137 10 20655 https://www.socictopen.socict.org/files/original/d30bda462bac2a894465b54f8d109ade.pdf 892cddd2a4c935f9d4cce1ea5ef44c0b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource CoMeta: classification of metagenomes using k-mers. Creator An entity primarily responsible for making the resource Jolanta Kawulok, Sebastian Deorowicz Description An account of the resource Nowadays, the study of environmental samples has been developing rapidly. Characterization of the environment composition broadens the knowledge about the relationship between species composition and environmental conditions. An important element of extracting the knowledge of the sample composition is to compare the extracted fragments of DNA with sequences derived from known organisms. In the presented paper, we introduce an algorithm called CoMeta (Classification of metagenomes), which assigns a query read (a DNA fragment) into one of the groups previously prepared by the user. Typically, this is one of the taxonomic rank (e.g., phylum, genus), however prepared groups may contain sequences having various functions. In CoMeta, we used the exact method for read classification using short subsequences (k-mers) and fast program for indexing large set of k-mers. In contrast to the most popular methods based on BLAST, where the query is compared with each reference sequence, we begin the classification from the top of the taxonomy tree to reduce the number of comparisons. The presented experimental study confirms that CoMeta outperforms other programs used in this context. CoMeta is available at https://github.com/jkawulok/cometa under a free GNU GPL 2 license. Date A point or period of time associated with an event in the lifecycle of the resource 2015 Identifier An unambiguous reference to the resource within a given context DOI: 10.1371/journal.pone.0121453 Source A related resource from which the described resource is derived PLoS ONE Publisher An entity responsible for making the resource available Public Library of Science (PLoS) Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Science, Medicine Language A language of the resource EN https://www.socictopen.socict.org/files/original/5617eb0edebc422e9df5f0a3dca7513e.pdf d9e168f43f6784f0e0a17f6e56833054 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Serological Evidence of MERS-CoV Antibodies in Dromedary Camels (Camelus dromedaries) in Laikipia County, Kenya. Creator An entity primarily responsible for making the resource Sharon L. Deem, Eric M. Fèvre, Margaret Kinnaird, A. Springer Browne, Dishon Muloi, Gert-Jan Godeke, Marion Koopmans, Chantal B Reusken Description An account of the resource Middle East respiratory syndrome coronavirus (MERS-CoV) is a recently identified virus causing severe viral respiratory illness in people. Little is known about the reservoir in the Horn of Africa. In Kenya, where no human MERS cases have been reported, our survey of 335 dromedary camels, representing nine herds in Laikipia County, showed a high seroprevalence (46.9%) to MERS-CoV antibodies. Between herd differences were present (14.3%- 82.9%), but was not related to management type or herd isolation. Further research should focus on identifying similarity between MERS-CoV viral isolates in Kenya and clinical isolates from the Middle East and elsewhere. Date A point or period of time associated with an event in the lifecycle of the resource 2015 Identifier An unambiguous reference to the resource within a given context DOI: 10.1371/journal.pone.0140125 Source A related resource from which the described resource is derived PLoS ONE Publisher An entity responsible for making the resource available Public Library of Science (PLoS) Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Science, Medicine Language A language of the resource EN https://www.socictopen.socict.org/files/original/d2c5f72eeea641c5d4c12b246933331c.pdf 884e31e4af591dfa4150a071d9b18c89 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 2019-nCoV: The Identify-Isolate-Inform (3I) Tool Applied to a Novel Emerging Coronavirus Creator An entity primarily responsible for making the resource Christian K. Beÿ, Eric C. McDonald Description An account of the resource 2019 Novel Coronavirus (2019-nCoV) is an emerging infectious disease closely related to MERS-CoV and SARS-CoV that was first reported in Wuhan City, Hubei Province, China in December 2019. As of January 2020, cases of 2019-nCoV are continuing to be reported in other Eastern Asian countries as well as in the United States, Europe, Australia, and numerous other countries. An unusually high volume of domestic and international travel corresponding to the beginning of the 2020 Chinese New Year complicated initial identification and containment of infected