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In China, more than 80,000 people have been diagnosed with COVID-19, and more than 3,000 people have lost their lives. It seems that there will be more deaths since the epidemic is not over. All the Chinese provinces have reported the COVID-19 cases. This chapter aims to explore the trend of COVID-19 treatment efficiency in Chinese provinces using the data released daily by China Center for Disease Control and Prevention. Since China Center for Disease Control and Prevention began to release data daily from January 24 to March 12, we have more than 40 groups of daily data for 31 provinces in China mainland. In the calculation, we take the daily data of each province as a sample and then we have more than 1,200 samples in this study.

We use additive two-stage data envelopment analysis as an efficiency evaluation tool to calculate the COVID-19 treatment efficiency. In our framework, the first stage is to understand the infection rate and the second stage is to evaluate the treatment efficiency. In the first stage for the tth day, we use total population (p) and number of people infected in the previous day (inf _t−1) as the inputs and cumulative number of people infected in the current day (inf _t ) as the output. In the second stage for the tth day, we use cumulative number of people infected in the current day (inf _t ) as the input and cumulative death in the current day (death _t ) and cumulative recovery in the current day (recov _t ) as the outputs. Some techniques on how to deal with undesirable outputs such as inf _t and death _t are employed in this study.

After we have the infection rate and treatment efficiency for the samples more than 1,200, we analyze the COVID-19 treatment efficiency and its development trend from January 24 to March 12 in 34 regions of China from static and dynamic aspects. The results show that, on the whole, the overall efficiency and phased efficiency of COVID-19 treatment efficiency in all regions of China are relatively high, which reflects the key factor for the Chinese government to quickly control the epidemic in the short term. Relatively speaking, the average efficiency value in the infection stage (first stage) is lower than the average efficiency value in the healing stage (second stage), which shows that the focus of anti-epidemic in China should be early detection and prevention rather than treatment process. In terms of trend, the total efficiency of COVID-19 treatment in each region shows a trend of “increasing first and then decreasing.” Our analysis indicates that in the initial stage, the continuous increase of various resources leads to the rise of the total efficiency, while in the later stage, the rapid decline of the number of infected people leads to the decrease of the total efficiency. Based on the results of the efficiency analysis, this study provides corresponding management implications and policy suggestions, hoping to provide some enlightenment and suggestions for the anti-epidemic work of other countries in the severe environment where the epidemic is spreading rapidly.

We study changes in age-specific diabetes-related mortality and annual health care utilization. We find that half of the estimated 16% increase of diabetic mortality falls within employable age groups. We estimate that disease combination-specific increase in case fatality has resulted in premature diabetic mortality costing $3.2 billion annually. The estimated annual direct cost of treating high-risk diabetics reaches $36 billion, of which Medicare and Other Federal Programs compensate 54%. Respiratory conditions among diabetics comprise the same proportion of high-risk diabetics as do the disease combinations including coronary heart diseases. Treating of general diabetic conditions has become more efficient as indicated by the estimated declines in per unit health care costs.

The COVID-19 pandemic has had a profound impact on the world, particularly on the international travel and hospitality industry, which is highly sensitive to such disruptions. The global tourism industry has been severely impacted by the COVID-19 pandemic, leading to extensive discussions and examinations of the relationship between the virus and tourism in international tourism literature. Based on this premise, this chapter presents the results of a bibliometric study analyzing the COVID-19 literature in tourism. The study aims to identify current research areas and provide recommendations for future research. A total of 537 studies, comprising 477 articles and 60 other types, were included in the analysis. Analysis was conducted using R tools to identify and discuss the most pertinent sources, authors, affiliations, and countries. The most frequently cited countries and documents were also examined. The 537 studies that were examined in these discussions have developed various propositions. It is desirable for these propositions and other research findings to serve as a roadmap for potential research opportunities.

