Question: What new public datasets are available related to COVID-19?

Title: COVID-19: A Survey on Public Medical Imaging Data Resources Content: This regularly updated survey provides an overview of public resources that offer medical images and metadata of COVID-19 cases. The purpose of this survey is to simplify the access to open COVID-19 image data resources for all scientists currently working on the coronavirus crisis.

Title: Comparing and Integrating US COVID-19 Daily Data from Multiple Sources: A County-Level Dataset with Local Characteristics Content: Over the past several months, the outbreak of COVID-19 has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and for policy-makers to make better decisions. We collect the COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, and compare the similarities and differences among them. In addition, we examine the following problems which occur frequently: (1) the order dependencies violation, (2) the delay-reported issue on weekends and/or holidays, and (3) abnormal data point or data period. We also integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environment information, which are important for understanding the spread of the virus.

Title: A New Resource for Genomics and Precision Health Information and Publications on the Investigation and Control of COVID-19 and other Coronaviruses Content: Summary We developed a new online database that contains the most updated published scientific literature, online news and reports, CDC and National Institutes of Health (NIH) resources. The tool captures emerging discoveries and applications of genomics, molecular, and other precision medicine and precision public health tools in the investigation and control of coronavirus diseases, including COVID-19, MERS-CoV, and SARS. Availability Coronavirus Disease Portal (CDP) can be freely accessed via https://phgkb.cdc.gov/PHGKB/coVInfoStartPage.action. Contact wyu@cdc.gov

Title: Lest We Forget: A Dataset of Coronavirus-Related News Headlines in Swiss Media Content: We release our COVID-19 news dataset, containing more than 10,000 links to news articles related to the Coronavirus pandemic published in the Swiss media since early January 2020. This collection can prove beneficial in mining and analysis of the reaction of the Swiss media and the COVID-19 pandemic and extracting insightful information for further research. We hope this dataset helps researchers and the public deliver results that will help analyse the pandemic and potentially lead to a better understanding of the events.

Title: Open access institutional and news media tweet dataset for COVID-19 social science research Content: As COVID-19 quickly became one of the most concerned global crisis, the demand for data in academic research is also increasing. Currently, there are several open access Twitter datasets, but none of them is dedicated to the institutional and news media Twitter data collection, to fill this blank, we retrieved data from 69 institutional/news media Twitter accounts, 17 of them were related to government and international organizations, 52 of them were news media across North America, Europe and Asia. We believe our open access data can provide researchers more availability to conduct social science research.

Title: An Interactive Online Dashboard for Tracking COVID-19 in U.S. Counties, Cities, and States in Real Time Content: OBJECTIVE: To create an online resource that informs the public of COVID-19 outbreaks in their area. MATERIALS AND METHODS: This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard. RESULTS: The web resource, called the COVID-19 Watcher, can be accessed at https://covid19watcher.research.cchmc.org/. It displays COVID-19 data from every county and 188 metropolitan areas in the U.S. Features include rankings of the worst affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths. DISCUSSION: The Centers for Disease Control and Prevention (CDC) do not publish COVID-19 data for local municipalities, so it is critical that academic resources fill this void so the public can stay informed. The data used have limitations and likely underestimate the scale of the outbreak. CONCLUSIONS: The COVID-19 Watcher can provide the public with real-time updates of outbreaks in their area.

Title: Preservação da privacidade no enfrentamento da COVID-19: dados pessoais e a pandemia global./ Preservação da privacidade no enfrentamento da COVID-19: dados pessoais e a pandemia global./ Personal data usage and privacy considerations in the COVID-19 global pandemic Content: Data has become increasingly important and valuable for both scientists and health authorities searching for answers to the COVID-19 crisis. Due to difficulties in diagnosing this infection in populations around the world, initiatives supported by digital technologies are being developed by governments and private companies to enable the tracking of the public's symptoms, contacts and movements. Considering the current scenario, initiatives designed to support infection surveillance and monitoring are essential and necessary. Nonetheless, ethical, legal and technical questions abound regarding the amount and types of personal data being collected, processed, shared and used in the name of public health, as well as the concomitant or posterior use of this data. These challenges demonstrate the need for new models of responsible and transparent data and technology governance in efforts to control SARS-COV2, as well as in future public health emergencies.

Title: Personal data usage and privacy considerations in the COVID-19 global pandemic. Content: Data has become increasingly important and valuable for both scientists and health authorities searching for answers to the COVID-19 crisis. Due to difficulties in diagnosing this infection in populations around the world, initiatives supported by digital technologies are being developed by governments and private companies to enable the tracking of the public's symptoms, contacts and movements. Considering the current scenario, initiatives designed to support infection surveillance and monitoring are essential and necessary. Nonetheless, ethical, legal and technical questions abound regarding the amount and types of personal data being collected, processed, shared and used in the name of public health, as well as the concomitant or posterior use of this data. These challenges demonstrate the need for new models of responsible and transparent data and technology governance in efforts to control SARS-COV2, as well as in future public health emergencies.

Title: COVID-19: Open-data resources for monitoring, modeling, and forecasting the epidemic Content: We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources.

Title: Dataset for country profile and mobility analysis in the assessment of COVID-19 pandemic Content: Understanding the COVID-19 pandemic is a multidisciplinary effort that requires a significant number of variables. This dataset comprises (i) sociodemographic characteristics, compiled from 35 datasets obtained at UN Data; (ii) mobility metrics that can assist the analysis of social distancing, from Google Community Mobility Reports and; (iii) daily counts of cases and deaths by COVID-19, from the European Centre for Disease Prevention and Control and the Johns Hopkins University Center for Systems Science and Engineering. This unified dataset ranges from February 15, 2020 to April 26, 2020, a total of 72 days, and is provided as a collection of time series for 131 countries with 192 variables. The pipeline to preprocess and generate the dataset, along with the dataset itself, are versioned with the Data Version Control tool (DVC) and are thus easily reproducible.

