A global blockchain for recording high rates of COVID-19 vaccinations.

Blockchain has been recently proposed to securely record vaccinations against COVID-19 and manage their verification. However, existing solutions may not fully meet the requirements of a global vaccination management system. These requirements include the scalability required to support a global vaccination campaign, like one against COVID-19, and the capability to facilitate the interoperation between the independent health administrations of different countries. Moreover, access to global statistics can help to control securing community health and provide continuity of care for individuals during a pandemic. In this paper, we propose GEOS, a blockchain-based vaccination management system designed to address the challenges faced by the global vaccination campaign against COVID-19. GEOS offers interoperability between vaccination information systems at both domestic and international levels, supporting high vaccination rates and extensive coverage for the global population. To provide those features, GEOS uses a two-layer blockchain architecture, a simplified byzantine-tolerant consensus algorithm, and the Boneh-Lynn-Shacham signature scheme. We analyze the scalability of GEOS by examining transaction rate and confirmation times, considering factors such as the number of validators, communication overhead, and block size within the blockchain network. Our findings demonstrate the effectiveness of GEOS in managing COVID-19 vaccination records and statistical data for 236 countries, encompassing crucial information such as daily vaccination rates for highly populous nations and the global vaccination demand, as identified by the World Health Organization.

Comparative Analysis of Energy Use and Greenhouse Gas Emission of Diesel and Electric Trucks for Food Distribution in Gowanus District of New York City.

New York City's food distribution system is among the largest in the United States. Food is transported by trucks from twelve major distribution centers to the city's point-of-sale locations. Trucks consume large amounts of energy and contribute to large amounts of greenhouse gas emissions. Therefore, there is interest to increase the efficiency of New York City's food distribution system. The Gowanus district in New York City is undergoing rezoning from an industrial zone to a mix residential and industrial zone. It serves as a living lab to test new initiatives, policies, and new infrastructure for electric vehicles. We analyze the impact of electrification of food-distribution trucks on greenhouse gas emissions and electricity demand in this paper. However, such analysis faces the challenges of accessing available and granular data, modeling of demands and deliveries that incorporate logistics and inventory management of different types of food retail stores, delivery route selection, and delivery schedule to optimize food distribution. We propose a framework to estimate truck routes for food delivery at a district level. We model the schedule of food delivery from a distribution center to retail stores as a vehicle routing problem using an optimization solver. Our case study shows that diesel trucks consume 300% more energy than electric trucks and generate 40% more greenhouse gases than diesel trucks for food distribution in the Gowanus district.

Correlation of subway turnstile entries and COVID-19 incidence and deaths in New York City.

In this paper, we show a strong correlation between turnstile entries data of the New York City (NYC) subway provided by NYC Metropolitan Transport Authority and COVID-19 deaths and cases reported by the NYC Department of Health from March to May 2020. This correlation is obtained through linear regression and confirmed by the prediction of the number of deaths by a Long Short-Term Memory neural network. The correlation is more significant after considering incubation and symptomatic phases of this disease as experienced by people who died from it. We extend the analysis to each individual NYC borough. We also estimate the dates when the number of COVID-19 deaths and cases would approach zero by using the Auto-Regressive Integrated Moving Average model on the reported deaths and cases. We also backward forecast the dates when the first cases and deaths might have occurred.

Survey of Saliva Components and Virus Sensors for Prevention of COVID-19 and Infectious Diseases.

The United States Centers for Disease Control and Prevention considers saliva contact the lead transmission means of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19). Saliva droplets or aerosols expelled by heavy breathing, talking, sneezing, and coughing may carry this virus. People in close distance may be exposed directly or indirectly to these droplets, especially those droplets that fall on surrounding surfaces and people may end up contracting COVID-19 after touching the mucosa tissue on their faces. It is of great interest to quickly and effectively detect the presence of SARS-CoV-2 in an environment, but the existing methods only work in laboratory settings, to the best of our knowledge. However, it may be possible to detect the presence of saliva in the environment and proceed with prevention measures. However, detecting saliva itself has not been documented in the literature. On the other hand, many sensors that detect different organic components in saliva to monitor a person's health and diagnose different diseases that range from diabetes to dental health have been proposed and they may be used to detect the presence of saliva. This paper surveys sensors that detect organic and inorganic components of human saliva. Humidity sensors are also considered in the detection of saliva because a large portion of saliva is water. Moreover, sensors that detect infectious viruses are also included as they may also be embedded into saliva sensors for a confirmation of the virus' presence. A classification of sensors by their working principle and the substance they detect is presented. This comparison lists their specifications, sample size, and sensitivity. Indications of which sensors are portable and suitable for field application are presented. This paper also discusses future research and challenges that must be resolved to realize practical saliva sensors. Such sensors may help minimize the spread of not only COVID-19 but also other infectious diseases.