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The study aims to carry out predictive modeling based on publicly available COVID-19 data for the duration April 01, 2020 to June 20, 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat and Rajasthan.

The study leverages the susceptible, infected, recovered and dead (SIRD) epidemiological framework for predictive modeling. The basic reproduction number R0 is derived by an exponential growth method using RStudio package R0. The differential equations reflecting the SIRD model have been solved using Python 3.7.4 on the Jupyter Notebook platform. For visualization, Python Matplotlib 3.2.1 package is used.

The study offers insights on peak-date, peak number of COVID-19 infections and end-date pertaining to India and five of its states.

The results subtly indicate toward the amount of effort required to completely eliminate the infection. It could be leveraged by the political leadership and industry doyens for economic policy planning and execution.

The emergence of a clear picture about COVID-19 lifecycle is impossible without integrating data science algorithms and epidemiology theoretical framework. This study amalgamates these two disciplines to undertake predictive modeling based on COVID-19 data from India and five of its states. Population-specific granular and objective assessment of key parameters such as reproduction number (R0), susceptible population (S), effective contact rate (ß) and case-fatality rate (s) have been used to generate a visualization of COVID-19 lifecycle pattern for a critically affected population.

This paper aims to assess the economic impact of uniform COVID-controlling policies that were implemented by the US government in 2020 and compare it with hypothetical targeted policies that consider the heterogenous effect of COVID-19 on different age groups.

The author began by showing that the adjusted SEQIHR model is a good fit to the US COVID-induced daily death data in that it can capture the nonlinearities of the data very well. Then, he used this model with extra parameters to evaluate the economic effects of COVID-19 through its impact on the job market.

The results show that targeted COVID-controlling policies could reduce the US death rate and GDP loss to 0.03% and 2%, respectively. By comparing these results with uniform COVID-controlling policies, which led to a 0.1% death rate and 3.5% GDP loss, we could conclude that the death rate reduction is 0.07%. Approximately 378,000 Americans died because of COVID-19 during 2020, therefore, reducing the death rate to 0.03% means saving a significant proportion of the COVID-19 casualties, around 280,000 lives.

To the best of the author's knowledge, this paper is the first study to assess the economic impacts of COVID-controlling policies by using the multirisk SEQIHR model.

The objective of this chapter is to discuss how different techniques in Regional Science and Peace Science and the emerging techniques in Management Science can be used in analysing Disaster Management and Global pandemic with special reference to developing countries. It is necessary for me to first discuss the subjects of Disaster Management, Regional Science, Peace Science and Management Science. The objective of this chapter is to emphasise that the studies of Disaster Management should be more integrated with socioeconomic and geographical factors. The greatest disaster facing the world is the possibility of war, particularly nuclear war, and the preparation of the means of destruction through military spending.

Traditional epidemic models, like the classical SIR, are fitted to real data using deterministic optimization techniques. As a consequence, their performances cannot be properly assessed and, more importantly, the estimates of the critical epidemic parameters (which are of dramatic importance in monitoring the epidemic evolution) cannot be complemented with the calculation of confidence intervals. The aim of the present work is to remove such limitations and to compare the results obtained using two stochastic versions of deterministic SIR models. We describe the two alternatives and the associated estimation procedures, and we apply the two methodologies to a set of COVID-19 data observed in Italy in the 2020 pandemic wave. Our estimates of the basic reproduction number are comparable with the official sources, but using our methods uncertainty can also be properly assessed.

The purpose of this study is to understand the factors that contribute to the number of reported coronavirus (COVID-19) deaths among low-income and high-income countries, and to understand the sources of differences between these two groups of countries.

Multiple linear regression models evaluate the socio-economic factors that determine COVID-19 deaths in the two groups of countries. The Oaxaca–Blinder decomposition is used to examine sources of differences between these two groups.

Low-income countries report a significantly lower average number of COVID-19 deaths compared to high-income countries. Community mobility and the easiness of carrying the virus from one place to another are significant factors affecting the number of deaths, while life expectancy is only significant in high-income countries. Higher health expenditure is associated with more reported deaths in both high- and low-income countries. Factors such as the transport infrastructure system, life expectancy and the percent of expenditure on health lead to the differences in the number of deaths between high- and low-income countries.

Our study shows that mobility measures taken by individuals to limit the spread of the virus are important to prevent deaths in both high- and low-income countries. Additionally, our results suggest that countries with weak health institutions underestimate the number of deaths from COVID-19, especially low-income countries. The underestimation of COVID-19 deaths could be affecting a great number of people in poverty in low-income economies.

This paper contributes to the emerging literature on COVID-19 and its relation to socio-economic factors by examining the differences in reported between deaths between rates in low-income and high-income countries.

The global pandemic has had a considerable effect on organisations’ performance and development and on the daily lives of the general population. This study aims to analyse the recent literature on the topics of Covid-19 and sustainability and proposes to rethink and redefine sustainability with the intersection of human health as a fourth sustainable pillar.

