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The next decade is likely to produce any number of global challenges that will affect health and health care, including pan-national infections such as the new coronavirus COVID-19 and others that will be related to global warming. Nurses will be required to react to these events, even though they will also be affected as ordinary citizens. The future resilience of healthcare services will depend on having sufficient numbers of nurses who are adequately resourced to face the coming challenges.

A series of epidemiological explorations has suggested a negative association between national bacillus Calmette–Guérin (BCG) vaccination policy and the prevalence and mortality of coronavirus disease 2019 (COVID-19). However, these comparisons are difficult to validate due to broad differences between countries such as socioeconomic status, demographic structure, rural vs. urban settings, time of arrival of the pandemic, number of diagnostic tests and criteria for testing, and national control strategies to limit the spread of COVID-19. We review evidence for a potential biological basis of BCG cross-protection from severe COVID-19, and refine the epidemiological analysis to mitigate effects of potentially confounding factors (e.g., stage of the COVID-19 epidemic, development, rurality, population density, and age structure). A strong correlation between the BCG index, an estimation of the degree of universal BCG vaccination deployment in a country, and COVID-19 mortality in different socially similar European countries was observed (r2 = 0.88; P = 8 × 10−7), indicating that every 10% increase in the BCG index was associated with a 10.4% reduction in COVID-19 mortality. Results fail to confirm the null hypothesis of no association between BCG vaccination and COVID-19 mortality, and suggest that BCG could have a protective effect. Nevertheless, the analyses are restricted to coarse-scale signals and should be considered with caution. BCG vaccination clinical trials are required to corroborate the patterns detected here, and to establish causality between BCG vaccination and protection from severe COVID-19. Public health implications of a plausible BCG cross-protection from severe COVID-19 are discussed.

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Abstract. Emerging infectious diseases can present serious threats to wildlife, even to the point of causing extinction. Whitenose fungus (Pseudogymnoascus destructans) is causing an epizootic in bats that is expanding rapidly, both geographically and taxonomically. Little is known of the ecology and distributional potential of this intercontinental pathogen. We address this gap via ecological niche models that characterise coarse resolution niche differences between fungus populations on different continents, identifying areas potentially vulnerable to infection in South America. Here we explore a novel approach to identifying areas of potential distribution across novel geographic regions that avoids perilious extrapolation into novel environments. European and North American fungus populations show differential use of environmental space, but rather than niche differentiation, we find that changes are best attributed to climatic differences between the two continents. Suitable areas for spread of the pathogen were identified across southern South America; however caution should be taken to avoid underestimating the potential for spread of this pathogen in South America.

Robust methods by which to generate virtual species are needed urgently in the emerging field of distributional ecology to evaluate performance of techniques for modeling ecological niches and species distributions and to generate new questions in biogeography. Virtual species provide the opportunity to test hypotheses and methods based on known and unbiased distributions. We present Niche Analyst (NicheA), a toolkit developed to generate virtual species following the Hutchinsonian approach of an n‐multidimensional space occupied by the species. Ecological niche models are generated, analyzed, and visualized in an environmental space, and then projected to the geographic space in the form of continuous or binary species distribution models. NicheA is implemented in a stable and user‐friendly Java platform. The software, online manual, and user support are freely available at < http://nichea.sourceforge.net >.

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Ecological niche modeling (ENM) is used widely to study species’ geographic distributions. ENM applications frequently involve transferring models calibrated with environmental data from one region to other regions or times that may include novel environmental conditions. When novel conditions are present, transferability implies extrapolation, whereas, in absence of such conditions, transferability is an interpolation step only. We evaluated transferability of models produced using 11 ENM algorithms from the perspective of interpolation and extrapolation in a virtual species framework. We defined fundamental niches and potential distributions of 16 virtual species distributed across Eurasia. To simulate real situations of incomplete understanding of species’ distribution or existing fundamental niche (environmental conditions suitable for the species contained in the study area; N* F ), we divided Eurasia into six regions and used 1–5 regions for model calibration and the rest for model evaluation. The models produced with the 11 ENM algorithms were evaluated in environmental space, to complement the traditional geographic evaluation of models. None of the algorithms accurately estimated the existing fundamental niche (N* F ) given one region in calibration, and model evaluation scores decreased as the novelty of the environments in the evaluation regions increased. Thus, we recommend quantifying environmental similarity between calibration and transfer regions prior to model transfer, providing an avenue for assessing uncertainty of model transferability. Different algorithms had different sensitivity to completeness of knowledge of N* F , with implications for algorithm selection. If the goal is to reconstruct fundamental niches, users should choose algorithms with limited extrapolation when N* F is well known, or choose algorithms with increased extrapolation when N* F is poorly known. Our assessment can inform applications of ecological niche modeling transference to anticipate species invasions into novel areas, disease emergence in new regions, and forecasts of species distributions under future climate conditions.