Efforts to curtail the spread of the novel coronavirus (SARS-CoV2) have led to the unprecedented concurrent confinement of nearly two-thirds of the global population. The large human lockdown and its ...eventual relaxation can be viewed as a Global Human Confinement Experiment. This experiment is a unique opportunity to identify positive and negative effects of human presence and mobility on a range of natural systems, including wildlife, and protected areas, and to study processes regulating biodiversity and ecosystems. We encourage ecologists, environmental scientists, and resource managers to contribute their observations to efforts aiming to build comprehensive global understanding based on multiple data streams, including anecdotal observations, systematic assessments and quantitative monitoring. We argue that the collective power of combining diverse data will transcend the limited value of the individual data sets and produce unexpected insights. We can also consider the confinement experiment as a “stress test” to evaluate the strengths and weaknesses in the adequacy of existing networks to detect human impacts on natural systems. Doing so will provide evidence for the value of the conservation strategies that are presently in place, and create future networks, observatories and policies that are more adept in protecting biological diversity across the world.
•4.5 billion humans have been confined to control the COVID-19 epidemic, reaching a maximum on April 5.•This “Global Human Confinement Experiment” offers understanding of how human presence and activity affects nature.•Parallel data streams identify strong responses across socio-ecological systems.•Observation networks with global capacity are needed to identify strategic actions.•Large-scale societal change is possible.
Abstract
The iconic planetary nebula (PN) NGC 7027 is bright, nearby (
D
∼ 1 kpc), highly ionized, intricately structured, and well observed. This nebula is hence an ideal case study for ...understanding PN shaping and evolution processes. Accordingly, we have conducted a comprehensive imaging survey of NGC 7027 comprised of 12 HST Wide Field Camera 3 images in narrow-band and continuum filters spanning the wavelength range 0.243–1.67
μ
m. The resulting panchromatic image suite reveals the spatial distributions of emission lines covering low-ionization species such as singly ionized Fe, N, and Si, through H recombination lines, to more highly ionized O and Ne. These images, combined with available X-ray and radio data, provide the most extensive view of the structure of NGC 7027 obtained to date. Among other findings, we have traced the ionization structure and dust extinction within the nebula in subarcsecond detail; uncovered multipolar structures actively driven by collimated winds that protrude through and beyond the PN’s bright inner core; compared the ionization patterns in the WFC3 images to X-ray and radio images of its interior hot gas and to its molecular outflows; pinpointed the loci of thin, shocked interfaces deep inside the nebula; and more precisely characterized the central star. We use these results to describe the recent history of this young and rapidly evolving PN in terms of a series of shaping events. This evolutionary sequence involves both thermal and ram pressures, and is far more complex than predicted by extant models of UV photoionization or winds from a single central progenitor star, thereby highlighting the likely influence of an unseen binary companion.
In this paper we first describe the class of log-Gaussian Cox processes (LGCPs) as models for spatial and spatio-temporal point process data. We discuss inference, with a particular focus on the ...computational challenges of likelihood-based inference. We then demonstrate the usefulness of the LGCP by describing four applications: estimating the intensity surface of a spatial point process; investigating spatial segregation in a multi-type process; constructing spatially continuous maps of disease risk from spatially discrete data; and real-time health surveillance. We argue that problems of this kind fit naturally into the realm of geostatistics, which traditionally is defined as the study of spatially continuous processes using spatially discrete observations at a finite number of locations. We suggest that a more useful definition of geostatistics is by the class of scientific problems that it addresses, rather than by particular models or data formats.
Abstract
NGC 6302 (The Butterfly Nebula) is an extremely energetic and rapidly expanding bipolar planetary nebula (PN). If the central source is a single star, then its apparent location in an H-R ...diagram places it among the most massive, hottest, and presumably rapidly evolving of all central stars of PNe. Our proper motion study of NGC 6302, based on Hubble Space Telescope WFC3 images spanning 11 yr, has uncovered at least four different pairs of uniformly expanding internal lobes ejected at various times and orientations over the past two millennia at speeds ranging from 10–600 km s
−1
. In addition, we find a pair of collimated off-axis flows in constant motion at ∼770 ± 100 km s
−1
within which bright Fe
ii
feathers
are conspicuous. Combining our results with those previously published, we find that the ensemble of flows has an ionized mass >0.1
M
⊙
and its kinetic energy, between 10
46
and 10
48
erg, lies at the upper end of gravity-powered PNe ejection processes such as stellar mergers or mass accretion. We assemble our results into a plausible historical timeline of ejections from the nucleus and suggest that the ejections are powered by gravitational infall.
Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ...ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
Spatial and spatio-temporal data are used in a wide range of fields including environmental, health and social disciplines. Several packages in the statistical software R have been recently developed ...as clients for various databases to meet the growing demands for easily accessible and reliable spatial data. While documentation on how to use many of these packages exist, there is an increasing need for a one stop repository for tutorials on this information. In this paper, we present
rspatialdata
a website that provides a collection of data sources and tutorials on downloading and visualising spatial data using R. The website includes a wide range of datasets including administrative boundaries of countries, Open Street Map data, population, temperature, vegetation, air pollution, and malaria data. The goal of the website is to equip researchers and communities with the tools to engage in spatial data analysis and visualisation so that they can address important local issues, such as estimating air pollution, quantifying disease burdens, and evaluating and monitoring the United Nation's sustainable development goals.