persons. Due to the rapidly rising number of cases and reported deaths, all countries should be considered at risk of imported 2019-nCoV. Therefore, it is essential for prehospital, clinic, and emergency department personnel to be able to rapidly assess 2019-nCoV risk and take immediate actions if indicated. The Identify-Isolate-Inform (3I) Tool, originally conceived for the initial detection and management of Ebola virus and later adjusted for other infectious agents, can be adapted for any emerging infectious disease. This paper reports a modification of the 3I Tool for use in the initial detection and management of patients under investigation for 2019-nCoV. After initial assessment for symptoms and epidemiological risk factors, including travel to affected areas and exposure to confirmed 2019-nCoV patients within 14 days, patients are classified in a risk-stratified system. Upon confirmation of a suspected 2019-nCoV case, affected persons must immediately be placed in airborne infection isolation and the appropriate public health agencies notified. This modified 3I Tool will assist emergency and primary care clinicians, as well as out-of-hospital providers, in effectively managing persons with suspected or confirmed 2019-nCoV. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Identifier An unambiguous reference to the resource within a given context DOI: 10.5811/westjem.2020.1.46760 Source A related resource from which the described resource is derived Western Journal of Emergency Medicine Publisher An entity responsible for making the resource available eScholarship Publishing, University of California Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine, Medical emergencies. Critical care. Intensive care. First aid Language A language of the resource EN https://www.socictopen.socict.org/files/original/e53552b33c1d0da5942acf97df22f91c.pdf a19948e8ba5bc7ff5b8e4edbaffed148 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Effective Risk Communication for Public Health Emergency: Reflection on the COVID-19 (2019-nCoV) Outbreak in Wuhan, China Creator An entity primarily responsible for making the resource Li-Wei Zhang, Hui-Jie Li, Kelin Chen Description An account of the resource Risk communication is critical to emergency management. The objective of this paper is to illustrate the effective process and attention points of risk communication reflecting on the COVID-19 (2019-nCoV) outbreak in Wuhan, China. We provide the timeline of risk communication progress in Wuhan and use a message-centered approach to identify problems that it entailed. It was found that the delayed decision making of the local government officials and the limited information disclosure should be mainly responsible for the ineffective risk communication. The principles for effective risk communication concerning Wuhan’s outbreak management were also discussed. The whole communication process is suggested to integrate the accessibility and openness of risk information, the timing and frequency of communication, and the strategies dealing with uncertainties. Based on these principles and lessons from Wuhan’s case, this paper employed a simplified Government−Expert−Public risk communication model to illustrate a collaborative network for effective risk communication. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource risk communication, Public Health, outbreak management, COVID-19 Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/healthcare8010064 Source A related resource from which the described resource is derived Healthcare Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine Language A language of the resource EN https://www.socictopen.socict.org/files/original/4d5f564ca65ac0b1dc63f8a6df0371cc.pdf 8e252c9a4abff6b8ff9fefe168b4a626 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Disrupting the LC3 Interaction Region (LIR) Binding of Selective Autophagy Receptors Sensitizes AML Cell Lines to Cytarabine Creator An entity primarily responsible for making the resource Mateusz Putyrski, Olesya Vakhrusheva, Florian Bonn, Suchithra Guntur, Andrew Vorobyov, Christian Brandts, Ivan Dikic, Andreas Ernst Description An account of the resource Short linear motifs (SLiMs) located in disordered regions of multidomain proteins are important for the organization of protein–protein interaction networks. By dynamic association with their binding partners, SLiMs enable assembly of multiprotein complexes, pivotal for the regulation of various aspects of cell biology in higher organisms. Despite their importance, there is a paucity of molecular tools to study SLiMs of endogenous proteins in live cells. LC3 interacting regions (LIRs), being quintessential for orchestrating diverse stages of autophagy, are a prominent example