2053 10 20655 https://www.socictopen.socict.org/files/original/052fbdc0919bac35f5a1b259999cb19a.pdf 089ae48124acc1428401a60e24d03790 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 Viral genome sequencing by random priming methods Creator An entity primarily responsible for making the resource Zhang Xinsheng, Afonso Claudio, Feldblyum Jeremy, Sengamalay Naomi, DePasse Jay, Kuzmickas Ryan, Halpin Rebecca, Djikeng Appolinaire, Anderson Norman G, Ghedin Elodie, Spiro David J Description An account of the resource Abstract Background Most emerging health threats are of zoonotic origin. For the overwhelming majority, their causative agents are RNA viruses which include but are not limited to HIV, Influenza, SARS, Ebola, Dengue, and Hantavirus. Of increasing importance therefore is a better understanding of global viral diversity to enable better surveillance and prediction of pandemic threats; this will require rapid and flexible methods for complete viral genome sequencing. Results We have adapted the SISPA methodology 123 to genome sequencing of RNA and DNA viruses. We have demonstrated the utility of the method on various types and sources of viruses, obtaining near complete genome sequence of viruses ranging in size from 3,000–15,000 kb with a median depth of coverage of 14.33. We used this technique to generate full viral genome sequence in the presence of host contaminants, using viral preparations from cell culture supernatant, allantoic fluid and fecal matter. Conclusion The method described is of great utility in generating whole genome assemblies for viruses with little or no available sequence information, viruses from greatly divergent families, previously uncharacterized viruses, or to more fully describe mixed viral infections. Date A point or period of time associated with an event in the lifecycle of the resource 2008 Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/1471-2164-9-5 Source A related resource from which the described resource is derived BMC Genomics 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 Genetics, Biotechnology Language A language of the resource EN https://www.socictopen.socict.org/files/original/f78d917eb4cb671da430f84530730295.pdf dafd1f8e5b8a71fa049a41790d52d81e 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 Identifications of conserved 7-mers in 3'-UTRs and microRNAs in <it>Drosophila</it> Creator An entity primarily responsible for making the resource Zhang Xuegong, Fu Hu, Gu Jin, Li Yanda Description An account of the resource Abstract Background MicroRNAs (miRNAs) are a class of endogenous regulatory small RNAs which play an important role in posttranscriptional regulations by targeting mRNAs for cleavage or translational repression. The base-pairing between the 5'-end of miRNA and the target mRNA 3'-UTRs is essential for the miRNA:mRNA recognition. Recent studies show that many seed matches in 3'-UTRs, which are fully complementary to miRNA 5'-ends, are highly conserved. Based on these features, a two-stage strategy can be implemented to achieve the de novo identification of miRNAs by requiring the complete base-pairing between the 5'-end of miRNA candidates and the potential seed matches in 3'-UTRs. Results We presented a new method, which combined multiple pairwise conservation information, to identify the frequently-occurred and conserved 7-mers in 3'-UTRs. A pairwise conservation score (PCS) was introduced to describe the conservation of all 7-mers in 3'-UTRs between any two Drosophila species. Using PCSs computed from 6 pairs of flies, we developed a support vector machine (SVM) classifier ensemble, named Cons-SVM and identified 689 conserved 7-mers including 63 seed matches covering 32 out of 38 known miRNA families in the reference dataset. In the second stage, we searched for 90 nt conserved stem-loop regions containing the complementary sequences to the identified 7-mers and used the previously published miRNA prediction software to analyze these stem-loops. We predicted 47 miRNA candidates in the genome-wide screen. Conclusion Cons-SVM takes advantage of the independent evolutionary information from the 6 pairs of flies and shows high sensitivity in identifying seed matches in 3'-UTRs. Combining the multiple pairwise conservation information by the machine learning approach, we finally identified 47 miRNA candidates in D. melanogaster. Date A point or period of time associated with an event in the lifecycle of the resource 2007 Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/1471-2105-8-432 Source A related resource from which the described resource is derived BMC Bioinformatics 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 Biology (General), Computer applications to medicine. Medical informatics Language A language of the resource EN https://www.socictopen.socict.org/files/original/f314d1159e18656d48dc9584d0d50112.pdf 28ebdb01754e4288c15d1896cb1f0cf0 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 Nomogram-Based Prediction for Severe Pneumonia in Patients with Coronavirus Disease 2019 (COVID-19) Creator An entity primarily responsible for making the resource Zhang Y, Wu L, Yang J, Zhou C, Liu Y Description An account of the resource Yue Zhang,1 Lin Wu,2 Jibin Yang,3 Congyang Zhou,3 Ying Liu3 1Health Management