Title: A real-time policy dashboard can aid global transparency in the response to coronavirus disease 2019 Content: There has been great provision of open data across the coronavirus disease 2019 (COVID-19) pandemic response, with, for example, dashboards presenting real-time descriptions of new daily cases and risk factors. Transparency has been an important discussion point and there have been concerns and criticisms of governments for not publishing the evidence base that is informing their decision-making. A 'policy dashboard' could act as a hub to show the localised reasoning behind COVID-19 policy decisions and allow the global health community to provide further support to governments and international stakeholders.

Title: A real-time policy dashboard can aid global transparency in the response to coronavirus disease 2019. Content: There has been great provision of open data across the coronavirus disease 2019 (COVID-19) pandemic response, with, for example, dashboards presenting real-time descriptions of new daily cases and risk factors. Transparency has been an important discussion point and there have been concerns and criticisms of governments for not publishing the evidence base that is informing their decision-making. A 'policy dashboard' could act as a hub to show the localised reasoning behind COVID-19 policy decisions and allow the global health community to provide further support to governments and international stakeholders.

Title: COVID-19 Government Response Event Dataset (CoronaNet v.1.0) Content: Governments worldwide have implemented countless policies in response to the COVID-19 pandemic. We present an initial public release of a large hand-coded dataset of over 13,000 such policy announcements across more than 195 countries. The dataset is updated daily, with a 5-day lag for validity checking. We document policies across numerous dimensions, including the type of policy, national versus subnational enforcement, the specific human group and geographical region targeted by the policy, and the time frame within which each policy is implemented. We further analyse the dataset using a Bayesian measurement model, which shows the quick acceleration of the adoption of costly policies across countries beginning in mid-March 2020 through 24 May 2020. We believe that these data will be instrumental for helping policymakers and researchers assess, among other objectives, how effective different policies are in addressing the spread and health outcomes of COVID-19.

Title: COVID-19 Government Response Event Dataset (CoronaNet v.1.0). Content: Governments worldwide have implemented countless policies in response to the COVID-19 pandemic. We present an initial public release of a large hand-coded dataset of over 13,000 such policy announcements across more than 195 countries. The dataset is updated daily, with a 5-day lag for validity checking. We document policies across numerous dimensions, including the type of policy, national versus subnational enforcement, the specific human group and geographical region targeted by the policy, and the time frame within which each policy is implemented. We further analyse the dataset using a Bayesian measurement model, which shows the quick acceleration of the adoption of costly policies across countries beginning in mid-March 2020 through 24 May 2020. We believe that these data will be instrumental for helping policymakers and researchers assess, among other objectives, how effective different policies are in addressing the spread and health outcomes of COVID-19.

Title: Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey Content: The widely spread CoronaVirus Disease (COVID)-19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

Title: A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms Content: The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries [1,2], as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360[3] and TCdata360[4] platforms. From these platforms, only annual indicators from 2015 and later were collected, and their missing values were replaced with previous annual values, in descending order by year, until 2015. During preprocessing, indicators (columns) were filtered out when the ratio of missing values exceeded 50%. Then, the same filtration was applied for the ratio of missing values above 25% in the case of countries (rows). Finally, duplicated variables were removed from the dataset. As a result of these steps, the missing value rate of the employed indicators was reduced to 4.25% on average. In addition to the database, the Kendall rank correlation matrix is provided to facilitate subsequent analysis. The dataset and the correlation matrix can be updated and customized with an R Notebook file, which is also available publicly in Mendeley Data [5].

Title: CORD-19: The Covid-19 Open Research Dataset. Content: The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 75K times and has served as the basis of many Covid-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and preview tools and upcoming shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for Covid-19.

Title: Open Data Resources for Fighting COVID-19 Content: We provide an insight into the open data resources pertinent to the study of the spread of Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behaviour, regional mortality rates, and effectiveness of government measures. Open data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, at a world scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 data-sets at a country-wide level (i.e. China, Italy, Spain, France, Germany, U.S., etc.). In an attempt to facilitate the rapid response to the study of the seasonal behaviour of Covid-19, we enumerate the main open resources in terms of weather and climate variables. CONCO-Team: The authors of this paper belong to the CONtrol COvid-19 Team, which is composed of different researches from universities of Spain, Italy, France, Germany, United Kingdom and Argentina. The main goal of CONCO-Team is to develop data-driven methods for the better understanding and control of the pandemic.

Title: Common Pitfalls in the Interpretation of COVID-19 Data and Statistics Content: Policymakers, experts and the general public heavily rely on the data that are being reported in the context of the coronavirus pandemic. Daily data releases on confirmed COVID-19 cases and deaths provide information on the course of the pandemic.

Title: CovidCounties - an interactive, real-time tracker of the COVID-19 pandemic at the level of US counties Content: Management of the COVID-19 pandemic has proven to be a significant challenge to policy makers. This is in large part due to uneven reporting and the absence of open-access visualization tools to present local trends and infer healthcare needs. Here we report the development of CovidCounties.org, an interactive web application that depicts daily disease trends at the level of US counties using time series plots and maps. This application is accompanied by a manually curated dataset that catalogs all major public policy actions made at the state-level, as well as technical validation of the primary data. Finally, the underlying code for the site is also provided as open source, enabling others to validate and learn from this work.

Title: Autopsy registry can facilitate COVID-19 research Content: The WHO declared the global outbreak of SARS-CoV-2 a pandemic on March 11, 2020, and "call(ed) on all countries to exchange country experiences and practices in a transparent and timely way" (http://www.euro.who.int/en/health-topics/health-emergencies/pages/news/news/2020/03/who-announces-covid-19-outbreak-a-pandemic). To date, many medical societies have announced their intention to collect and analyze data from COVID-19 patients and some large-scale prospective data collections are already running, such as the LEOSS registry (Lean European Open Survey on SARS-CoV-2 Infected Patients) or the CAPACITYCOVID registry (registry of patients with COVID-19 including cardiovascular risk and complications). The necessity to mobilize and harmonize basic and applied research worldwide is of utmost importance (Sansonetti, 2020).

