educação Básica (EMEB) Dr. Crespo de Oliveira no município de Uruguaiana, participante do Programa de Educação Ambiental da Rede Municipal de Ensino (PEARME), projeto de extensão da Universidade Federal do Pampa (UNIPAMPA) - campus Uruguaiana. EA é um tema transversal necessário a ser debatido nesta escola localizada na zona rural que vivencia os efeitos positivos e negativos da produção agrícola, seus descartes e não conta com a coleta seletiva do município. O projeto foi planejado para oito aulas de Educação Física (EF) e articulado com a EA. Os objetivos são: propiciar o entendimento dos alunos dos anos finais do correto descarte dos produtos orgânicos e secos nas lixeiras adequadas e evidenciarem o cuidado com a limpeza, organização e estética da escola. Este cuidado está relacionado com educar, com agir pensando nas relações entre os seres vivos e os ambientes onde vivem. Funcionários/as, professores/as e equipe diretiva participaram da execução do estudo. Os resultados foram parcialmente atingidos em um menor número de aulas, mas a pesquisa evidenciou a adesão dos alunos ao trabalho em grupo e o cuidado com a escola. Ficou evidente a relação entre EF e EA na construção do conhecimento de maneira integrada.

The year 2020 has undoubtedly made Digital Health a necessity and no longer an exception. Many technological advances that seemed futuristic became a reality in a few months due to the pandemic’s needs. Stroke was one of the diseases that most benefited from the digital health revolution. Due to the need for immediate care and a shortage of neurologists worldwide, telestroke has revolutionized the acute care of cerebrovascular diseases in many areas based on strong scientific evidence. In this brief article, we have tried to summarize all we have experienced in 2020, the year which irreversibly transformed the practice of medicine.

Abstract Due to the spread of Coronavirus disease 2019 (COVID-19), the world has encountered an ongoing pandemic to date. It is a highly contagious disease. In addition to the vaccination, social distancing and isolation of patients are proven to be one of the commonly used strategies to reduce the spread of disease. For efficient social distancing, contact tracing is a critical requirement in the incubation period of 14-days of the disease to contain any further spread. However, we identify that there is a lack of reliable and practical social interaction tracking methods and prediction methods for the probability of getting the disease. This paper focuses on user tracking and predicting the infection probability based on these social interactions. We first developed an energy-efficient BLE (Bluetooth Low Energy) based social interaction tracking system to achieve this. Then, based on the collected data, we propose an algorithm to predict the possibility of getting the COVID-19. Finally, to show the practicality of our solution, we implemented a prototype with a mobile app and a web monitoring tool for healthcare authorities. In addition to that, to analyze the proposed algorithm’s behaviour, we performed a simulation of the system using a graph-based model.

The outbreak of the novel coronavirus and its disease COVID-19 present an unprecedented challenge for humanity. Artificial Intelligence (AI) and robotics may help fighting COVID-19. Potential applications of AI in this accelerating pandemic include, but are not limited to, early detection and diagnosis, massive agent modeling and simulation, data analytics, assistive robots, disinfection robots, public awareness and patrolling, contactless delivery services, virtual healthcare assistants, drug repurposing and vaccination discovery. This chapter sheds light on the roles AI and robotics can play in fighting this disastrous pandemic, and possible future ones, and highlights several potential applications to transform this challenge into opportunities. This chapter also discusses the ethical implications of AI and robotics during the pandemic and in the post-pandemic world.

The use of simulation and visualization tools to validate mechanical designs and programming for robots has become increasingly important. Being used in many scenarios, simulators have collaborated for research in machine learning, deep learning and training of professionals. Nowadays, with the Covid-19 pandemic and social distancing, simulators have great potential as a tool to help in the teaching of robotics. However, research indicates that the user may encounter differences between what happens in the simulator and in real life, this effect is called reality gap. This article aims to investigate the effects of this gap in an educational robotics simulator, named Virtual Robotics Toolkit (VRT), as well as its impacts on the testing of algorithms and to propose strategies to reduce the impact of this effect. Three batch tests were carried out on two different robots, both in the simulation and in the real world. By capturing data from sensors such as tachometer, infrared, gyroscope and a light sensor, it was possible to compare the performance in the two scenarios and their differences. Finally, the entire test field and models were made available along with videos of some tests.

COVID-19 pandemic is on a trajectory to cause catastrophic global upheaval with the potential to alter geopolitical and socio-economic norms. Many countries are frantically responding with staggering financial stimulus recovery initiatives. This opinion-paper reviews challenges, opportunities, and potential solutions for the post-COVID-19 era that focuses on intensive sustaining of agri-food supply chain in tandem with meeting the high demand for new green deal innovation. For example, the development of wet peatland innovation, known as Paludiculture, can intensively sustain and blend agri-food and green innovations that will help support COVID-19 pandemic transitioning. The future looks bright for the creation of new sustainability multi-actor innovation hubs that will support, connect, and enable businesses to recover and pivot beyond the COVID-19 pandemic. The nexus between first ‘Green Deal’ initiative supporting 64 selected European Startups and SMEs (European Innovation Council) and 43 Irish Disruptive Technology projects are addressed in the context of cross-cutting developments and relevance to COVID-19. Candidate areas for future consideration will focus on climate action, digitization, manufacturing, and sustainable food production, security, and waste mitigation. Recommendations are also provided to facilitate community transitioning, training, enterprise, and employment to low carbon economy. yes

This study aimed to describe the prevalence, severity and socio-demographic predictors of food insecurity in Australian households during the COVID-19 pandemic in 2020, from the perspective of women. A cross-sectional online survey of Australian (18–50 years) women was conducted. The survey collected demographic information and utilised the 18-item US Department of Agriculture Household Food Security Survey Module and the Kessler Psychological Distress Scale (K10). A multivariable regression was used to identify predictors of food security status. In this cohort (n = 1005), 19.6% were living in households experiencing food insecurity; with 11.8% experiencing low food-security and 7.8% very low food-security. A further 13.7% of households reported marginal food-security. Poor mental health status (K10 score ≥ 20) predicted household food insecurity at all levels. The presence of more than three children in the household was associated with low food-security (OR 6.24, 95% CI: 2.59–15.03). Those who were renting were 2.10 (95% CI: 1.09–4.05) times likely to experience very low food-security than those owning their own home. The COVID-19 pandemic may have contributed to an increased prevalence of household food insecurity. This study supports the need for a range of responses that address mental health, financial, employment and housing support to food security in Australia. Monash University National Heart Foundation Future Leader Fellowship National Health & Medical Research Council