Center, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, Peoples&rsquo; Republic of China; 2Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, Peoples&rsquo; Republic of China; 3Department of Emergency Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, Peoples&rsquo; Republic of ChinaCorrespondence: Ying LiuDepartment of Emergency Medicine, The First Affiliated Hospital of Nanchang University, Yongwaizhen Street 17, Nanchang, Jiangxi 330006, Peoples&rsquo; Republic of ChinaEmail yingliu_ncu@163.comBackground: The outbreak of a novel coronavirus disease 2019 (COVID-19) is currently ongoing worldwide. A proportion of COVID-19 patients progress rapidly to acute respiratory failure.Objective: We aimed to build a model to predict the risk of developing severe pneumonia in patients with COVID-19 in the early stage.Methods: Data from patients who were confirmed to have COVID-19 and were admitted within 7 days from the onset of respiratory symptoms were retrospectively collected. The patients were classified into severe and non-severe groups according to the presence or absence of severe pneumonia during 1&ndash; 2 weeks of follow-up. The clinical characteristics and laboratory indicators were screened by cross-validation based on LASSO regression to build a prediction model presented by a nomogram. The discrimination and stability, as well as the prediction performance of the model, were analysed.Results: The neutrophil&ndash;lymphocyte ratio, monocyte counts, eosinophil percentage, serum lactate dehydrogenase level and history of diabetes mellitus were collected for the model. Bootstrap resampling showed the apparent C-statistics, and the brier scores were 0.929 and 0.098. The optimism of the C-statistics and brier score was 0.0172 and &minus; 0.019, respectively. The adjusted C-statistics and brier score were 0.9108 and 0.1169, respectively. The optimal cut-off value of the total nomogram score was determined to be 119 according to the maximal Youden index. The sensitivity, specificity, positive predictive value, and negative predictive value for differentiating the presence and absence of severe pneumonia were 83%, 89%, 74%, and 94%, respectively.Conclusion: In our study, the neutrophil&ndash;lymphocyte ratio, monocyte counts, eosinophil percentage, serum lactate dehydrogenase level and history of diabetes mellitus showed great discrimination and stability for the prediction of the presence of severe pneumonia and were selected for the model.Keywords: COVID-19, prediction, nomogram 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, prediction, nomogram Source A related resource from which the described resource is derived Biotemas Publisher An entity responsible for making the resource available Universidade Federal de Santa Catarina 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 https://www.socictopen.socict.org/files/original/e0104f3ad4c31754c262b66c2e4b04f2.pdf fa2c2590c7dfddcc6aaa4744ef2d43cd 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 Identification of a conserved linear B-cell epitope in the M protein of porcine epidemic diarrhea virus Creator An entity primarily responsible for making the resource Zhang Zhibang, Chen Jianfei, Shi-hong Yan, Chen Xiaojin, Shi Da, Feng Li, Yang Bin Description An account of the resource Abstract Background The major structural protein of coronaviruses, the membrane (M) protein, can elicit the formation of protective antibodies, but little information is available about the M protein of porcine epidemic diarrhea virus (PEDV). Identification of epitopes on the PEDV M protein will be helpful in the elucidation of the antigenic properties of this protein. Results One hybridoma cell line secreting anti-M protein monoclonal antibody (McAb) was generated and designated 4D4. To map the epitopes on the PEDV M protein, a total of 17 partially overlapping fragments covering the C-terminus of M protein were expressed as fusion proteins with a 6×His tag or a GST tag. A linear motif, 193TGWAFYVR200, was identified by enzyme-linked immunosorbent assay (ELISA) and western blot (WB) analysis using McAb 4D4. The motif 195WAFYVR200 was the minimal requirement for reactivity, as demonstrated by removing amino acids individually from both ends of the motif 193TGWAFYVR200. The result of WB analysis showed that the 4D4-defined epitope could be recognized by PEDV-positive serum, but not transmissible gastroenteritis virus (TGEV)-positive serum. Furthermore, this epitope was highly conserved among different PEDV strains, as shown by alignment and comparison of sequences. Conclusion A McAb, 4D4, directed against the M protein of PEDV, was obtained, and the 4D4-defined minimal epitope sequence was 195WAFYVR200. The McAb could serve as a candidate for development of a McAb-based antigen capture ELISA for detection of PEDV. The epitope identified provides a basis for the development of epitope-based differential diagnostic techniques and may be useful in the design of epitope-based vaccines. Date A point or period of time associated with an event in the lifecycle of the resource 2012 Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/1743-422X-9-225 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/c0be34798f1f4252905c96f8bef3d8ad.pdf b5ca1ace03436d2fdb388b9723644884 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 Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study Creator An entity primarily responsible for making the resource Zhang, Chunyan, Xu, Songhua, Li, Zongfang, Hu, Shunxu Description An account of the resource BackgroundSince the beginning of the COVID-19 pandemic in late 2019, its far-reaching impacts have been witnessed globally across all aspects of human life, such as health, economy, politics, and education. Such widely penetrating impacts cast significant and profound burdens on all population groups, incurring varied concerns and sentiments among them. ObjectiveThis study aims to identify the concerns, sentiments, and disparities of various population groups during the COVID-19 pandemic through a cross-sectional study conducted via large-scale Twitter data mining infoveillance. MethodsThis study consisted of three steps: first, tweets posted during the pandemic were collected and preprocessed on a large scale; second, the key population attributes, concerns, sentiments, and emotions were extracted via a collection of natural language processing procedures; third, multiple analyses were conducted to reveal concerns, sentiments, and disparities among population groups during the pandemic. Overall, this study implemented a quick, effective, and economical approach for analyzing population-level disparities during a public health event. The source code developed in this study was released for free public use at GitHub. ResultsA total of 1,015,655 original English tweets posted from August 7 to 12, 2020, were acquired and analyzed to obtain the following results. Organizations were significantly more concerned about COVID-19 (odds ratio [OR] 3.48, 95% CI 3.39-3.58) and expressed more fear and depression emotions than individuals. Females were less concerned about COVID-19 (OR 0.73, 95% CI 0.71-0.75) and expressed less fear and depression emotions than males. Among all age groups (ie, ≤18, 19-29, 30-39, and ≥40 years of age), the attention ORs of COVID-19 fear and depression increased significantly with age. It is worth noting that not all females paid less attention to COVID-19 than males. In the age group of 40 years or older, females were more concerned than males, especially regarding the economic and education topics. In addition, males 40 years or older and 18 years or younger were the least positive. Lastly, in all sentiment analyses, the sentiment polarities regarding political topics were always the lowest among the five topics of concern across all population groups. ConclusionsThrough large-scale Twitter data mining, this study revealed that meaningful differences regarding concerns and sentiments about COVID-19-related topics existed among population groups during the study period. Therefore, specialized and varied attention and support are needed for different population groups. In addition, the efficient analysis method implemented by our publicly released code can be utilized to dynamically track the evolution of each population group during the pandemic or any other major event for better informed public health research and interventions. 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 10.2196/26482 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, Computer applications to medicine. Medical informatics https://www.socictopen.socict.org/files/original/9ff63e732f9eaa621d5f4a7cae4c0d4f.pdf 2de0afd8102dcd7abd6e8411973a50f1 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 Model for Optimizing the Structure of Teaching Techniques for Distance Learning in the Russian Higher Education System Creator An entity primarily responsible for making the resource Zhanna Gardanova, Vadim Ponkratov, Nikolay Kuznetsov, Natalya Nikitina, Olesya Dudnik, Endzhe Latypova, Sergey Shcherbatykh Description An account of the resource This study aims to develop a methodical approach to optimize the structure of teaching techniques and approaches to distance education, as exemplified by Russian universities, to enhance students’ professional competencies. A pedagogical experiment was conducted with the participation of 746 second to fourth year students majoring in engineering at the Ural Federal University in Russia. The coefficients for the relative importance of professional competencies were obtained through expert assessment. The number of experts was 40 faculty members with teaching experience of more than five years. The pedagogical experiment revealed the regularities of the influence of the forms of distance learning on the formation of students’ professional competency levels. The values of students’ developmental competency levels have been determined. Non-linear regression models have been developed based on the values of the integral level of professional competencies and the percentage of distance-learning forms in the number of instructional hours. The proposed methodological approach will be useful for university professors and teachers in identifying the most effective forms of distance learning and in differentiating teaching methods not only in the context of the general pandemic but also in the natural conditions of the educational process. 