Title: A County-level Dataset for Informing the United States' Response to COVID-19 Content: As the coronavirus disease 2019 (COVID-19) becomes a global pandemic, policy makers must enact interventions to stop its spread. Data driven approaches might supply information to support the implementation of mitigation and suppression strategies. To facilitate research in this direction, we present a machine-readable dataset that aggregates relevant data from governmental, journalistic, and academic sources on the county level. In addition to county-level time-series data from the JHU CSSE COVID-19 Dashboard, our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and in come, climate, transit scores, and healthcare system-related metrics. Furthermore, we present aggregated out-of-home activity information for various points of interest for each county, including grocery stores and hospitals, summarizing data from SafeGraph. By collecting these data, as well as providing tools to read them, we hope to aid researchers investigating how the disease spreads and which communities are best able to accommodate stay-at-home mitigation efforts. Our dataset and associated code are available at https://github.com/JieYingWu/COVID-19_US_County-level_Summaries.

Title: COVID-19 Variants Database: A repository for Human SARS-CoV-2 Polymorphism Data Content: COVID-19 is a newly communicable disease with a catastrophe outbreak that affects all over the world. We retrieved about 8,781 nucleotide fragments and complete genomes of SARS-CoV-2 reported from sixty-four countries. The CoV-2 reference genome was obtained from the National Genomics Data Center (NGDC), GISAID, and NCBI Genbank. All the sequences were aligned against reference genomes using Clustal Omega and variants were called using in-house built Python script. We intend to establish a user-friendly online resource to visualize the variants in the viral genome along with the Primer Infopedia. After analyzing and filtering the data globally, it was made available to the public. The detail of data available to the public includes mutations from 5688 SARS-CoV-2 sequences curated from 91 regions. This database incorporated 39920 mutations over 3990 unique positions. According to the translational impact, these mutations include 11829 synonymous mutations including 681 synonymous frameshifts and 21701 nonsynonymous mutations including 10 nonsynonymous frameshifts. Development of SARS-CoV-2 mutation genome browsers is a fundamental step obliging towards the virus surveillance, viral detection, and development of vaccine and therapeutic drugs. The SARS-COV-2 mutation browser is available at http://covid-19.dnageography.com.

Title: A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19 Content: In the past few months, several works were published in regards to the dynamics and early detection of COVID-19 via mathematical modeling and Artificial intelligence (AI). The aim of this work is to provide the research community with comprehensive overview of the methods used in these studies as well as a compendium of available open source datasets in regards to COVID-19. In all, 61 journal articles, reports, fact sheets, and websites dealing with COVID-19 were studied and reviewed. It was found that most mathematical modeling done were based on the Susceptible-Exposed-Infected-Removed (SEIR) and Susceptible-infected-recovered (SIR) models while most of the AI implementations were Convolutional Neural Network (CNN) on X-ray and CT images. In terms of available datasets, they include aggregated case reports, medical images, management strategies, healthcare workforce, demography, and mobility during the outbreak. Both Mathematical modeling and AI have both shown to be reliable tools in the fight against this pandemic. Several datasets concerning the COVID-19 have also been collected and shared open source. However, much work is needed to be done in the diversification of the datasets. Other AI and modeling applications in healthcare should be explored in regards to this COVID-19.

Title: Use of Available Data To Inform The COVID-19 Outbreak in South Africa: A Case Study Content: The coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization (WHO) in February 2020. Currently, there are no vaccines or treatments that have been approved after clinical trials. Social distancing measures, including travel bans, school closure, and quarantine applied to countries or regions are being used to limit the spread of the disease and the demand on the healthcare infrastructure. The seclusion of groups and individuals has led to limited access to accurate information. To update the public, especially in South Africa, announcements are made by the minister of health daily. These announcements narrate the confirmed COVID-19 cases and include the age, gender, and travel history of people who have tested positive for the disease. Additionally, the South African National Institute for Communicable Diseases updates a daily infographic summarising the number of tests performed, confirmed cases, mortality rate, and the regions affected. However, the age of the patient and other nuanced data regarding the transmission is only shared in the daily announcements and not on the updated infographic. To disseminate this information, the Data Science for Social Impact research group at the University of Pretoria, South Africa, has worked on curating and applying publicly available data in a way that is computer-readable so that information can be shared to the public - using both a data repository and a dashboard. Through collaborative practices, a variety of challenges related to publicly available data in South Africa came to the fore. These include shortcomings in the accessibility, integrity, and data management practices between governmental departments and the South African public. In this paper, solutions to these problems will be shared by using a publicly available data repository and dashboard as a case study.

Title: CovidNet: To Bring Data Transparency in the Era of COVID-19 Content: Timely, creditable, and fine-granular case information is vital for local communities and individual citizens to make rational and data-driven responses to the COVID-19 pandemic. This paper presents CovidNet, a COVID-19 tracking project associated with a large scale epidemic dataset, which was initiated by 1Point3Acres. To the best of our knowledge, the project is the only platform providing real-time global case information of more than 4,124 sub-divisions from over 27 countries worldwide with multi-language supports. The platform also offers interactive visualization tools to analyze the full historical case curves in each region. Initially launched as a voluntary project to bridge the data transparency gap in North America in January 2020, this project by far has become one of the major independent sources worldwide and has been consumed by many other tracking platforms. The accuracy and freshness of the dataset is a result of the painstaking efforts from our voluntary teamwork, crowd-sourcing channels, and automated data pipelines. As of May 18, 2020, the project website has been visited more than 200 million times and the CovidNet dataset has empowered over 522 institutions and organizations worldwide in policy-making and academic researches. All datasets are openly accessible for non-commercial purposes at https://coronavirus.1point3acres.com via a formal request through our APIs.

Title: The SARS-CoV-2 outbreak: What we know Content: There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus.

Title: Epidemiological data from the COVID-19 outbreak, real-time case information Content: Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.

