Modeling the recent outbreak of COVID-19 in India and its control strategies

The recent emergence of COVID-19 has drawn attention to the various methods of disease control. Since no proper treatment is available till date and the vaccination is restricted to certain age groups, also vaccine efficacy is still under progress, the emphasis has been given to the method of isolation and quarantine. This control is induced by tracing the contacts of the infectious individuals, putting them to the quarantine class and based on their symptoms, classifying them either as the susceptible or sick individuals and moving the sick individuals to the isolated class. To track the current pandemic situation of COVID-19 in India, we consider an extended Susceptible-Exposed-Quarantine-Infected-Isolated-Recovered (SEQ1IQ2R) compartmental model along with calculating its control reproductive number Rc. The disease can be kept in control if the value of Rc remains below one. This “threshold” value of Rc is used to optimize the period of quarantine, and isolation and have been calculated in order to eradicate the disease. The sensitivity analysis of Rc with respect to the quarantine and isolation period has also been done. Partial rank correlation coefficient method is applied to identify the most significant parameters involved in Rc. Based on the observed data, 7-days moving average curves are plotted for prelockdown, lockdown and unlock 1 phases. Following the trend of the curves for the infection, a generalized exponential function is used to estimate the data, and corresponding 95% confidence intervals are simulated to estimate the parameters. The effect of control measures such as quarantine and isolation are discussed. Following various mathematical and statistical tools, we systematically explore the impact of lockdown strategy in order to control the recent outbreak of COVID-19 transmission in India.

Analysis of the effectiveness of face-coverings on the death ratio of COVID-19 using machine learning

The recent outbreak of the COVID-19 led to death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US employed different strategies, including the mask mandate order issued by the states’ governors. In the current work, we defined a parameter called average death ratio as the monthly average of the number of daily deaths to the monthly average number of daily cases. We utilized survey data to quantify people’s abidance by the mask mandate order. Additionally, we implicitly addressed the extent to which people abide by the mask mandate order, which may depend on some parameters such as population, income, and education level. Using different machine learning classification algorithms, we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. The results showed that for the majority of counties, the mask mandate order decreased the death ratio, reflecting the effectiveness of such a preventive measure on the West Coast. Additionally, the changes in the death ratio demonstrated a noticeable correlation with the socio-economic condition of each county. Moreover, the results showed a promising classification accuracy score as high as 90%.

Traces of Time through Space

Technology have long been a partner of workplace meeting facilitation. The recent outbreak of COVID-19 and the cautionary measures to reduce its spread have made it more prevalent than ever before in the form of online-meetings. In this paper, we recount our experiences during weekly meetings in three modalities: using SAGE2 - a collaborative sharing software designed for large displays - for co-located meetings, using a conventional projector for co-located meetings, and using the Zoom video-conferencing tool for distributed meetings. We view these meetings through the lens of effective meeting attributes and share ethnographic observations and attitudinal survey conducted in our research lab. We discuss patterns of content sharing, either sequential, parallel, or semi-parallel, and the potential advantages of creating complex canvases of content. We see how the SAGE2 tool affords parallel content sharing to create complex canvases, which represent queues of ideas and contributions (past, present, and future) using the space on a large display to suggest the progression of time through the meeting.

402. COVID-19 Infection in Nepal: Epidemiological Analysis from April 2020 to March 2021

Background In December 2019, SARS-CoV-2 or coronavirus disease 2019 (COVID-19) emerged from Wuhan, China. A global pandemic quickly unfolded, infecting >137 million people and causing >2.9 million deaths globally as of April 13, 2021. Before April 1, 2020, there were only five confirmed COVID-19 cases in Nepal. Like many countries around the world, the COVID-19 situation quickly escalated in Nepal. The purpose of this study was to determine the trends in COVID-19 cases and deaths in Nepal from April 2020 to March 2021. Methods We utilized epidemiological data from daily Situation Reports published by the Ministry of Health and Population (MOHP) of Nepal. Data were extracted or calculated from April 1, 2020 to March 31, 2021. Primary variables of interest were national and provincial daily cases, total cases, daily deaths, and total deaths. Results Between April 1, 2020 to March 31, 2021, there were 277,304 cases. October 2020 had the highest monthly cases with 92,926 cases. During the one-year study period, the infection rate was 915 cases per 100,000 people. The largest single-day new cases was October 21, 2020 with 5,743 cases, which is calculated to 19 cases per 100,000 people. There were a total of 3,030 deaths. The largest daily new deaths was November 4, 2020 with 43 cases. June 10, 2020 had the highest number of people in quarantine with 172,266 people. October 23, 2020 had the highest number of active cases with 46,329 cases. By March 31, 2021, the percent of mortality was 1.1%, active infection was 0.5%, and recovery was 98.4%. Conclusion Nepal had lower COVID-19 infection and case-fatality rates compared to other countries most affected by the pandemic. This was due to several factors, most notably early implementation of strict lockdown measures and closing of international borders on March 24, 2020 after the second confirmed COVID-19 case. As lockdown restrictions were lifted on July 7, 2020, COVID-19 cases and deaths in Nepal rose rapidly. As vaccination begun on January 27, 2021, cases started to slow down until the most recent outbreak coinciding with the second wave in its neighboring country, India. Now, infection and case-fatality rates in Nepal are at an all-time high, prompting further lockdowns on April 29, 2021. Disclosures All Authors: No reported disclosures