of SLiMs and mediate binding to the ubiquitin-like LC3/GABARAP family of proteins. The role of LIRs ranges from the posttranslational processing of their binding partners at early stages of autophagy to the binding of selective autophagy receptors (SARs) to the autophagosome. In order to generate tools to study LIRs in cells, we engineered high affinity binders of LIR motifs of three archetypical SARs: OPTN, p62, and NDP52. In an array of in vitro and cellular assays, the engineered binders were shown to have greatly improved affinity and specificity when compared with the endogenous LC3/GABARAP family of proteins, thus providing a unique possibility for modulating LIR interactions in living systems. We exploited these novel tools to study the impact of LIR inhibition on the fitness and the responsiveness to cytarabine treatment of THP-1 cells – a model for studying acute myeloid leukemia (AML). Our results demonstrate that inhibition of LIR of a single autophagy receptor is insufficient to sensitize the cells to cytarabine, while simultaneous inhibition of three LIR motifs in three distinct SARs reduces the IC50 of the chemotherapeutic. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource phage display, selective autophagy receptor, LIR interaction, cytarabine, AML—acute myeloid leukemia, inhibitors Identifier An unambiguous reference to the resource within a given context DOI: 10.3389/fcell.2020.00208 Source A related resource from which the described resource is derived Frontiers in Cell and Developmental Biology Publisher An entity responsible for making the resource available Frontiers Media S.A. Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General) Language A language of the resource EN https://www.socictopen.socict.org/files/original/d639e6199434f6ea4aae6e89dcdd970b.pdf 92cc5c46226a111f506dfd3c6a0e5279 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Incorporating media data into a model of infectious disease transmission. Creator An entity primarily responsible for making the resource Louis Kim, Shannon M Fast, Natasha Markuzon Description An account of the resource Understanding the effect of media on disease spread can help improve epidemic forecasting and uncover preventive measures to slow the spread of disease. Most previously introduced models have approximated media effect through disease incidence, making media influence dependent on the size of epidemic. We propose an alternative approach, which relies on real data about disease coverage in the news, allowing us to model low incidence/high interest diseases, such as SARS, Ebola or H1N1. We introduce a network-based model, in which disease is transmitted through local interactions between individuals and the probability of transmission is affected by media coverage. We assume that media attention increases self-protection (e.g. hand washing and compliance with social distancing), which, in turn, decreases disease model. We apply the model to the case of H1N1 transmission in Mexico City in 2009 and show how media influence-measured by the time series of the weekly count of news articles published on the outbreak-helps to explain the observed transmission dynamics. We show that incorporating the media attention based on the observed media coverage of the outbreak better estimates the disease dynamics from what would be predicted by using media function that approximate the media impact using the number of cases and rate of spread. Finally, we apply the model to a typical influenza season in Washington, DC and estimate how the transmission pattern would have changed given different levels of media coverage. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Identifier An unambiguous reference to the resource within a given context DOI: 10.1371/journal.pone.0197646 Source A related resource from which the described resource is derived PLoS ONE Publisher An entity responsible for making the resource available Public Library of Science (PLoS) Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Science, Medicine Language A language of the resource EN https://www.socictopen.socict.org/files/original/0cd0ecea2c8231c86173adbd913b4516.pdf f256efeaea0a1cdd99b9e124b97c086b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images Creator An entity primarily responsible for making the resource Kan Zeng, Yixiao Wang Description An account of the resource Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Synthetic Aperture Radar (SAR), deep convolutional neural network (DCNN), oil spill detection, oil spills, lookalikes, dark patch Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/rs12061015 Source A related resource from which the described resource is derived Remote Sensing Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Science