Title: Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release Content: Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. As a contribution to that effort, we are aggregating numerous small molecules from a variety of sources, using high-performance computing (HPC) to computer diverse properties of those molecules, using the computed properties to train ML/AI models, and then using the resulting models for screening. In this first data release, we make available 23 datasets collected from community sources representing over 4.2 B molecules enriched with pre-computed: 1) molecular fingerprints to aid similarity searches, 2) 2D images of molecules to enable exploration and application of image-based deep learning methods, and 3) 2D and 3D molecular descriptors to speed development of machine learning models. This data release encompasses structural information on the 4.2 B molecules and 60 TB of pre-computed data. Future releases will expand the data to include more detailed molecular simulations, computed models, and other products.

Title: OpenSAFELY: factors associated with COVID-19 death in 17 million patients. Content: COVID-19 has rapidly affected mortality worldwide1. There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England, here we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59, 95% confidence interval (CI) 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people with white ethnicity, Black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.30-1.69 and 1.44, 1.32-1.58, respectively). We have quantified a range of clinical risk factors for COVID-19-related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients' records; we will update and extend results regularly.

Title: OpenSAFELY: factors associated with COVID-19 death in 17 million patients Content: COVID-19 has rapidly affected mortality worldwide1. There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England, here we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59, 95% confidence interval (CI) 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people with white ethnicity, Black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.30-1.69 and 1.44, 1.32-1.58, respectively). We have quantified a range of clinical risk factors for COVID-19-related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients' records; we will update and extend results regularly.

Title: The SARS-CoV-2 outbreak: What we know Content: Abstract There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus.

Title: Tracing open data in emergencies: the case of the COVID-19 pandemic Content: BACKGROUND: The COVID-19 pandemic constitutes an ongoing, burning Public Health Emergency of International Concern (PHEIC). In 2015, the World Health Organization (WHO) adopted an open-data policy recommendation in such situations. OBJECTIVES: The present cross-sectional meta-research study aimed to assess the availability of open data and metrics and of articles pertaining to the COVID-19 outbreak in five high-impact journals. METHODS: All articles regarding the SARS-CoV-2, published in five high impact journals (Ann Intern Med, BMJ, JAMA, NEJM and Lancet) until March 14, 2020 were retrieved. Meta-data (namely the type of article, number of authors, number of patients, citations, errata, news and social media mentions) were extracted for each article in each journal in a systematic way. Google Scholar and Scopus were used for citations and author metrics respectively, and Altmetrics and PlumX were used for news and social media mentions retrieval. The degree of adherence to the PHEIC open data call was also evaluated. RESULTS: A total of 140 articles were published until March 14, 2020, mostly opinion papers. Sixteen errata followed these publications. The number of authors in each article ranged from 1 to 63, whereas the number of patients with a laboratory-confirmed SARS-CoV-2 infection reached 2,645. The impact of these publications reached a total of 4,210 cumulative crude citations and 342,790 news and social media mentions. Only one publication (0.7%) provided complete open data, while 32 (22.9%) included patient data. CONCLUSIONS: Even though a large number of manuscripts was produced since the pandemic, availability of open data remains restricted.

Title: CORD-19: The COVID-19 Open Research Dataset Content: The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19.

Title: The first few cases and fatalities of Corona Virus Disease 2019 (COVID-19) in the Eastern Mediterranean Region of the World Health Organization: A rapid review Content: In December 2019, a cluster of atypical Pneumonia cases in Wuhan, China were reported to the World Health Organization (WHO). Later, those cases were attributed to a novel respiratory virus currently known as COVID-19. The infection is affecting every continent. It was characterized by WHO as a global pandemic on 11 March 2020. Countries worldwide are implementing various preventive measures to contain the spread of the infection such as travel and trade restrictions, closure of educational institutions and shops, and some took more strict measures such as imposing curfew. WHO is emphasizing the importance of early detection of cases, contact tracing, risk communications, implementing multisectoral approach in order to combat COVID-19 infection. Countries should provide the public with accurate, transparent information about the local and global situation of this escalating infection. Much uncertainty still surrounds this viral infection, its modes of transmission and dynamics. Epidemiological investigations particularly for the first few cases of COVID-19 infection are critical to expand our knowledge about this evolving pandemic. In this review we summarized the data available about the first few cases and fatalities of COVID-19 infection up to 18 March 2020 across Eastern Mediterranean Region of the World Health Organization. such data were only available in websites of ministries of health of the targeted countries, WHO situational reports, online newspapers, and other media channels and this gave us an idea about the amount and type of data available for the public regarding this evolving infection.

Title: How to Best Predict the Daily Number of New Infections of Covid-19 Content: Knowledge about the daily number of new infections of Covid-19 is important because it is the basis for political decisions resulting in lockdowns and urgent health care measures. We use Germany as an example to illustrate shortcomings of official numbers, which are, at least in Germany, disclosed only with several days of delay and severely underreported on weekends (more than 40%). These shortcomings outline an urgent need for alternative data sources. The other widely cited source provided by the Center for Systems Science and Engineering at Johns Hopkins University (JHU) also deviates for Germany on average by 79% from the official numbers. We argue that Google Search and Twitter data should complement official numbers. They predict even better than the original values from Johns Hopkins University and do so several days ahead. These two data sources could also be used in parts of the world where official numbers do not exist or are perceived to be unreliable.

Title: COVID-19, cytokines and immunosuppression: what can we learn from severe acute respiratory syndrome? Content: A severe outbreak of coronavirus disease 2019 (COVID-19) emerged in China in December 2019, and spread so rapidly that more than 200,000 cases have so far been reported worldwide; on January 30, 2020, the WHO declared it the sixth public health emergency of international concern. The two previously reported coronavirus epidemics (severe acute respiratory syndrome [SARS] and Middle East respiratory syndrome [MERS]) share similar pathogenetic, epidemiological and clinical features as COVID-19. As little is currently known about SARS-CoV-2, it is likely that lessons learned from these major epidemics can be applied to the new pandemic, including the use of novel immunosuppressive drugs.