Using the Scopus and ISI Web of Science databases, 119 articles were analysed in detail and classified according to concepts and principles for achieving sustainable development, based on the Brundtland Report, 1987.

The results indicate a high number of publications in the social dimension, with a relevant proportion of studies in the health sector. This study allows us to conclude that all sectors of society are being affected by the pandemic. However, the enormous tension and the immediate impact felt by the health sector during the pandemic reflect directly on the population, and there are clear signs that in the medium and long term, instability and uncertainty in the environmental, economic and social dimensions will remain. In national health systems, monitoring, innovating in human resource management and investing in information technology can ensure organizations’ reliability and sustainability. The conclusion involves the suggestion of introducing health as a new pillar for sustainability to consolidate the basis and structure of the United Nations’ Sustainable Development Objectives. The use of fundamental concepts is necessary and must be aligned to reassess the results obtained in studies, in comparison with observational data.

The implications arising from the inclusion of health as a fourth pillar of sustainability are diverse. The need to build a new theoretical and conceptual framework for sustainability derives from the fact that health reflects the concern of many postulants in this field of practices. The determining or conditioning conditions of the observed effects of the pandemic by COVID-19, whether situated simply as factors and/or economic, environmental or social reflexes that precede them, requires a conceptual development that allows its approach, as a complex object, whose determinations are subject to variable degrees of uncertainty and diversity.

This study aims to redefine the concept of sustainability, considering that health has become a public health emergency of international interest. Health affects the supply chain, cash flow, interferes with the educational format and interrupts the workforce’s routine, among other aspects, showing the true nature of its importance and its impact in all spheres (economic, environmental and social).

Educational policies, integrated practices, obliged strategies and notable benchmarks are always required by the higher educational institutions (HEIs) for operating business ventures into competent boundaries and to preside toward the overall new business density. The same are needed to be evaluated based on student's concerns for road-mapping sustainability. Accordingly, authors conducted present study to identify crucial quality characteristics (measures) under the origins of HEIs based on student's concerns using qualitative medium under Indian economy. The study is presenting critical dimensions and quality characteristics, which are seeking by the students for selecting HEIs for their studies.

Kano integrated-Grey-VIKOR approach is utilized in present study for road-mapping sustainability based on the determination of priority index and ranking. The study utilized three segments of methodology, where in the first segment, Kano technique is implicated to define priority index of quality characteristics. In the second segment, grey sets theory is implicated to capture the perceptions of the respondents. In the third segment, VIKOR technique is implicate to rank the HEIs.

The findings of the study will assist administrators in planning the prominent strategies that can embrace performance traits under HEI, which in turn will participate in growth and development of an economy. The findings have revealed “PPCS, ICMC, TSTR, PICM, AFEP, IMIS as Attractive performance characteristics,” “IEAF, OIAR, INET as One dimensional performance characteristics,” “QTCS, PORE, SIRD as Must-be performance characteristics” and “PQPE, PCTM as Indifferent performance characteristics.” Additionally, “Professional and placement characteristics of institute” is found as the most significant measure inspiring students for admiring engineering institutes. It is found that “Observance of institutional affiliation and recognition” and “Infrastructure, classroom management and control methods” are found as the second significant measures. “Patterns of question papers and evaluation medium” and “Personal characteristics of teacher and management” are found as the least competent characteristics admiring stakeholders for selecting HEI.

The present study can assist administrators in drafting refined policies and strategies for practising quality outputs by HEI. The study suggested critical quality characteristics, which in respond will aid in attracting more number of students toward educational institutes. A study under Indian context is demonstrated for presenting critical facts and attaining higher student's enrolment rates.

The purpose of this paper is to examine the impact of short sale prospect on future income smoothing.

This study examines how short sale prospect impacts future income smoothing. This study follows prior research and uses two measures of income smoothing. One is the correlation between the change in prediscretionary income and the change in discretionary accruals. The other is the variability of earnings relative to the variability of cash flows.

This study finds that short sale prospect has a negative impact on future income smoothing. This finding is robust to use different measures of short sale prospect and income smoothing and to subsample tests. Additional analysis reveals that short sale prospect, by curbing income smoothing, reduces future stock price crash risk.

To the best of the authors’ knowledge, this study is the first to examine the impact of short selling on firms’ subsequent smoothing of reported income. This study contributes to the earnings quality literature by demonstrating the governance role of short selling on future earnings smoothness.

The purpose of this study is to obtain an analytical solution for a nonlinear system of the COVID-19 model for susceptible, exposed, infected, isolated and recovered.

The Laplace decomposition method and the differential transformation method are used.

The obtained analytical results are useful on two fronts: first, they would contribute to a better understanding of the dynamic spread of the COVID-19 disease and help prepare effective measures for prevention and control. Second, researchers would benefit from these results in modifying the model to study the effect of other parameters such as partial closure, awareness and vaccination of isolated groups on controlling the pandemic.

The approach presented is novel in its implementation of the nonlinear system of the COVID-19 model