Occurrence, Fate, Effects, and Risks of Dexamethasone: Ecological Implications Post-COVID-19

The recent outbreak of respiratory syndrome-coronavirus-2 (SARS-CoV-2), which causes coronavirus disease (COVID-19), has led to the widespread use of therapeutics, including dexamethasone (DEXA). DEXA, a synthetic glucocorticoid, is among the widely administered drugs used to treat hospitalized COVID-19 patients. The global COVID-19 surge in infections, consequent increasing hospitalizations, and other DEXA applications have raised concerns on eminent adverse ecological implications to aquatic ecosystems. Here, we aim to summarize published studies on DEXA occurrence, fate, and effects on organisms in natural and engineered systems as, pre-COVID, the drug has been identified as an emerging environmental contaminant. The results demonstrated a significant reduction of DEXA in wastewater treatment plants, with a small portion, including its transformation products (TPs), being released into downstream waters. Fish and crustaceans are the most susceptible species to DEXA exposure in the parts-per-billion range, suggesting potential deleterious ecological effects. However, there are data deficits on the implications of DEXA to marine and estuarine systems and wildlife. To improve DEXA management, toxicological outcomes of DEXA and formed TPs should entail long-term studies from whole organisms to molecular effects in actual environmental matrices and at realistic exposure concentrations. This can aid in striking a fine balance of saving human lives and protecting ecological integrity.

COVID-19 Digital Vaccination Passport Based on Blockchain with Its Own Cryptocurrency as a Reward and Mobile App for Its Use

Due to the recent outbreak of the COVID-19 pandemic, everybody’s lives have changed dramatically and society went through new stages accompanied by new needs. This article details a solution for one of these needs, since it provides a system of digital certificates for the secure storage and sharing of medical data related to COVID-19, with the goal of demonstrating immunity or lack of viral infection in a unequivocal, unbreakable and secure way. The proposed system is based on Blockchain and its inherent benefits, developing also a decentralized, mobile and multiplatform app for its use and an incentive system with a customized cryptocurrency in order to reward the users who use it.

Forest Health in Italy: Learning From the Xylella Incursion

Italy has an increasing part of its territory covered by forest, particularly prone to invasion by invasive forest pathogens (IFPs) owing to the presence of many different habitats and climates. To be effective, any preventive or management tools to prevent the arrival and establishment of IFPs must be included in the framework of phytosanitary legislation. The European Union has an open-door phytosanitary system meaning that any consignment that is not specifically regulated can enter. Increasing evidence of the limitations and subsequent gaps in biosecurity with the current system. Learning from the recent outbreak of Xylella fastidiosa, we provide comments and suggestions to fill the gaps and improve the phytosanitary system in Italy.

Toward a ‘New Normal’? Tourist Preferences Impact on Hospitality Industry Competitiveness

The recent outbreak of novel coronavirus (Covid-19) has led to a global panic due to its fatal nature which has harshly impacted the tourist sector and on the place reputation in general. This study aims to compare the factors that develop tourist preferences in terms of (i) what drives the favorability of tourist preferences? (ii) what relationship exists between tourist expectations, proximity, and favorable reputation? and (iii) what are the main influences of tourist preferences on hospitality system competitiveness pre and post Covid-19? By employing structural equation modeling, this study advances knowledge into the research variables’ relationships and advances reputation and marketing performance and practices in the hospitality industry.