Title: An OpenData portal to share COVID-19 drug repurposing data in real time Content: The National Center for Advancing Translational Sciences (NCATS) has developed an online open science data portal for its COVID-19 drug repurposing campaign – named OpenData – with the goal of making data across a range of SARS-CoV-2 related assays available in real-time. The assays developed cover a wide spectrum of the SARS-CoV-2 life cycle, including both viral and human (host) targets. In total, over 10,000 compounds are being tested in full concentration-response ranges from across multiple annotated small molecule libraries, including approved drug, repurposing candidates and experimental therapeutics designed to modulate a wide range of cellular targets. The goal is to support research scientists, clinical investigators and public health officials through open data sharing and analysis tools to expedite the development of SARS-CoV-2 interventions, and to prioritize promising compounds and repurposed drugs for further development in treating COVID-19.

Title: COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature Content: The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. We also designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.

Title: COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature. Content: The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. We also designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.

Title: What Are People Asking About COVID-19? A Question Classification Dataset Content: We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question classes. The most common questions in our dataset asked about transmission, prevention, and societal effects of COVID, and we found that many questions that appeared in multiple sources were not answered by any FAQ websites of reputable organizations such as the CDC and FDA. We post our dataset publicly at https://github.com/JerryWei03/COVID-Q . For classifying questions into 15 categories, a BERT baseline scored 58.1% accuracy when trained on 20 examples per class, and for classifying questions into 89 question classes, the baseline achieved 54.6% accuracy. We hope COVID-Q can be helpful either for direct use in developing applied systems or as a domain-specific resource for model evaluation.

Title: COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database. Content: In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing ROBOKOP biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to test new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19. COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.

Title: COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database Content: In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing ROBOKOP biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to test new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19. COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.

Title: A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration Content: As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.

Title: A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration. Content: As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.

Title: Analyzing the epidemiological outbreak of COVID-19: A visual exploratory data analysis approach Content: There is an obvious concern globally regarding the fact about the emerging coronavirus 2019 novel coronavirus (2019-nCoV) as a worldwide public health threat. As the outbreak of COVID-19 causes by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness and intervention. The recent outbreak of pneumonia in Wuhan, China, caused by the SARS-CoV-2 emphasizes the importance of analyzing the epidemiological data of this novel virus and predicting their risks of infecting people all around the globe. In this study, we present an effort to compile and analyze epidemiological outbreak information on COVID-19 based on the several open datasets on 2019-nCoV provided by the Johns Hopkins University, World Health Organization, Chinese Center for Disease Control and Prevention, National Health Commission, and DXY. An exploratory data analysis with visualizations has been made to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China. Overall, at the outset of an outbreak like this, it is highly important to readily provide information to begin the evaluation necessary to understand the risks and begin containment activities.

Title: COVID-19 Datasets: A Survey and Future Challenges Content: In December 2019, a novel virus named as COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as most consequential global crisis after the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. On the other hand, the medical service providers are the first responders for infected persons with severe symptoms of COVID-19. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open-source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open-source efforts that can be mainly categorized as: (a) COVID-19 diagnosis from CT scans and X-ray images, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We review and critically analyze works in these directions that are accompanied by open-source data and code. We hope that the article will provide the scientific community with an initiative to start open-source extensible and transparent research in the collective fight against COVID-19.

Title: Online Information Search During COVID-19 Content: Public information search data from sources such as Google Trends affords researchers a perspective on what society does not know, or what society wants to find out prompted by, or in response to, developments in societal communication and news media events. In times of crisis, online public information search thus offers a window into the most urgent concerns of, and demands by society. By extension, such data offers a window into upcoming, and currently pressing demands on businesses, policymakers and researchers. This research note aims to illustrate some of these themes with a visual-based overview of the current COVID-19 / Coronavirus crisis.

Title: Negative Nasopharyngeal and Oropharyngeal Swabs Do Not Rule Out COVID-19 Content: Coronavirus Disease 19 (COVID-19), has become the Public Health Emergency of International Concern.….

Title: Scientific solidarity in the face of the COVID-19 pandemic: researchers, publishers, and medical associations Content: In the face of COVID-19, the scientific community has rapidly come together to address this outbreak in an open and collaborative manner to support the global response to this outbreak by rapidly sharing and highlighting research data and relevant findings COVID-19 research is being published at a furious pace Over 6,000 articles have been published as of 20 April 2020, and at least 15 online resource centers/websites for COVID-19 have been created by publishers to enable fast and free access to the latest research, evidence, and data available Moreover, many evidence-based guidelines for COVID-19 have been issued based on academic articles and summaries of the experiences of frontline medical personnel Various academic medical associations are also actively sharing information and providing technical support As an example, 93 guides/proposals/responses to COVID-19 have been issued so far by 50 medical associations in Japan However, few publications and national situation reports have provided information on the number of infected healthcare workers (HCWs) More publications and national situation reports are urgently needed to provide scientific information to devise specific infection prevention and control measures in order to protect HCWs from infection

Title: Tracing open data in emergencies: the case of the COVID‐19 pandemic Content: BACKGROUND: The COVID‐19 pandemic constitutes an ongoing, burning Public Health Emergency of International Concern (PHEIC). In 2015, the World Health Organization (WHO) adopted an open‐data policy recommendation in such situations. OBJECTIVES: The present cross‐sectional meta‐research study aimed to assess the availability of open data and metrics and of articles pertaining to the COVID‐19 outbreak in five high‐impact journals. METHODS: All articles regarding the SARS‐CoV‐2, published in five high impact journals (Ann Intern Med, BMJ, JAMA, NEJM and Lancet) until March 14, 2020 were retrieved. Meta‐data (namely the type of article, number of authors, number of patients, citations, errata, news and social media mentions) were extracted for each article in each journal in a systematic way. Google Scholar and Scopus were used for citations and author metrics respectively, and Altmetrics and PlumX were used for news and social media mentions retrieval. The degree of adherence to the PHEIC open data call was also evaluated. RESULTS: A total of 140 articles were published until March 14, 2020, mostly opinion papers. Sixteen errata followed these publications. The number of authors in each article ranged from 1 to 63, whereas the number of patients with a laboratory‐confirmed SARS‐CoV‐2 infection reached 2,645. The impact of these publications reached a total of 4,210 cumulative crude citations and 342,790 news and social media mentions. Only one publication (0.7%) provided complete open data, while 32 (22.9%) included patient data. CONCLUSIONS: Even though a large number of manuscripts was produced since the pandemic, availability of open data remains restricted.

