2001 10 20655 https://www.socictopen.socict.org/files/original/f71ac2419a747e6c71735887c5a3fab5.pdf 7b60c1c6b5a2e1f6377c8525f63db769 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 First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model Creator An entity primarily responsible for making the resource Xinguang Chen, Bin Yu Description An account of the resource Abstract Background Similar to outbreaks of many other infectious diseases, success in controlling the novel 2019 coronavirus infection requires a timely and accurate monitoring of the epidemic, particularly during its early period with rather limited data while the need for information increases explosively. Methods In this study, we used a second derivative model to characterize the coronavirus epidemic in China with cumulatively diagnosed cases during the first 2 months. The analysis was further enhanced by an exponential model with a close-population assumption. This model was built with the data and used to assess the detection rate during the study period, considering the differences between the true infections, detectable and detected cases. Results Results from the second derivative modeling suggest the coronavirus epidemic as nonlinear and chaotic in nature. Although it emerged gradually, the epidemic was highly responsive to massive interventions initiated on January 21, 2020, as indicated by results from both second derivative and exponential modeling analyses. The epidemic started to decelerate immediately after the massive actions. The results derived from our analysis signaled the decline of the epidemic 14 days before it eventually occurred on February 4, 2020. Study findings further signaled an accelerated decline in the epidemic starting in 14 days on February 18, 2020. Conclusions The coronavirus epidemic appeared to be nonlinear and chaotic, and was responsive to effective interventions. The methods used in this study can be applied in surveillance to inform and encourage the general public, public health professionals, clinicians and decision-makers to take coordinative and collaborative efforts to control the epidemic. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource COVID-19, 2019ncov, Outbreak, second derivative, Infectious disease epidemic, Dynamic Modeling Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s41256-020-00137-4 Source A related resource from which the described resource is derived Global Health Research and Policy Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Public aspects of medicine Language A language of the resource EN https://www.socictopen.socict.org/files/original/6e4b0ceaefffed7506e815a8c5fe3fe8.pdf 436c02e757d2440b1f896db027a42ab0 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 Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter logistic growth model Creator An entity primarily responsible for making the resource Xinguang Chen, Ding-Geng Chen, Jenny K. Chen Description An account of the resource Abstract Background Many studies have modeled and predicted the spread of COVID-19 (coronavirus disease 2019) in the U.S. using data that begins with the first reported cases. However, the shortage of testing services to detect infected persons makes this approach subject to error due to its underdetection of early cases in the U.S. Our new approach overcomes this limitation and provides data supporting the public policy decisions intended to combat the spread of COVID-19 epidemic. Methods We used Centers for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S. from January 22 to April 6, 2020, and reconstructed the epidemic using a 5-parameter logistic growth model. We fitted our model to data from a 2-week window (i.e., from March 21 to April 4, approximately one incubation period) during which large-scale testing was being conducted. With parameters obtained from this modeling, we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of underdetection. Results The data fit the model satisfactorily. The estimated daily growth rate was 16.8% overall with 95% CI: [15.95, 17.76%], suggesting a doubling period of 4 days. Based on the modeling result, the tipping point at which new cases will begin to decline will be on April 7th, 2020, with a peak of 32,860 new cases on that day. By the end of the epidemic, at least 792,548 (95% CI: [789,162, 795,934]) will be infected in the U.S. Based on our model, a total of 12,029 cases were not detected between January 22 (when the first case was detected in the U.S.) and April 4. Conclusions Our findings demonstrate the utility of a 5-parameter logistic growth model with reliable data that comes from a specified period during which governmental interventions were appropriately implemented. Beyond informing public health decision-making, our model adds a tool for more faithfully capturing the spread of the COVID-19 epidemic. