690 10 20655 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/a9bb0fd7f56e73d15e8e7e67ea657cff.pdf 1a219d3279de7ec5f22afcd55c70fb48 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 New machine learning method for image-based diagnosis of COVID-19. Creator An entity primarily responsible for making the resource Mohamed Abd Elaziz, Khalid M Hosny, Ahmad Salah, Mohamed M Darwish, Songfeng Lu, Ahmed T Sahlol Description An account of the resource COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively. 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.0235187 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/044608a37981ad3b23a17d0a870d3bb7.pdf 9d48775766dc9973a8d47e7381036153 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—Importance for Patients on the Waiting List and after Kidney Transplantation—A Single Center Evaluation in 2020–2021 Creator An entity primarily responsible for making the resource Simone C. Boedecker, Pascal Klimpke, Daniel Kraus, Stefan Runkel, Peter R. Galle, Martina Koch, Julia Weinmann-Menke Description An account of the resource (1) Background: Dialysis patients and recipients of a kidney allograft are at high risk for infection with SARS-CoV-2. It has been shown that the development of potent neutralizing humoral immunity against SARS CoV-2 leads to an increased probability of survival. However, the question of whether immunocompromised patients develop antibodies has not yet been sufficiently investigated; (2) Methods: SARS-CoV-2 antibodies were examined in hemodialysis patients on the waiting list for kidney transplantation as well as patients after kidney transplantation. Patients were interviewed about symptoms and comorbidities, BMI, and smoking history; (3) Results: SARS-CoV-2 antibodies were found in 16 out of 259 patients (6%). The trend of infections here reflects the general course of infection in Germany with a peak in November/December of 2020. Remarkably, patients on the waiting list experienced only mild disease. In contrast, transplanted patients had to be hospitalized but recovered rapidly from COVID-19. Most interesting is that all immunosuppressed patients developed antibodies against SARS-CoV-2 after infection; (4) Conclusions: Even with extensive hygiene concepts, an above-average number of patients were infected with SARS-CoV-2 during the second wave of infections in Germany. Because SARS-CoV-2 infection triggered the formation of antibodies even in these immunocompromised patients, we expect vaccination to be effective in this group of patients. Thus, dialysis patients and patients after kidney transplantation should be given high priority in vaccination programs. 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, Hemodialysis, kidney transplantation, waiting list Identifier An unambiguous reference to the resource within a given context 10.3390/pathogens10040429 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 Medicine https://www.socictopen.socict.org/files/original/c97649c3c13cea9b4ff2f4137000559c.pdf fa99a1f39785cfc13a06f9ced38b7519 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 Outbreak analysis with a logistic growth model shows COVID-19 suppression dynamics in China. Creator An entity primarily responsible for making the resource Yi Zou, Stephen Pan, Peng Zhao, Lei Han, Xiaoxiang Wang, Lia Hemerik, Johannes Knops, Wopke van der Werf Description An account of the resource China reported a major outbreak of a novel coronavirus, SARS-CoV2, from mid-January till mid-March 2020. We review the epidemic virus growth and decline curves in China using a phenomenological logistic growth model to summarize the outbreak dynamics using three parameters that characterize the epidemic's timing, rate and peak. During the initial phase, the number of virus cases doubled every 2.7 days (range 2.2-4.4 across provinces). The rate of increase in the number of reported cases peaked approximately 10 days after suppression measures were started on 23-25 January 2020. The peak in the number of reported sick cases occurred on average 18 days after the start of suppression measures. From the time of starting measures till the peak, the number of cases increased by a factor 39 in the province Hubei, and by a factor 9.5 for all of China (range: 6.2-20.4 in the other provinces). Complete suppression took up to 2 months (range: 23-57d.), during which period severe restrictions, social distancing measures, testing and isolation of cases were in place. The suppression of the disease in China has been successful, demonstrating that suppression is a viable strategy to contain SARS-CoV2. 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.0235247 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/8cd0471d12c8cdcfe8a74f87b8195584.pdf 03db02157469cd5ea22fad845e538f92 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 Lesson Learned from the Power of Open Data: Resolving the Mask Shortage Problem Caused by COVID-19 in South Korea Creator An entity primarily responsible for making the resource Haklae Kim Description An account of the resource COVID-19 is spreading globally and causing severe damage across different sectors including social culture, politics, and economy. Governments worldwide are responding in various ways to prevent the spread of COVID-19 and to protect the health and safety of citizens. South Korea is escaping the crisis through rapid and early inspection with ICT technology, isolation for confirmed cases, and transparency and communication through public–private partnerships. This paper presents how face mask shortages can be managed through the disclosure of government data and with effective online services. This study shows how South Korea enhances government transparency and that combining open data with ICT technologies can effectively solve social problems during the COVID-19 pandemic. 