Title: OUTBREAK: A user-friendly georeferencing online tool for disease surveillance Content: The current COVID-19 pandemic has already claimed more than 100,000 victims and it will cause more deaths in the coming months. Tools that can track the number and locations of cases are critical for surveillance and can help in making policy decisions for controlling the outbreak. The current surveillance web-based dashboards run on proprietary platforms, which are often expensive and require specific computational knowledge. We present a new tool (OUTBREAK) for studying and visualizing epidemiological data. It permits even non-specialist users to input data most conveniently and track outbreaks in real-time. This tool has the potential to guide and help health authorities to intervene and minimize the effects of the outbreaks. It is freely available at http://outbreak.sysbio.tools/.

Title: Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University Content: We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University. Our main objective is to provide predictions of the number of infected people for different countries in the next 14 days. The predictive analysis is done using time-series data transformed on a logarithmic scale. We use two well-known methods for prediction: polynomial regression and neural network. As the number of training data for each country is limited, we use a single-layer neural network called the extreme learning machine (ELM) to avoid over-fitting. Due to the non-stationary nature of the time-series, a sliding window approach is used to provide a more accurate prediction.

Title: Mental health groups providing support, education in wake of COVID‐19 Content: As news and updates continue to spread regarding the new coronavirus (COVID‐19), the deadly virus that causes respiratory illness and pneumonia, mental health groups and organizations have issued information to consumers, providers and the mental health community at large about ways of addressing the virus and seeking information from public health sources.

Title: Povidone‐iodine gargle as a prophylactic intervention to interrupt the transmission of SARS‐CoV‐2 Content: Coronavirus disease 2019 (COVID-19), caused by a novel coronavirus, SARS-COV-2 was declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) in January 2020. Human-to-Human transmission occurs through close contact with an infected person or surfaces that are contaminated with droplets or secretions.

Title: Repurposing current therapeutics for treating COVID-19: A vital role of prescription records data mining Content: Since its outbreak in late 2019, the SARS-Cov-2 pandemic already infected over 3.7 million people and claimed more than 250,000 lives globally. At least 1 year may take for an approved vaccine to be in place, and meanwhile millions more could be infected, some with fatal outcome. Over thousand clinical trials with COVID-19 patients are already listed in ClinicalTrials.com, some of them for assessing the utility of therapeutics approved for other conditions. However, clinical trials take many months, and are typically done with small cohorts. A much faster and by far more efficient method for rapidly identifying approved therapeutics that can be repurposed for treating COVID-19 patients is data mining their past and current electronic health and prescription records for identifying drugs that may protect infected individuals from severe COVID-19 symptoms. Examples are discussed for applying health and prescription records for assessing the potential repurposing (repositioning) of angiotensin receptor blockers, estradiol, or antiandrogens for reducing COVID-19 morbidity and fatalities. Data mining of prescription records of COVID-19 patients will not cancel the need for conducting controlled clinical trials, but could substantially assist in trial design, drug choice, inclusion and exclusion criteria, and prioritization. This approach requires a strong commitment of health provides for open collaboration with the biomedical research community, as health provides are typically the sole owners of retrospective drug prescription records.

Title: Open-source analytics tools for studying the COVID-19 coronavirus outbreak Content: To provide convenient access to epidemiological data on the coronavirus outbreak, we developed an R package, nCov2019 (https://github.com/GuangchuangYu/nCov2019). Besides detailed real-time statistics, it offers access to three data sources with detailed daily statistics from December 1, 2019, for 43 countries and more than 500 Chinese cities. We also developed a web app (http://www.bcloud.org/e/) with interactive plots and simple time-series forecasts. These analytics tools could be useful in informing the public and studying how this and similar viruses spread in populous countries.

Title: Rapid implementation of mobile technology for real-time epidemiology of COVID-19 Content: The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (COVID-19) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) consortium to bring together scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application that we launched in the UK on March 24, 2020 and the US on March 29, 2020 garnering more than 2.8 million users as of May 2, 2020. This mobile application offers data on risk factors, herald symptoms, clinical outcomes, and geographical hot spots. This initiative offers critical proof-of-concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis which is critical for a data-driven response to this public health challenge.

Title: What do we know about COVID-19? A review article Content: OBJECTIVES: To bring summarized information about what has been published so far regarding Covid-19, facilitating the access to information and a better understanding of this pandemic, and to contribute to the medical community in the decision-making against this virus METHODS: This review article brings collected information from different articles published since the beginning of the pandemic of the 2019 novel coronavirus KEY RESULTS: This paper aggregates and consolidates some epidemiological parameters and clinical knowledge about the novel coronavirus and brings what is new in the search for pandemic control MAJOR CONCLUSIONS: Governments and health authorities are under increased pressure to control the COVID-19 spreading In this scenario, the scientific community is working hard to produce relevant papers which will help in the next steps against coronavirus Our review summarized the latest news about SARS-CoV2, evidencing what we know about COVID-19 until now

Title: Mathematic modeling of COVID-19 in the United States Content: COVID-19, the worst pandemic in 100 years, has rapidly spread to the entire world in 2 months since its early report in January 2020. Based on the publicly available data sources, we developed a simple mathematic modeling approach to track the outbreaks of COVID-19 in the US and three selected states: New York, Michigan and California. The same approach is applicable to other regions or countries. We hope our work can stimulate more effort in understanding how an outbreak is developing and how big a scope it can be and in what kind of time framework. Such information is critical for outbreak control, resource utilization and re-opening of the normal daily life to citizens in the affected community.

Title: Keeping Journal Club Relevant in a Pandemic: The 'Rapid Fire' 5-Minute Format Content: There has been an explosion of data published in a short period of time about COVID-19. It can be difficult for learners to stay on top of the latest information while also critically appraising each new piece of information. As a result, there can be a lack of local consensus on how to best manage these patients as new data continually becomes available.