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Prediction, epidemics, logistic growth model, disease dynamics, COVID-19, population-based model Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s41256-020-00152-5 Source A related resource from which the described resource is derived Global Health Research and Policy Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Public aspects of medicine https://www.socictopen.socict.org/files/original/5b3f59e14e1a6aedff4c4e72d1407926.pdf df428cebb570dcd5719bf948c0a9a1c5 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 Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter logistic growth model Creator An entity primarily responsible for making the resource Xinguang Chen, Ding-Geng Chen, Jenny K. Chen Description An account of the resource Abstract Background Many studies have modeled and predicted the spread of COVID-19 (coronavirus disease 2019) in the U.S. using data that begins with the first reported cases. However, the shortage of testing services to detect infected persons makes this approach subject to error due to its underdetection of early cases in the U.S. Our new approach overcomes this limitation and provides data supporting the public policy decisions intended to combat the spread of COVID-19 epidemic. Methods We used Centers for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S. from January 22 to April 6, 2020, and reconstructed the epidemic using a 5-parameter logistic growth model. We fitted our model to data from a 2-week window (i.e., from March 21 to April 4, approximately one incubation period) during which large-scale testing was being conducted. With parameters obtained from this modeling, we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of underdetection. Results The data fit the model satisfactorily. The estimated daily growth rate was 16.8% overall with 95% CI: [15.95, 17.76%], suggesting a doubling period of 4 days. Based on the modeling result, the tipping point at which new cases will begin to decline will be on April 7th, 2020, with a peak of 32,860 new cases on that day. By the end of the epidemic, at least 792,548 (95% CI: [789,162, 795,934]) will be infected in the U.S. Based on our model, a total of 12,029 cases were not detected between January 22 (when the first case was detected in the U.S.) and April 4. Conclusions Our findings demonstrate the utility of a 5-parameter logistic growth model with reliable data that comes from a specified period during which governmental interventions were appropriately implemented. Beyond informing public health decision-making, our model adds a tool for more faithfully capturing the spread of the COVID-19 epidemic. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource covid-19, epidemics, prediction, disease dynamics, logistic growth model, population-based model Identifier An unambiguous reference to the resource within a given context 10.1186/s41256-020-00152-5 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Public aspects of medicine https://www.socictopen.socict.org/files/original/4ffacdc76557795c4304ed738a78accc.pdf 229297e213feb5fd8f35284c40513039 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 Detection of alpha- and betacoronaviruses in rodents from Yunnan, China Creator An entity primarily responsible for making the resource Xingyi Ge, Weihong Yang, Jihua Zhou, Bei Li, Wei Zhang, Zhengli Shi, Yunzhi Zhang Description An account of the resource Abstract Background Rodents represent the most diverse mammals on the planet and are important reservoirs of human pathogens. Coronaviruses infect various animals, but to date, relatively few coronaviruses have been identified in rodents worldwide. The evolution and ecology of coronaviruses in rodent have not been fully investigated. Results In this study, we collected 177 intestinal samples from thress species of rodents in Jianchuan County, Yunnan Province, China. Alphacoronavirus and betacoronavirus were detected in 23 rodent samples from three species, namely Apodemus chevrieri (21/98), Eothenomys fidelis (1/62), and Apodemus ilex (1/17). We further characterized the full-length genome of an alphacoronavirus from the A. chevrieri rat and named it as AcCoV-JC34. The AcCoV-JC34 genome was 27,649 nucleotides long and showed a structure similar to the HKU2 bat coronavirus. Comparing the normal transcription regulatory sequence (TRS), 3 variant TRS sequences upstream the spike (S), ORF3, and ORF8 genes were found in the genome of AcCoV-JC34. In the conserved replicase domains, AcCoV-JC34 was most closely related to Rattus norvegicus coronavirus LNRV but diverged from other alphacoronaviruses, indicating that AcCoV-JC34 and LNRV may represent a novel alphacoronavirus species. However, the S and nucleocapsid proteins showed low similarity to those of LRNV, with 66.5 and 77.4% identities, respectively. Phylogenetic analysis revealed that the S genes of AcCoV-JC34, LRNV, and HKU2 formed a distinct lineage with all known coronaviruses. Conclusions Both alphacoronaviruses and betacoronaviruses were detected in Apodemus chevrieri in the Yunnan Province of China, indicating that Apodemus chevrieri is an important host for coronavirus. Several new features were identified in the genome