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, open data, mask shortage, government data Identifier An unambiguous reference to the resource within a given context 10.3390/su13010278 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 Environmental effects of industries and plants, Renewable energy sources, Environmental sciences https://www.socictopen.socict.org/files/original/4671d7f945796c97f9614f2df40027fa.pdf b0ea136376793d333c1377271ed938b8 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 Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect Creator An entity primarily responsible for making the resource Karim Barigou, Stéphane Loisel, Yahia Salhi Description An account of the resource Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Standard single population models typically suffer from two major drawbacks: on the one hand, they use a large number of parameters compared to the sample size and, on the other hand, model choice is still often based on in-sample criterion, such as the Bayes information criterion (BIC), and therefore not on the ability to predict. In this paper, we develop a model based on a decomposition of the mortality surface into a polynomial basis. Then, we show how regularization techniques and cross-validation can be used to obtain a parsimonious and coherent predictive model for mortality forecasting. We analyze how COVID-19-type effects can affect predictions in our approach and in the classical one. In particular, death rates forecasts tend to be more robust compared to models with a cohort effect, and the regularized model outperforms the so-called P-spline model in terms of prediction and stability. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource mortality, Forecasting, regularization, smoothing, elastic net, Poisson generalized linear model Identifier An unambiguous reference to the resource within a given context 10.3390/risks9010005 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 Insurance https://www.socictopen.socict.org/files/original/e6ad06e19d119124c35c0f831244333e.pdf 66d9859b5104afa7f0493f2dc348b027 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 Systemic Inflammation and COVID-19 Mortality in Patients with Major Noncommunicable Diseases: Chronic Coronary Syndromes, Diabetes and Obesity Creator An entity primarily responsible for making the resource Andreea-Luciana Buicu, Simona Cernea, Imre Benedek, Corneliu-Florin Buicu, Theodora Benedek Description An account of the resource COVID-19 is currently considered an inflammatory disease affecting the entire organism. In severe forms, an augmented inflammatory response leads to the fulminant “cytokine storm”, which may result in severe multisystemic end-organ damage. Apart from the acute inflammatory response, it seems that chronic inflammation also plays a major role in the clinical evolution of COVID-19 patients. Pre-existing inflammatory conditions, such as those associated with chronic coronary diseases, type 2 diabetes mellitus or obesity, may be associated with worse clinical outcomes in the context of COVID-19 disease. These comorbidities are reported as powerful predictors of poor outcomes and death following COVID-19 disease. Moreover, in the context of chronic coronary syndrome, the cytokine storm triggered by SARS-CoV-2 infection may favor vulnerabilization and rupture of a silent atheromatous plaque, with consequent acute coronary syndrome, leading to a sudden deterioration of the clinical condition of the patient. This review aims to present the current status of knowledge regarding the link between COVID-19 mortality, systemic inflammation and several major diseases associated with poor outcomes, such as cardiovascular diseases, diabetes and obesity. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource inflammation, cytokine storm, obesity, type 2 diabetes, chronic coronary syndrome Identifier An unambiguous reference to the resource within a given context 10.3390/jcm10081545 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 Medicine https://www.socictopen.socict.org/files/original/9b0279bff8e2d1ae7b951af4b75d19ee.pdf 6dae966090ec28c76940a174b173bd93 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 Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables Creator An entity primarily responsible for making the resource Alex Minetto, Andrea Nardin, Fabio Dovis Description An account of the resource In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium range distance estimation to provide behavioral models in the emergency. By exploiting the availability of smart devices, the synergy between mobile network connectivity and Global Navigation Satellite Systems (GNSS), cooperative ranging approaches allow computing inter-personal distance measurements in outdoor environments through the exchange of light-weight navigation data among interconnected users. In this paper, a model for Inter-Agent Ranging (IAR) is provided and experimentally assessed to offer a naive collaborative distancing technique that leverages these features. Although the technique provides distance information, it does not imply the disclosure of the user’s locations being intrinsically prone to protect sensitive user data. A statistical error model is presented and validated through synthetic simulations and real, on-field experiments to support implementation in GNSS-equipped mobile devices. Accuracy and precision of IAR measurements are compared to other consolidated GNSS-based techniques showing comparable performance at lower complexity and computational effort. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource social distancing, distance estimation, mobile devices, Global Navigation Satellite System, collaborative positioning, collaborative measurements Identifier An unambiguous reference to the resource within a given context 10.3390/s21082588 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 Chemical technology https://www.socictopen.socict.org/files/original/6cec45f917dbdb15299153e9f93eccb5.pdf 083df739efcf89f228ec54b6e5f78c7f 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 Time and Covid-19 stress in the lockdown situation: Time free, «Dying» of boredom and sadness. Creator An entity primarily responsible for making the resource Sylvie Droit-Volet, Sandrine Gil, Natalia Martinelli, Nicolas Andant, Maélys Clinchamps, Lénise Parreira, Karine Rouffiac, Michael Dambrun, Pascal Huguet, Benoît Dubuis, Bruno Pereira, COVISTRESS network, Jean-Baptiste Bouillon, Frédéric Dutheil Description An account of the resource A lockdown of people has been used as an efficient public health measure to fight against the exponential spread of the coronavirus disease (Covid-19) and allows the health system to manage the number of patients. The aim of this study (clinicaltrials.gov NCT00430818) was to evaluate the impact of both perceived stress aroused by Covid-19 and of emotions triggered by the lockdown situation on the individual experience of time. A large sample of the French population responded to a survey on their experience of the passage of time during the lockdown compared to before the lockdown. The perceived stress resulting from Covid-19 and stress at work and home were also assessed, as were the emotions felt. The results showed that people have experienced a slowing down of time during the lockdown. This time experience was not explained by the levels of perceived stress or anxiety, although these were considerable, but rather by the increase in boredom and sadness felt in the lockdown situation. The increased anger and fear of death only explained a small part of variance in the time judgment. The conscious experience of time therefore reflected the psychological difficulties experienced during lockdown and was not related to their perceived level of stress or anxiety. 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.0236465 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/28340fdee4dc1901c3fb061f89d4c640.pdf b6fcc9426518a9544defc3c2ce3139d2 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 Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: A systematic review and meta-analysis. Creator An entity primarily responsible for making the resource Paddy Ssentongo, Anna E Ssentongo, Emily S Heilbrunn, Djibril M Ba, Vernon M Chinchilli Description An account of the resource BackgroundEstimating the risk of pre-existing comorbidities on coronavirus disease 2019 (COVID-19) mortality may promote the importance of targeting populations at risk to improve survival. This systematic review and meta-analysis aimed to estimate the association of pre-existing comorbidities with COVID-19 mortality.MethodsWe searched MEDLINE, SCOPUS, OVID, and Cochrane Library databases, and medrxiv.org from December 1st, 2019, to July 9th, 2020. The outcome of interest was the risk of COVID-19 mortality in patients with and without pre-existing comorbidities. We analyzed 11 comorbidities: cardiovascular diseases, hypertension, diabetes, congestive heart failure, cerebrovascular disease, chronic kidney disease, chronic liver disease, cancer, chronic obstructive pulmonary disease, asthma, and HIV/AIDS. Two reviewers independently extracted data and assessed the risk of bias. All analyses were performed using random-effects models and heterogeneity was quantified.ResultsEleven pre-existing comorbidities from 25 studies were included in the meta-analysis (n = 65, 484 patients with COVID-19; mean age; 61 years; 57% male). Overall, the between-study heterogeneity was medium, and studies had low publication bias and high quality. Cardiovascular disease (risk ratio (RR) 2.25, 95% CI = 1.60-3.17, number of studies (n) = 14), hypertension (1.82 [1.43 to 2.32], n = 13), diabetes (1.48 [1.02 to 2.15], n = 16), congestive heart failure (2.03 [1.28 to 3.21], n = 3), chronic kidney disease (3.25 [1.13 to 9.28)], n = 9) and cancer (1.47 [1.01 to 2.14), n = 10) were associated with a significantly greater risk of mortality from COVID-19.ConclusionsPatients with COVID-19 with cardiovascular disease, hypertension, diabetes, congestive heart failure, chronic kidney disease and cancer have a greater risk of mortality compared to patients with COVID-19 without these comorbidities. Tailored infection prevention and treatment strategies targeting this high-risk population might improve survival. 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.0238215 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