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, Pandemic, higher education, students, distance learning, professional competencies Identifier An unambiguous reference to the resource within a given context 10.3390/joitmc6040147 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 Business, Management. Industrial management https://www.socictopen.socict.org/files/original/147bdcc35b63d3dd0b83870380688a9e.pdf c536724e8abdf518d6bbe6b9884af8c7 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 Effects of Proactive Social Distancing on COVID-19 Outbreaks in 58 Cities, China Creator An entity primarily responsible for making the resource Zhanwei Du, Xiaoke Xu, Lin Wang, Spencer J. Fox, Benjamin J. Cowling, Alison P. Galvani, Lauren Ancel Meyers Description An account of the resource Cities across China implemented stringent social distancing measures in early 2020 to curb coronavirus disease outbreaks. We estimated the speed with which these measures contained transmission in cities. A 1-day delay in implementing social distancing resulted in a containment delay of 2.41 (95% CI 0.97–3.86) days. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Viruses, Respiratory Infections, zoonoses, covid-19, SARS-CoV-2, severe acute respiratory syndrome coronavirus 2 Identifier An unambiguous reference to the resource within a given context 10.3201/eid2609.201932 Source A related resource from which the described resource is derived Emerging Infectious Diseases Publisher An entity responsible for making the resource available Centers for Disease Control and Prevention 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, Infectious and parasitic diseases https://www.socictopen.socict.org/files/original/313030fb4aa7fdeeb8b5077a597ce9a4.pdf 8c6eded3f9c22893e7f9dea8f964e5b6 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 Ground Moving Target Imaging and Analysis for Near-Space Hypersonic Vehicle-Borne Synthetic Aperture Radar System with Squint Angle Creator An entity primarily responsible for making the resource Zhanye Chen, Yu Zhou, Linrang Zhang, Chun-Hui Lin, Yan Huang, Shiyang Tang Description An account of the resource Near space is the key to integrating &#8220;sky&#8222; and &#8220;space&#8222; into the future. A synthetic aperture radar (SAR) that works in this area would initiate a technological revolution for remote sensing applications. This study mainly focused on ground moving target imaging (GMTIm) for a near-space hypersonic vehicle-borne SAR (NS-HSV-SAR) with squint angle. The range history, parameter coupling, and Doppler ambiguity of the squint-looking NS-HSV-SAR are more complicated than traditional side-looking airborne or space-borne SARs. Thus, a precise range model is presented on the basis of phase error analyses. Then, all potential distributions of echo&#8217;s azimuth spectrum are derived, and a GMTIm method is proposed on the basis of a detailed analysis of the echo characteristics. The proposed method consists of three steps. Firstly, a prior information-based pre-processing function was created to decrease the Doppler ambiguity and range migration effects. Secondly, a blur matched keystone transform was developed to correct the residual range walk migration. Thirdly, a time-saving chirp Fourier transform was investigated for azimuth focusing. Implementation considerations, including the curvilinear trajectory of the NS-HSV-SAR, multiple moving target imaging, and applicability and limitation of the method, are discussed. Finally, simulation results are presented to validate the effectiveness of the proposed method. Date A point or period of time associated with an event in the lifecycle of the resource 2018 Subject The topic of the resource near-space hypersonic vehicle, synthetic aperture radar, ground moving target imaging, squint angle, Doppler centre blur, Doppler spectrum ambiguity, curvilinear trajectory Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/rs10121966 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/8c03db55016b8fc55847544caa314f94.pdf 09a06eca25132b7428f7ffca8db6c8d5 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 Evidence-Based Framework and Implementation of China&rsquo;s Strategy in Combating COVID-19 Creator An entity primarily responsible for making the resource Zhao D, Lin H, Zhang Z Description An account of the resource Dahai Zhao,1,2,* Haijiang Lin,3,* Zhiruo Zhang4 1School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, People&rsquo;s Republic of China; 2Shanghai Jiao Tong University-Yale University Joint Center for Health Policy, Shanghai, People&rsquo;s Republic of China; 3Taizhou City Center for Disease Control and Prevention, Taizhou, Zhejiang Province, People&rsquo;s Republic of China; 4School of Public Health, Shanghai Jiao Tong University, Shanghai, People&rsquo;s Republic of China*These authors contributed equally to this workCorrespondence: Dahai ZhaoSchool of International and Public Affairs, Shanghai Jiao Tong University, Xinjian Building, No. 1954 Huashan Road, Shanghai 200030, People&rsquo;s Republic of ChinaTel +86-139-1896-8766Email dahaizhao@sjtu.edu.cnIntroduction: In less than two months, the COVID-19 outbreak in China was controlled through the stringent strategies of screening and isolation. This article aims to use empirical data from all cases from a prefecture-level city of China to introduce and examine the feasibility and efficiency of the screening and isolation