Title: Keeping Journal Club Relevant in a Pandemic: The 'Rapid Fire' 5-Minute Format. Content: There has been an explosion of data published in a short period of time about COVID-19. It can be difficult for learners to stay on top of the latest information while also critically appraising each new piece of information. As a result, there can be a lack of local consensus on how to best manage these patients as new data continually becomes available.

Title: COVID-19 Image Data Collection Content: This paper describes the initial COVID-19 open image data collection. It was created by assembling medical images from websites and publications and currently contains 123 frontal view X-rays.

Title: Mega-COV: A Billion-Scale Dataset of 65 Languages For COVID-19 Content: We describe Mega-COV, a billion-scale dataset from Twitter for studying COVID-19. The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets (~32M tweets). We release tweet IDs from the dataset, hoping it will be useful for studying various phenomena related to the ongoing pandemic and accelerating viable solutions to associated problems.

Title: Clinical Best Practice Advice for Hepatology and Liver Transplant Providers During the COVID-19 Pandemic: AASLD Expert Panel Consensus Statement Content: Coronavirus disease 2019 (COVID-19), the illness caused by the SARS-CoV-2 virus, is rapidly spreading throughout the world. Hospitals and healthcare providers are preparing for the anticipated surge in critically ill patients but few are wholly equipped to manage this new disease. We all must do our part to prepare our patients, clinics, and hospitals for the drastic changes necessary to mitigate the spread of SARS-CoV-2 or we risk overwhelming the capacity of our healthcare system. The goals of this document are to provide data on what is currently known about COVID-19, and how it may impact hepatologists and liver transplant providers and their patients. Our aim is to provide a template for the development of clinical recommendations and policies to mitigate the impact of the COVID-19 pandemic on liver patients and healthcare providers.

Title: Genomic surveillance of SARS-CoV-2 in Thailand reveals mixed imported populations, a local lineage expansion and a virus with truncated ORF7a Content: Coronavirus Disease 2019 (COVID-19) is a global public health threat. Genomic surveillance of SARS-CoV-2 was implemented during March 2020 at a major diagnostic hub in Bangkok, Thailand. Several virus lineages supposedly originated in many countries were found, and a Thai-specific lineage, designated A/Thai-1, has expanded to be predominant in Thailand. A virus sample in the SARS-CoV-2 A/Thai-1 lineage contains a frame-shift deletion at ORF7a, encoding a putative host antagonizing factor of the virus.

Title: Rapid implementation of mobile technology for real-time epidemiology of COVID-19 Content: The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (COVID-19) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) consortium to bring together scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application that we launched in the UK on March 24, 2020 and the US on March 29, 2020 garnering more than 2.25 million users to date. This mobile application offers data on risk factors, herald symptoms, clinical outcomes, and geographical hot spots. This initiative offers critical proof-of-concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis which is critical for a data-driven response to this public health challenge.

Title: Time Course of COVID-19 Cases in Austria Content: COVID-19 is an infectious disease caused by a novel coronavirus, which first appeared in China in late 2019, and reached pandemic distribution in early 2020. The first major outbreak in Europe occurred in Northern Italy where it spread to neighboring countries, notably to Austria, where skiing resorts served as a main transmission hub. Soon, the Austrian government introduced strict measures to curb the spread of the virus. Using publicly available data, we assessed the efficiency of the governmental measures. We assumed an average incubation period of one week and an average duration of infectivity of 10 days. One week after the introduction of strict measures, the increase in daily new cases was reversed, and the reproduction number dropped. The crude estimates tended to overestimate the reproduction rate in the early phase. Publicly available data provide a first estimate about the effectiveness of public health measures. However, more data are needed for an unbiased assessment.

Title: Monitoring the Covid-19 epidemics in Italy from mortality data Content: The mortality data can be used as an alternative source to monitor the status of Covid-19. We have studied a dataset including deaths up to the fourth week of April. There is a large excess, more pronounced at the beginning of the pandemic, showing a difference in age and gender compared to the Covid-19-confirmed cases. The study indicates that mortality information can be used to provide a less biased time profile of the pandemic.

Title: Should SARS-CoV-2 influence immunosuppressive therapy for autoimmune blistering diseases? Content: In this dramatic period where the whole world is affected by the outbreak of coronavirus disease 19 (COVID-19), scientific data relating to the causative virus SARS-CoV-2 as well as the subsequent therapeutic repercussions on the management of other diseases should be divulged in order to share as much information as possible among experts in a timely manner.

Title: Genetic diversity and evolution of SARS-CoV-2 Content: COVID-19 is a viral respiratory illness caused by a new coronavirus called SARS-CoV-2. The World Health Organization declared the SARS-CoV-2 outbreak a global public health emergency. We performed genetic analyses of eighty-six complete or near-complete genomes of SARS-CoV-2 and revealed many mutations and deletions on coding and non-coding regions. These observations provided evidence of the genetic diversity and rapid evolution of this novel coronavirus.

Title: What country, university or research institute, performed the best on COVID-19? Bibliometric analysis of scientific literature Content: In this article, we conduct data mining to discover the countries, universities and companies, produced or collaborated the most research on Covid-19 since the pandemic started. We present some interesting findings, but despite analysing all available records on COVID-19 from the Web of Science Core Collection, we failed to reach any significant conclusions on how the world responded to the COVID-19 pandemic. Therefore, we increased our analysis to include all available data records on pandemics and epidemics from 1900 to 2020. We discover some interesting results on countries, universities and companies, that produced collaborated most the most in research on pandemic and epidemics. Then we compared the results with the analysing on COVID-19 data records. This has created some interesting findings that are explained and graphically visualised in the article.

