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The Covid-19 pandemic has already stressed health care systems throughout the world, requiring rationing of medical equipment and care. The authors discuss the ethical values relevant to health care rationing and provide six recommendations to guide fair allocation of scarce medical resources during the pandemic.

Physicians in northern Italy have learned some painful lessons about rationing care during an epidemic. As health care systems work out ethical allocation principles, it seems clear that only with transparency and inclusivity can public trust and cooperation be achieved.

The ongoing novel coronavirus disease (COVID-19) pandemic has already infected millions worldwide and, with no vaccine available, interventions to mitigate transmission are urgently needed. While there is broad agreement that travel restrictions and social distancing are beneficial in limiting spread, recommendations around face mask use are inconsistent. Here, we use mathematical modeling to examine the epidemiological impact of face masks, considering resource limitations and a range of supply and demand dynamics. Even with a limited protective effect, face masks can reduce total infections and deaths, and can delay the peak time of the epidemic. However, random distribution of masks is generally suboptimal; prioritized coverage of the elderly improves outcomes, while retaining resources for detected cases provides further mitigation under a range of scenarios. Face mask use, particularly for a pathogen with relatively common asymptomatic carriage, is an effective intervention strategy, while optimized distribution is important when resources are limited. Recommendations regarding the use of face masks as a preventive measure for COVID-19 are inconsistent. Here, the authors show that optimal distribution of surgical-standard face masks in the population, or universal coverage of homemade face coverings, could reduce total infections and deaths.

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.

As global populations age, a looming nursing shortage is anticipated to become a critical issue. Charge nurses have the responsibility of optimally allocating nursing resources to ensure the quality of patient care during a shift. Therefore, an accurate estimate of nursing demand is crucial. However, the ability to forecast future nursing demand remains underdeveloped, mainly because the nature of nursing demand is highly individualized and does not follow a definitive pattern. In practice, the nursing demand is often perceived as unpredictable, leading to an ad hoc approach to staffing. The primary objective of our study is to demonstrate that longitudinal data analysis can reveal strong statistical regularities in the temporal dynamics of nursing demand. This approach not only provides new possibilities for efficient resource allocation but also paves the way for data‐driven prediction of nursing demand. Our study uses Sankey diagrams to visualize the temporal dynamics of nursing demand within each ward for each fiscal year, representing these dynamics as an overlay of trajectories from multiple individual patients. Consequently, our study reveals ward‐specific statistical regularities in the temporal dynamics of nursing demand. In one ward, approximately 25% of patients experienced an increase in nursing demand from 1 to between 6 and 9 points from the second to the third day of hospitalization, while in another, only 0.1% showed such an increase. These findings suggest that patients admitted to the wards tend to exhibit a certain probabilistic change in nursing demand. This study can predict probabilistically the temporal variation of nursing demand among patients in the coming years by analyzing data on the temporal changes in nursing demand over the past years. Our findings are expected to significantly influence the forecasting of nursing demand and the estimation of nursing resources, leading to data‐driven and more efficient nursing management.

Irene Agyepong and colleagues share experiences and ideas to strengthen capacity for health research co-production in low and middle income countries

The adoption of the shared prosperity goal by the World Bank in 2013 and Sustainable Development Goal 10, on inequality, by the United Nations in 2015 should strengthen the focus of development interventions and cooperation on the income growth of the bottom 40 percent of the income distribution. This paper contributes to the incipient literature on within-country allocations of development institutions and assesses the geographic targeting of World Bank projects to the bottom 40 percent. Bivariate correlations between the allocation of project funding approved over 2005-14 and the geographical distribution of the bottom 40 as measured by survey income or consumption data are complemented by regressions with population and other potential factors affecting the within-country allocations as controls. The correlation analysis shows that, of the 58 countries in the sample, 41 exhibit a positive correlation between the shares of the bottom 40 and World Bank funding, and, in almost half of these, the correlation is above 0.5. Slightly more than a quarter of the countries, mostly in Sub-Saharan Africa, exhibit a negative correlation. The regression analysis shows that, once one controls for population, the correlation between the bottom 40 and World Bank funding switches sign and becomes significant and negative on average. This is entirely driven by Sub-Saharan Africa and not observed in the other regions. Hence, the significant and positive correlation in the estimations without controlling for population suggests that World Bank project funding is concentrated in administrative areas in which more people live (including the bottom 40) rather than in poorer administrative areas. Furthermore, capital cities receive disproportionally high shares of World Bank funding on average.

Background The inequitable allocation of health resources is a worldwide problem, and it is also one of the obstacles facing for health services utilization in China. A new round of health care reform which contains the important aspect of improving the equity in health resource allocation was released by Chinese government in 2009. The aim of this study is to understand the changes of equity in health resource allocation from 2009 to 2013, and make a further inquiry of the main factors which influence the equity conditions in China. Methods Data resources are the China Health Statistics Yearbook (2014) and the China Statistical Yearbook (2014). Four indicators were chosen to measure the trends in equity of health resource allocation. Data were disaggregated by three geographical regions: west, central, and east. Theil index was used to calculate the degree of unfairness. Results The total amount of health care resources in China had been increasing in recent years. However, the per 10, 000 km 2 number of health resources showed a huge gap in different regions, and per 10, 000 capita health resources ownership showed a relatively small disparities at the same time. The index of health resources showed an overall downward trend, in which health financial investment the most unfair from 2009 to 2012 and the number of health institutions the most unfair in 2013. The equity of health resources allocation in eastern regions was the worst except for the aspect of health technical personnel allocation. The regional contribution rates were lower than that of the inter-regional contribution rates which were all beyond 60 %. Conclusion The equity of health resource allocation improved gradually from 2009 to 2013. However, the internal differences within the eastern region still have a huge impact on the overall equity in health resource allocation. The tough issues of inequitable in health resource allocation should be resolved by comprehensive measures from a multidisciplinary perspective.

The efficacy of influenza vaccines varies from one year to the next, with efficacy during the 2017–2018 season anticipated to be lower than usual. However, the impact of low-efficacy vaccines at the population level and their optimal age-specific distribution have yet to be ascertained. Applying an optimization algorithm to a mathematical model of influenza transmission and vaccination in the United States, we determined the optimal age-specific uptake of low-efficacy vaccine that would minimize incidence, hospitalization, mortality, and disability-adjusted life-years (DALYs), respectively. We found that even relatively low-efficacy influenza vaccines can be highly impactful, particularly when vaccine uptake is optimally distributed across age groups. As vaccine efficacy declines, the optimal distribution of vaccine uptake shifts toward the elderly to minimize mortality and DALYs. Health practitioner encouragement and concerted recruitment efforts are required to achieve optimal coverage among target age groups, thereby minimizing influenza morbidity and mortality for the population overall.