The energy assessment of COVID-19 medical waste as a potential fuel

The COVID-19 pandemic has caused huge health and economic damages. Various protective face masks, such as single-use, cotton, and the most widespread FFP2 or KN95 masks, are used to prevent the spread of this virus. However, these face masks are usually packaged in plastic packaging, which increases the amount of plastic waste. Plastic gloves are also often used in the connection of the pandemic. All this leads to a large production of protective equipment, but their use contributes to the increase of this type of waste, which presents a new challenge in waste management. This article investigates a complete element analysis of these mentioned materials and observes potential harmful substances. Further, pellets, as a potential fuel for combustion or pyrolysis purposes, were produced with the content of 5% and 10% of face masks. FFP2 were firstly separated from ear straps and wires, then disintegrated, added to spruce sawdust, and compressed into pellets. A series of experiments were realized and aimed at elemental, thermogravimetric, and calorific value analyses of produced pellets. Based on the results, it can be concluded that the presence of face masks FFP2 in pellets increases the content of carbon, hydrogen, and nitrogen, volatile matter, and calorific values, but decreases the content of fixed carbon. According to elemental analysis of produced pellets, no significant amounts of harmful elements were found. © 2023 The Author(s)

COVID-LPS: Location-Protected Services for COVID-19 Prevention

The COVID-19 pandemic has caused not only worldwide health problems but also economic damage. Numerous researchers and intuitions have attempted to visualize confirmed COVID-19 cases with maps to provide timely information to users (e.g., warnings upon entry of crowded areas) and prevent the spread of COVID-19. However, such systems are limited by their poor protection of private information because they must collect sensitive information, such as the locations of individuals. We propose a practical method of obtaining a distribution of users while anonymizing their location data that can be used in location-based services for the prevention of the spread of COVID-19. Generalization and local differential privacy are used to guarantee user and data anonymity while maintaining high data utility and accuracy. To our knowledge, COVID-LPS is not only the first COVID-19 tracing system in Taiwan but also the first system to visualize user distributions for location-based services while protecting user privacy through generalization and local differential privacy. © 2022 IEEE.

The trend of modern cold chain management: Characteristics, challenges and opportunities of vaccine logistics

The high demand for Covid-19 vaccines due to the pandemic increases the need for vaccine transportation and storage worldwide, and a cold chain is often required for many vaccines. The existing cold chains are confronting some obstacles today but are facing many great opportunities at the same time with the emerging new technologies. This paper aims to address the trend of modern cold chain management of vaccine logistics through a literature review on the topic of Covid-19 vaccine transportation and storage. Empirical studies are needed in future research. The transportation and storage of the Covid-19 vaccines are important for the overall vaccine administrations and can save large amounts of money and lives when improved. Failures of the cold chain can lead to great economic loss and damage of the vaccine potency;therefore, it is essential to study the characteristics of the current cold chain and how related innovations influence its development. © 2021 IEEE.

Post COVID-19 challenges for the sustainable entrepreneusrhip

The Outbreak of Covid-19 pandemic has proved to be destructive at economic, social, and ecological ends across the globe. It has disrupted human life in an extremely adverse way. The pandemic aftermath is yet to be completely realised, yet the scale of economic, social, and ecological damage is unfathomable. The situation has elevated the responsibility on entrepreneurial ground to bring in solutions at the affected fronts and work towards sustainability. It is high time to realize the significance of sustainable entrepreneurship at the time when pandemic consequences are severe and still unfolding which will show in the times to come. Though the disruption caused by the pandemic is huge on everyone and everything, the study reveals that sustainable entrepreneurship has comparatively less effect of adversities of COVID-19 than the normal business. This study presents the need and significance of sustainable entrepreneurship to address the damages caused by COVID-19 pandemic. © 2021 IEEE.

Additional time series features for preciseness improvement of LSTM-based COVID-19 spread forecasting model

The COVID-19 pandemic has spread rapidly since 2019. The worldwide uncontrollable outbreak has caused health and economic damage. Multiple deep learning predictable models have been proposed to forecast COVID-19 spread that can help monitor the situation. To improve preciseness of predicted results, we propose multiple time series variables that can be used in LSTM based model to get higher accuracy predicted results for both short and long periods of time. COVID-19 cumulative cases, new cases, 5 days simple moving average of cumulative cases and average of cumulative cases in neighboring countries are added as additional features fed to LSTM model to improve predicted results up to 5% better than the LSTM model without additional features on 7, 14, 21, 28-day prediction. © 2021 ACM.

Initial economic damage from the COVID-19 pandemic in the United States is more widespread across ages and geographies than initial mortality impacts.

The economic and mortality impacts of the COVID-19 pandemic have been widely discussed, but there is limited evidence on their relationship across demographic and geographic groups. We use publicly available monthly data from January 2011 through April 2020 on all-cause death counts from the Centers for Disease Control and Prevention and employment from the Current Population Survey to estimate excess all-cause mortality and employment displacement in April 2020 in the United States. We report results nationally and separately by state and by age group. Nationally, excess all-cause mortality was 2.4 per 10,000 individuals (about 30% higher than reported COVID deaths in April) and employment displacement was 9.9 per 100 individuals. Across age groups 25 y and older, excess mortality was negatively correlated with economic damage; excess mortality was largest among the oldest (individuals 85 y and over: 39.0 per 10,000), while employment displacement was largest among the youngest (individuals 25 to 44 y: 11.6 per 100 individuals). Across states, employment displacement was positively correlated with excess mortality (correlation = 0.29). However, mortality was highly concentrated geographically, with the top two states (New York and New Jersey) each experiencing over 10 excess deaths per 10,000 and accounting for about half of national excess mortality. By contrast, employment displacement was more geographically spread, with the states with the largest point estimates (Nevada and Michigan) each experiencing over 16 percentage points employment displacement but accounting for only 7% of the national displacement. These results suggest that policy responses may differentially affect generations and geographies.