The challenge of achieving maternal and neonatal health-related goals in developing countries is significantly impacted by high fertility rates, which are partly attributed to limited access to ...family planning and access to the healthcare systems. The most widely used indicator to monitor family planning coverage is the proportion of women in reproductive age using contraception (CPR). However, this metric does not accurately reflect the true family planning coverage, as it fails to account for the diverse needs of women in reproductive age. Not all women in this category require contraception, including those who are pregnant, wish to become pregnant, sexually inactive, or infertile. To effectively address the contraceptive needs of those who require it, this study aims to estimate family planning coverage among this specific group. Further, we aimed to explore the geographical variation and factors influencing contraceptive uptake of contraceptive use among those who need.
We used data from the Performance Monitoring for Action Ethiopia (PMA Ethiopia) survey of women of reproductive age and the service delivery point (SDP) survey conducted in 2019. A total of 4,390 women who need contraception were considered as the analytical sample. To account for the study design, sampling weights were considered to compute the coverage of modern contraceptive use disaggregated by socio-demographic factors. Bayesian geostatistical modeling was employed to identify potential factors associated with the uptake of modern contraception and produce spatial prediction to unsampled locations.
The overall weighted prevalence of modern contraception use among women who need it was 44.2% (with 95% CI: 42.4%-45.9%). Across regions of Ethiopia, contraceptive use coverage varies from nearly 0% in Somali region to 52.3% in Addis Ababa. The average nearest distance from a woman's home to the nearest SDP was high in the Afar and Somali regions. The spatial mapping shows that contraceptive coverage was lower in the eastern part of the country. At zonal administrative level, relatively high (above 55%) proportion of modern contraception use coverage were observed in Adama Liyu Zone, Ilu Ababor, Misrak Shewa, and Kefa zone and the coverage were null in majority of Afar and Somali region zones. Among modern contraceptive users, use of the injectable dominated the method-mix. The modeling result reveals that, living closer to a SDP, having discussions about family planning with the partner, following a Christian religion, no pregnancy intention, being ever pregnant and being young increases the likelihood of using modern contraceptive methods.
Areas with low contraceptive coverage and lower access to contraception because of distance should be prioritized by the government and other supporting agencies. Women who discussed family planning with their partner were more likely to use modern contraceptives unlike those without such discussion. Thus, to improve the coverage of contraceptive use, it is very important to encourage/advocate women to have discussions with their partner and establish movable health systems for the nomadic community.
Childhood overweight and obesity levels are rising and becoming a concern globally. In Costa Rica, the prevalence of these conditions has reached alarming values. Spatial analyses can identify risk ...factors and geographical patterns to develop tailored and effective public health actions in this context.
A Bayesian spatial mixed model was built to understand the geographic patterns of childhood overweight and obesity prevalence in Costa Rica and their association with some socioeconomic factors. Data was obtained from the 2016 Weight and Size Census (6 - 12 years old children) and 2011 National Census.
Average years of schooling increase the levels of overweight and obesity until reaching an approximate value of 8 years, then they start to decrease. Moreover, for every 10-point increment in the percentage of homes with difficulties to cover their basic needs and in the percentage of population under 14 years old, there is a decrease of 7.7 and 14.0 points, respectively, in the odds of obesity. Spatial patterns show higher values of prevalence in the center area of the country, touristic destinations, head of province districts and in the borders with Panama.
Especially for childhood obesity, the average years of schooling is a non-linear factor, describing a U-inverted curve. Lower percentages of households in poverty and population under 14 years old are slightly associated with higher levels of obesity. Districts with high commercial and touristic activity present higher prevalence risk.
Monitoring SARS-CoV-2 spread and evolution through genome sequencing is essential in handling the COVID-19 pandemic. Here, we sequenced 892 SARS-CoV-2 genomes collected from patients in Saudi Arabia ...from March to August 2020. We show that two consecutive mutations (R203K/G204R) in the nucleocapsid (N) protein are associated with higher viral loads in COVID-19 patients. Our comparative biochemical analysis reveals that the mutant N protein displays enhanced viral RNA binding and differential interaction with key host proteins. We found increased interaction of GSK3A kinase simultaneously with hyper-phosphorylation of the adjacent serine site (S206) in the mutant N protein. Furthermore, the host cell transcriptome analysis suggests that the mutant N protein produces dysregulated interferon response genes. Here, we provide crucial information in linking the R203K/G204R mutations in the N protein to modulations of host-virus interactions and underline the potential of the nucleocapsid protein as a drug target during infection.
The detection of regions with unusually high risk plays an important role in disease mapping and the analysis of public health data. In particular, the detection of groups of areas (i.e., clusters) ...where the risk is significantly high is often conducted by public health authorities. Many methods have been proposed for the detection of these disease clusters, most of them based on moving windows, such as Kulldorff's spatial scan statistic. Here we describe a model-based approach for the detection of disease clusters implemented in the DClusterm package. Our model-based approach is based on representing a large number of possible clusters by dummy variables and then fitting many generalized linear models to the data where these covariates are included one at a time. Cluster detection is done by performing a variable or model selection among all fitted models using different criteria. Because of our model-based approach, cluster detection can be performed using different types of likelihoods and latent effects. We cover the detection of spatial and spatiotemporal clusters, as well as how to account for covariates, zero-inflated datasets and overdispersion in the data.
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