of an Apodemus chevrieri coronavirus. The phylogenetic distance to other coronaviruses suggests a variable origin and evolutionary route of the S genes of AcCoV-JC34, LRNV, and HKU2. These results indicate that the diversity of rodent coronaviruses is much higher than previously expected. Further surveillance and functional studies of these coronaviruses will help to better understand the importance of rodent as host for coronaviruses. Date A point or period of time associated with an event in the lifecycle of the resource 2017 Subject The topic of the resource rodent, coronavirus, genetic diversity Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12985-017-0766-9 Source A related resource from which the described resource is derived Virology Journal Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Infectious and parasitic diseases Language A language of the resource EN https://www.socictopen.socict.org/files/original/b1f70db0a702338bfdd38ee5dbfe5ae5.pdf 0fa664ed6210bf376f24faf60e5c2c1d 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 Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study. Creator An entity primarily responsible for making the resource Xinkui Liu, Xinpei Yue, Furong Liu, Le Wei, Yuntian Chu, Honghong Bao, Yichao Dong, Wenjie Cheng, Linpeng Yang Description An account of the resource Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in December 2019. Although previous studies have described the clinical aspects of COVID-19, few studies have focused on the early detection of severe COVID-19. Therefore, this study aimed to identify the predictors of severe COVID-19 and to compare clinical features between patients with severe COVID-19 and those with less severe COVID-19. Patients admitted to designated hospital in the Henan Province of China who were either discharged or died prior to February 15, 2020 were enrolled retrospectively. Additionally, patients who underwent at least one of the following treatments were assigned to the severe group: continuous renal replacement therapy, high-flow oxygen absorption, noninvasive and invasive mechanical ventilation, or extracorporeal membrane oxygenation. The remaining patients were assigned to the non-severe group. Demographic information, initial symptoms, and first visit examination results were collected from the electronic medical records and compared between the groups. Multivariate logistic regression analysis was performed to determine the predictors of severe COVID-19. A receiver operating characteristic curve was used to identify a threshold for each predictor. Altogether,104 patients were enrolled in our study with 30 and 74 patients in the severe and non-severe groups, respectively. Multivariate logistic analysis indicated that patients aged ≥63 years (odds ratio = 41.0; 95% CI: 2.8, 592.4), with an absolute lymphocyte value of ≤1.02×109/L (odds ratio = 6.1; 95% CI = 1.5, 25.2) and a C-reactive protein level of ≥65.08mg/L (odds ratio = 8.9; 95% CI = 1.0, 74.2) were at a higher risk of severe illness. Thus, our results could be helpful in the early detection of patients at risk for severe illness, enabling the implementation of effective interventions and likely lowering the morbidity of COVID-19 patients. 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 10.1371/journal.pone.0235459 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology 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 https://www.socictopen.socict.org/files/original/cf9c6faf6041a5366451ac0717ef914a.pdf aad18a256df7c4b0246c6c695a799db6 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 One-Year Real-Time Measurement of Black Carbon in the Rural Area of Qingdao, Northeastern China: Seasonal Variations, Meteorological Effects, and the COVID-19 Case Analysis Creator An entity primarily responsible for making the resource Xinlei Ge, Shijie Cui, Fuzhen Shen, Jiukun Xian, Lin Zhang, Baoling Deng, Yunjiang Zhang Description An account of the resource In this paper, we report the results obtained from one year of real-time measurement (i.e., from December 2019 to November 2020) of atmospheric black carbon (BC) under a rural environment in Qingdao of Northeastern China. The annual average concentration of BC was 1.92 ± 1.89 μg m−3. The highest average concentration of BC was observed in winter (3.65 ± 2.66 μg m−3), followed by fall (1.73 ± 1.33 μg m−3), spring (1.53 ± 1.33 μg m−3), and summer (0.83 ± 0.56 μg m−3). A clear weekend effect was observed in winter, which was characterized by higher BC concentration (4.60 ± 2.86 μg m−3) during the weekend rather than that (3.22 ± 2.45 μg m−3) during weekdays. The influence of meteorological parameters, including surface horizontal wind speed, boundary layer height (BLH), and precipitation, on BC, was investigated. In particular, such BLH influence presented evidently seasonal dependence, while there was no significant seasonality for horizontal wind speed. These may reflect different roles of atmospheric vertical dilution on affecting BC in different seasons. The △BC/△CO ratio