The COVID-19 pandemic has recently had an impact on the stock market all over the globe. A thorough review of the literature that included the most cited articles and articles from well-known databases revealed that earlier research in the field had not specifically addressed how the BRIC stock markets responded to the COVID-19 pandemic. The data regarding COVID-19 were collected from the World Health Organization (WHO) website, and the stock market data were collected from Yahoo Finance and the respective country’s stock exchange. A random forest regression algorithm takes the closing price of respective stock indices as target variables and COVID-19 variables as input variables. Using this algorithm, a model is fit to the data and is visualised using line plots. This study’s findings highlight a relationship between the COVID-19 variables and stock market indices. In addition, the stock market of BRIC countries showed a high correlation, especially with the Shanghai Composite Stock Index with a correlation value of 0.7 and above. Brazil took the worst hit in the studied duration by declining approximately 45.99%, followed by India by 37.76%. Finally, the data set’s model fit, which employed the random forest machine learning method, produced R² values of 0.972, 0.005, 0.997, and 0.983 and mean percentage errors of 1.4, 0.8, 0.9, and 0.8 for Brazil, Russia, India, and China (BRIC), respectively. Even now, two years after the coronavirus pandemic started, the Brazilian stock index has not yet returned to its pre-pandemic level.

Using data from the Survey of Income and Program Participation (SIPP) matched to administrative records, we examine mortality risk and participation in the Disability Insurance (DI) and Supplemental Security Income (SSI) disability programs from a long-term perspective. Over a period of 14 years, we analyze the effect of self-reported health and disability on the probability of death and disability program entry among individuals aged 18–48 in 1984. We also assess DI and SSI programs from a life-cycle perspective. Self-reported poor health and severe disability at baseline are strongly correlated with death over the 14-year follow-up period. These variables also are strong predictors of disability program participation over the follow-up period among non-participants at baseline or before, with increasing marginal probabilities in the out-years. Our cross-sectional models are consistent with recent studies that find that the work-prevented measure is useful in modeling DI entry. However, once self-reported health and functional limitations are accounted for, the longitudinal entry models provide conflicting DI results for the work-prevented measure, suggesting that, contrary to claims based on cross-sectional or short-time horizon application models, the work-prevented measure is an unreliable indicator of severity. The risk of SSI and DI participation is significantly greater for individuals who die, suggesting that future mortality captures the effect of case severity and deterioration of health during the follow-up period. From a life-cycle perspective, a substantially greater proportion of individuals participate in SSI or DI at some point in their lives compared to typical cross-sectional estimates of participation, especially among minorities, people with less than a high school education, and those with early onset of poor health and/or disabilities. Cross-sectional estimates for the Social Security area population indicate SSI and DI participation rates of no more than 5% combined in 2000. In contrast, for individuals aged 43–48 in 1984, we observe a cumulative lifetime SSI and/or DI participation rate of 14%. The corresponding figure is 32% for individuals in that age group who did not graduate from high school, suggesting the need for human capital investments and/or improved work incentives.

This chapter investigates the impact of the COVID-19 pandemic on economic stimulus policies. Based on data from 156 economies, empirical results show that in the medium term, cumulative effect of COVID-19 pandemic is positively correlated with the economic stimulus policies but not in the short term. Heterogeneity tests show that while economic policies are used in developed economies more often, restrictive measures in developing countries are likely used as a substitution; deaths have a positive impact on economic stimulus policies but confirmed cases not. The results suggest that the pandemic may reinforce economic inequality due to potential stimulus policy capabilities, requiring international coordination and assistance to low-and-middle income countries in various aspects.

Climate change and the COVID-19 pandemic are complex and have multifaceted effects on countries in an unpredictable and unprecedented manner. While both COVID-19 and the climate crisis share similarities, they also have some notable differences. Being both systemic in nature with knock-on and cascading effects that propagate due to high connectedness of countries, COVID-19, however, presents imminent and directly visible dangers, while the risks from climate change are gradual, cumulative and often distributed dangers. Climate change has more significant medium and long-term impacts which are likely to worsen over time. There is no vaccine for climate change compared to COVID-19. In addition, those most affected by extreme climatic conditions have usually contributed the least to the root causes of the crisis. This is in fact the case of island economies. The chapter thus investigates into the vulnerability and resilience of 38 Small Islands Developing States (SIDs) to both shocks. Adopting a comprehensive conceptual framework and data on various indices from the literature and global databases, we assess the COVID-19 and climate change vulnerabilities of SIDs on multiple fronts. The results first reveal a higher vulnerability across all dimensions for the Pacific islands compared to the other islands in the sample. There is also evidence of a weak correlation between climate change risk and the COVID-19 pandemic confirming our premise that there are marked differences between these two shocks and their impacts on island communities.

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.

The interventions in section one (e.g., public policy initiatives, community-based intervention programs) discuss ways to reduce or alleviate health disparities among ethnic populations and provide careful descriptions of factors that increased or decreased the success of these interventions (e.g., barriers, resiliency, protective factors, and cultural competence).