strategies and how these were essential in combatting the COVID-19 outbreak.Methods: For this retrospective study, all confirmed COVID-19 patients were recruited from the Taizhou prefecture-level city of Zhejiang province, China.Results: Of the city&rsquo;s total population, 24% were screened for COVID-19 and isolated at home or designated locations for two weeks. From these, a total of 146 confirmed cases of COVID-19 were analysed. Of all cases, 51% were traced from Wuhan, and 21% of patients were in close contact with confirmed cases from outside of the city. Initially, 13% of all patients reported having no clear symptoms, while 42% of patients presented with fever and/or other symptoms. Compared with local patients, new arrivals to the city had fewer days between their exposure and the development of symptoms of COVID-19 (P&lt; 0.001), and fewer days from the time they developed symptoms to the confirmation of COVID-19 (P&lt; 0.001), respectively.Conclusion: This study has fully confirmed that controlling the COVID-19 outbreak through screening and isolation is effective, efficient, and essential. The evidence-based framework and implementation of China&rsquo;s strategy to combat COVID-19 can explain how China contained the COVID-19 outbreak in a short time period. This study offers important references and implications for containing the COVID-19 pandemic in the global community.Keywords: COVID-19, pandemic control, screening, isolation, China Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource China, isolation, covid-19, screening, pandemic control Source A related resource from which the described resource is derived Biotemas Publisher An entity responsible for making the resource available Universidade Federal de Santa Catarina 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/f5d16f79f5e990fcdafd7b4cc776df5d.pdf e542546abb956dfc5822831ce91e8262 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 in Shanghai: IPC Policy Exploration in Support of Work Resumption Through System Dynamics Modeling Creator An entity primarily responsible for making the resource Zhao J, Jia J, Qian Y, Zhong L, Wang J, Cai Y Description An account of the resource Jidi Zhao,1 Jianguo Jia,2 Ying Qian,3 Lumin Zhong,4 Jiancong Wang,5 Yuyang Cai4,6 1Faculty of Economics and Management, East China Normal University, Shanghai, People&rsquo;s Republic of China; 2System Dynamics Chapter, Systems Engineering Society of China, Beijing, People&rsquo;s Republic of China; 3School of Management, Shanghai University, Shanghai, People&rsquo;s Republic of China; 4School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People&rsquo;s Republic of China; 5Institute of Global Health, University of Geneva, Geneva, Switzerland; 6China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, People&rsquo;s Republic of ChinaCorrespondence: Yuyang Cai; Ying Qian Email caiyuyang@sjtu.edu.cn; qian.ying@t.shu.edu.cnPurpose: It is unclear how and to what extent various infection prevention and control (IPC) policies affect the spread of an epidemic during work resumption. In order to assess the impact of IPC policies, this research addresses the results of a policy simulation in Shanghai, China, which estimates the transmission dynamics of COVID-19 under various IPC policies and offers evidence-based outcomes of work resumption policies for the world.Materials and Methods: This simulation research is based on a system dynamics (SD) model that integrates IPC work resumption policies implemented in Shanghai into the classical susceptible-exposed-infected-removed (SEIR) epidemiological model. Input data were obtained from official websites, the Baidu migration index and published literature. The SD model was validated by comparing results with real-world data.Results: The simulations show that a non-quarantined and non-staged approach to work resumption (Policy 1) would bring a small secondary outbreak of COVID-19. The quarantined but non-staged approach (Policy 2) and the non-quarantined but staged approach (Policy 3) would not bring a secondary outbreak of COVID-19. However, they both would generate more newly confirmed cases than the staged and quarantined approach (Policy 4). Moreover, the 14-day quarantine policy alone appears to be more effective in reducing transmission risk than the staged work resumption policy alone. The combined staged and quarantined IPC policy led to the fewest confirmed cases caused by work resumption in Shanghai, and the spread of COVID-19 stopped (ie, the number of newly confirmed cases reduced to zero) at the earliest date.Conclusion: Conservative IPC policies can prevent a second outbreak of COVID-19 during work resumption. The dynamic systems model designed in this study can serve as a tool to test various IPC work resumption policies, facilitating decision-making in responses to combating the COVID-19 pandemic.Keywords: IPC policies, work resumption, COVID-19, system dynamics, SEIR 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, SEIR, System Dynamics, work resumption, ipc policies Source A related resource from which the described resource is derived Biotemas Publisher An entity responsible for making the resource available Universidade Federal de Santa Catarina 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