decreased with the increase of precipitation, indicative of the influence of below-cloud wet removal of BC, especially during summertime where rainfall events more frequently occurred than any of other seasons. The bivariate-polar-plot analysis showed that the high BC concentrations were mainly associated with low wind speed in all seasons, highlighting an important BC source originated from local emissions. By using concentration-weighted trajectory analysis, it was found that regional transports, especially from northeastern in winter, could not be negligible for contributing to BC pollution in rural Qingdao. In the coronavirus disease 2019 (COVID−19) case analysis, we observed an obvious increase in the BC/NO2 ratio during the COVID-19 lockdown, supporting the significant non-traffic source sector (such as residential coal combustion) for BC in rural Qingdao. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource covid-19, black carbon, seasonal variations, meteorological effects, Qingdao, rural site Identifier An unambiguous reference to the resource within a given context 10.3390/atmos12030394 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Meteorology. Climatology https://www.socictopen.socict.org/files/original/1873df2719299508916d9a74a9e7602f.pdf 2f879103bd27f64dbd2b853242b8eab8 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 Clinical determinants of the severity of COVID-19: A systematic review and meta-analysis. Creator An entity primarily responsible for making the resource Xinyang Li, Xianrui Zhong, Yongbo Wang, Xiantao Zeng, Ting Luo, Qing Liu Description An account of the resource ObjectiveWe aimed to systematically identify the possible risk factors responsible for severe cases.MethodsWe searched PubMed, Embase, Web of science and Cochrane Library for epidemiological studies of confirmed COVID-19, which include information about clinical characteristics and severity of patients' disease. We analyzed the potential associations between clinical characteristics and severe cases.ResultsWe identified a total of 41 eligible studies including 21060 patients with COVID-19. Severe cases were potentially associated with advanced age (Standard Mean Difference (SMD) = 1.73, 95% CI: 1.34-2.12), male gender (Odds Ratio (OR) = 1.51, 95% CI:1.33-1.71), obesity (OR = 1.89, 95% CI: 1.44-2.46), history of smoking (OR = 1.40, 95% CI:1.06-1.85), hypertension (OR = 2.42, 95% CI: 2.03-2.88), diabetes (OR = 2.40, 95% CI: 1.98-2.91), coronary heart disease (OR: 2.87, 95% CI: 2.22-3.71), chronic kidney disease (CKD) (OR = 2.97, 95% CI: 1.63-5.41), cerebrovascular disease (OR = 2.47, 95% CI: 1.54-3.97), chronic obstructive pulmonary disease (COPD) (OR = 2.88, 95% CI: 1.89-4.38), malignancy (OR = 2.60, 95% CI: 2.00-3.40), and chronic liver disease (OR = 1.51, 95% CI: 1.06-2.17). Acute respiratory distress syndrome (ARDS) (OR = 39.59, 95% CI: 19.99-78.41), shock (OR = 21.50, 95% CI: 10.49-44.06) and acute kidney injury (AKI) (OR = 8.84, 95% CI: 4.34-18.00) were most likely to prevent recovery. In summary, patients with severe conditions had a higher rate of comorbidities and complications than patients with non-severe conditions.ConclusionPatients who were male, with advanced age, obesity, a history of smoking, hypertension, diabetes, malignancy, coronary heart disease, hypertension, chronic liver disease, COPD, or CKD are more likely to develop severe COVID-19 symptoms. ARDS, shock and AKI were thought to be the main hinderances to recovery. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Identifier An unambiguous reference to the resource within a given context 10.1371/journal.pone.0250602 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology 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 https://www.socictopen.socict.org/files/original/b5a38257b6a8774da7f2f77c86ffcc6d.pdf 11d3f67bb061999299fdfbfce74c93fc 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 Co-Circulation of Canine Coronavirus I and IIa/b with High Prevalence and Genetic Diversity in Heilongjiang Province, Northeast China. Creator An entity primarily responsible for making the resource Xinyu Wang, Chun-qiu Li, Donghua Guo, Shan Wei, Yufei Geng, Enyu Wang, Zhihui Wang, Xi Wen Zhao, Ming-Jun Su, Qiujin Liu, Siyao Zhang, Li Feng, Dongbo Sun Description An account of the resource To trace the evolution of canine coronavirus (CCoV), 201 stool samples from diarrheic dogs in northeast China were subjected to reverse transcription-polymerase chain reactions (RT-PCRs) targeting the partial M and S genes of CCoV, followed by an epidemiological analysis. M gene RT-PCRs showed that 28.36% (57/201) of the samples were positive for CCoV; of the 57 positive samples, CCoV-I and CCoV-II accounted for 15.79% (9/57) and 84.21% (48/57), respectively. A sequence comparison of the partial M gene revealed nucleotide homologies of 88.4%-100% among the 57 CCoV strains, and 88.7%-96.2% identity between the 57 CCoV strains and the Chinese reference strain HF3. The CCoV-I and CCoV-II strains exhibited genetic diversity when