Language A language of the resource EN https://www.socictopen.socict.org/files/original/8353f4d3c45c8f42374208ad166feb80.pdf 70c8237477b204bc2dd240a2754f79c9 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases Creator An entity primarily responsible for making the resource Sung-Mok Jung, Andrei R. Akhmetzhanov, Katsuma Hayashi, Natalie M. Linton, Yichi Yang, Baoyin Yuan, Tetsuro Kobayashi, Ryo Kinoshita, Hiroshi Nishiura Description An account of the resource The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Mortality, censoring, travel, migration, Importation, emerging infectious diseases Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/jcm9020523 Source A related resource from which the described resource is derived Journal of Clinical Medicine Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine Language A language of the resource EN https://www.socictopen.socict.org/files/original/ff59f2d7c700d5b578d813408c0d0fbb.pdf 80c6fc6ba494f72507f513bbd336c684 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Porcine Epidemic Diarrhea Virus (PEDV) ORF3 Enhances Viral Proliferation by Inhibiting Apoptosis of Infected Cells Creator An entity primarily responsible for making the resource Fusheng Si, Xiaoxia Hu, Chen-Yang Wang, Bingqing Chen, Ruiyang WANG, Shijuan Dong, Ruisong Yu, Zhen Li Description An account of the resource The genomes of coronaviruses carry accessory genes known to be associated with viral virulence. The single accessory gene of porcine epidemic diarrhea virus (PEDV), ORF3, is dispensable for virus replication in vitro, while viral mutants carrying ORF3 truncations exhibit an attenuated phenotype of which the underlying mechanism is unknown. Here, we studied the effect of ORF3 deletion on the proliferation of PEDV in Vero cells. To this end, four recombinant porcine epidemic diarrhea viruses (PEDVs) were rescued using targeted RNA recombination, three carrying the full-length ORF3 gene from different PEDV strains, and one from which the ORF3 gene had been deleted entirely. Our results showed that PEDVs with intact or naturally truncated ORF3 replicated to significantly higher titers than PEDV without an ORF3. Further characterization revealed that the extent of apoptosis induced by PEDV infection was significantly lower with the viruses carrying an intact or C-terminally truncated ORF3 than with the virus lacking ORF3, indicating that the ORF3 protein as well as its truncated form interfered with the apoptosis process. Collectively, we conclude that PEDV ORF3 protein promotes virus proliferation by inhibiting cell apoptosis caused by virus infection. Our findings provide important insight into the role of ORF3 protein in the pathogenicity of PEDV. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource PEDV, the role of orf3, proliferation, apoptosis, Cells Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/v12020214 Source A related resource from which the described resource is derived Viruses Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Microbiology Language A language of the resource EN https://www.socictopen.socict.org/files/original/c5f3fed5e3e4b941840ef2dd0dd66cc0.pdf 8f3e87d4b0057854b0945fe737b3dbf8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Covid-19: Pandemonium in our time Creator An entity primarily responsible for making the resource Robert Bergquist, Laura Rinaldi Description An account of the resource While pandemonium has come to mean wild and noisy disorder, the reference here is to John Milton's epic poem Paradise Lost and the upheaval following Lucifer's banishment from Heaven and his construction of Pandæmonium as his hub. Today's avalanche of conflicting news on how to deal with the coronavirus disease 2019 (Covid-19) brings to mind the Trinity nuclear bomb test with Enrico Fermi estimating its strength by releasing small pieces of paper into the air and measuring their displacement by the shock wave. Fermi's result, in fact not far from the true value, emphasised his ability to make good approximations with few or no actual data. The current wave of Covid-19 presents just this kind of situation as it engulfs the world from ground zero in Wuhan, China. Much information is indeed missing, but datasets that might lead to useful ideas on how to handle this pandemic are steadily accumulating. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Identifier An unambiguous reference to the resource within a given context DOI: 10.4081/gh.2020.880 Source A related resource from which the described resource is derived Geospatial Health Publisher An entity responsible for making the resource available PAGEPress Publications Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Geography (General) Language A language of the resource EN