compared with reference strains from China and other countries. The 57 CCoV strains exhibited high co-infection rates with canine kobuvirus (CaKV) (33.33%) and canine parvovirus-2 (CPV-2) (31.58%). The CCoV prevalence in diarrheic dogs differed significantly with immunization status, regions, seasons, and ages. Moreover, 28 S genes were amplified from the 57 CCoV-positive samples, including 26 CCoV-IIa strains, one CCoV-IIb strain, and one CCoV-I strain. A sequence comparison of the partial S gene revealed 86.3%-100% nucleotide identity among the 26 CCoV-IIa strains, and 89.6%-92.2% identity between the 26 CCoV-IIa strains and the Chinese reference strain V1. The 26 CCoV-IIa strains showed genetic diversity when compared with reference strains from China and other countries. Our data provide evidence that CCoV-I, CCoV-IIa, and CCoV-IIb strains co-circulate in the diarrhoetic dogs in northeast China, high co-infection rates with CaKV and CPV-2 were observed, and the CCoV-II strains exhibited high prevalence and genetic diversity. Date A point or period of time associated with an event in the lifecycle of the resource 2016 Identifier An unambiguous reference to the resource within a given context DOI: 10.1371/journal.pone.0146975 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/867a0143d4dc7d1835cfd0c35f8a984e.pdf e480d6ce942a4bcb518701010783dfb6 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 High-Efficiency Simulation Framework to Analyze the Impact of Exhaust Air from COVID-19 Temporary Hospitals and its Typical Applications Creator An entity primarily responsible for making the resource Xinzheng Lu, Zhe Zheng, Donglian Gu, Pengju Zhao, Linlin Xie, Zhen Xu Description An account of the resource The outbreak of COVID-19 resulted in severe pressure on the existing medical infrastructure in China. Several Chinese cities began to construct temporary hospitals for the centralized treatment of COVID-19 patients. The harmful exhaust air from the outlets of these hospitals may have a significant adverse impact on the fresh-air intakes and surrounding environment. Owing to the need to rapidly construct these hospitals within 6–10 days, just a few hours are allowed for the analysis of the impact of this exhaust air on the environment. To overcome this difficulty, a high-efficiency simulation framework is proposed in this study. Based on the open-source computational fluid dynamics software, FDS, the proposed framework is adaptive and incorporates building information with different levels of detail during various design phases of the hospital, and has been applied in the design of the Wuhan Huoshenshan Hospital, the first typical COVID-19 temporary hospital in China. According to the simulation results, neither the fresh-air intakes nor the surrounding buildings would be polluted by the harmful air discharged from the air outlets of the Huoshenshan hospital. The proposed simulation framework can provide a reference for the design and overall planning of similar hospitals in China and other affected countries. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource covid-19, SARS-CoV-2, FDS, Pollutant dispersion, LOD Identifier An unambiguous reference to the resource within a given context 10.3390/app10113949 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology 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), Chemistry, Engineering (General). Civil engineering (General), Technology, Physics https://www.socictopen.socict.org/files/original/167d938638068e3e71acb95ac5a2a193.pdf 03b65e099b7e4a1e11e70c296a11cae2 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 Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible 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 Entrevista a Tatiana Nuño Martínez Creator An entity primarily responsible for making the resource Xira Ruiz Description An account of the resource Tatiana Nuño Martínez estudió Geológicas en la Universidad Complutense de Madrid y Ciencias del Mar en la Universidad de Cádiz. Tiene un máster en Gestión Integrada de Áreas Litorales por la Universidad de Cádiz y dos años de investigación de Doctorado en Oceanografía. En dos ocasiones ha navegado por los mares de Svalbard, en el Ártico noruego, donde ha podido ver en primera persona los efectos del deshielo y de la sobrepesca en una de las regiones más vulnerables de nuestro planeta. Desde hace años trabaja en Greenpeace España como responsable de cambio climático. Date A point or period of time associated with an event in the lifecycle of the resource 2016 Source A related resource from which the described resource is derived Relaciones Internacionales Publisher An entity responsible for making the resource available Universidad Autónoma de Madrid. Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Political science, International relations Relation A related resource <a href="https://revistas.uam.es/relacionesinternacionales/article/view/6732" target="_blank" rel="noreferrer noopener">https://revistas.uam.es/relacionesinternacionales/article/view/6732</a>