Title: Rapidly Bootstrapping a Question Answering Dataset for COVID-19 Content: We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. To our knowledge, this is the first publicly available resource of its type, and intended as a stopgap measure for guiding research until more substantial evaluation resources become available. While this dataset, comprising 124 question-article pairs as of the present version 0.1 release, does not have sufficient examples for supervised machine learning, we believe that it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19. This paper describes our methodology for constructing the dataset and presents the effectiveness of a number of baselines, including term-based techniques and various transformer-based models. The dataset is available at http://covidqa.ai/

Title: COVID-19, Australia: Epidemiology Report 3 (Reporting week ending 19:00 AEDT 15 February 2020) Content: This is the third epidemiological report for coronavirus disease 2019 (COVID-19), previously known as novel coronavirus (2019-nCoV), from the virus now known as SARS-CoV-2, reported in Australia as at 19:00 Australian Eastern Daylight Time [AEDT] 15 February 2020. It includes data on the COVID-19 Australian cases, the international situation and current information on the severity, transmission and spread.

Title: COVID-19, Australia: Epidemiology Report 3 (Reporting week ending 19:00 AEDT 15 February 2020). Content: This is the third epidemiological report for coronavirus disease 2019 (COVID-19), previously known as novel coronavirus (2019-nCoV), from the virus now known as SARS-CoV-2, reported in Australia as at 19:00 Australian Eastern Daylight Time [AEDT] 15 February 2020. It includes data on the COVID-19 Australian cases, the international situation and current information on the severity, transmission and spread.

Title: Coronaviruses and people with intellectual disability: an exploratory data analysis Content: BACKGROUND: Corona virus disease 2019 (COVID-19) has been announced as a new coronavirus disease by the World Health Organization. At the time of writing this article (April 2020), the world is drastically influenced by the COVID-19. Recently, the COVID-19 Open Research Dataset (CORD-19) was published. For researchers on ID such as ourselves, it is of key interest to learn whether this open research dataset may be used to investigate the virus and its consequences for people with an ID. METHODS: From CORD-19, we identified full-text articles containing terms related to the ID care and applied a text mining technique, specifically the term frequency-inverse document frequency analysis in combination with K-means clustering. RESULTS: Two hundred fifty-nine articles contained one or more of our specified terms related to ID. We were able to cluster these articles related to ID into five clusters on different topics, namely: mental health, viral diseases, diagnoses and treatments, maternal care and paediatrics, and genetics. CONCLUSION: The CORD-19 open research dataset consists of valuable information about not only COVID-19 disease but also ID and the relationship between them. We suggest researchers investigate literature-based discovery approaches on the CORD-19 and develop a new dataset that addresses the intersection of these two fields for further research.

Title: Artificial Intelligence-Powered Search Tools and Resources in the Fight Against COVID-19. Content: Emerging technologies are set to play an important role in our response to the COVID-19 pandemic. This paper explores three prominent initiatives: COVID-19 focused datasets (e.g., CORD-19); Artificial intelligence-powered search tools (e.g., WellAI, SciSight); and contact tracing based on mobile communication technology. We believe that increasing awareness of these tools will be important in future research into the disease, COVID-19, and the virus, SARS-CoV-2.

Title: Artificial Intelligence-Powered Search Tools and Resources in the Fight Against COVID-19 Content: Emerging technologies are set to play an important role in our response to the COVID-19 pandemic. This paper explores three prominent initiatives: COVID-19 focused datasets (e.g., CORD-19); Artificial intelligence-powered search tools (e.g., WellAI, SciSight); and contact tracing based on mobile communication technology. We believe that increasing awareness of these tools will be important in future research into the disease, COVID-19, and the virus, SARS-CoV-2.

Title: COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts Content: The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The results confirm a rapid increase in the volume of research, which particularly accessible through Google Scholar and Dimensions, and less through Scopus, the Web of Science, PubMed. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Google Scholar or Dimensions and can use tweet and Mendeley reader counts as indicators of likely importance.

Title: Point-of-Care RNA-Based Diagnostic Device for COVID-19 Content: At the end of 2019, the novel coronavirus disease (COVID-19), a fast-spreading respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was reported in Wuhan, China and has now affected over 123 countries globally [...].

Title: COVID-19 und die Niere Content: The drastic consequences that emerging infectious diseases can have for people and society are currently being demonstrated by coronavirus disease 2019 (COVID-19). Since its initial description in December 2019 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has dominated current scientific and public interest.

Title: Tests, surgical masks, hospital beds, and ventilators: add big data to the list of tools to fight the coronavirus that are in short supply Content: Big data could help identify potential clues about the immediate (and future) impact of coronavirus disease 2019, but it is in short supply.

Title: Tests, surgical masks, hospital beds, and ventilators: add big data to the list of tools to fight the coronavirus that are in short supply. Content: Big data could help identify potential clues about the immediate (and future) impact of coronavirus disease 2019, but it is in short supply.

Title: Evaluation of a rapid diagnostic assay for detection of SARS CoV-2 antigen in nasopharyngeal swab Content: Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the virus causing causing Coronavirus disease 2019 (COVID-19) was reported for the first time in Wuhan (Hubei, China) in December 2019 (1, 2) and has become a major public health concern all over the world. .

Title: Mapping the South African health landscape in response to COVID-19 Content: When the COVID-19 disease pandemic infiltrated the world, there was an immediate need for accurate information. As with any outbreak, the outbreak follows a clear trajectory, and subsequently, the supporting information for that outbreak needs to address the needs associated with that stage of the outbreak. At first, there was a need to inform the public of the information related to the initial situation related to the"who"of the COVID-19 disease. However, as time continued, the"where","when"and"how to"related questions started to emerge in relation to the public healthcare system themselves. Questions surrounding the health facilities including COVID-19 hospital bed capacity, locations of designated COVID-19 facilities, and general information related to these facilities were not easily accessible to the general public. Furthermore, the available information was found to be outdated, fragmented across several platforms, and still had gaps in the data related to these facilities. To rectify this problem, a group of volunteers working on the covid19za project stepped in to assist. Each member leading a part of the project chose to focus on one of four problems related to the challenges associated with the Hospital information including: data quality, data completeness, data source validation and data visualisation capacity. As the project developed, so did the sophistication of the data, visualisation and core function of the project. The future prospects of this project relate to a Progressive Web Application that will avail this information for the public as well as healthcare workers through comprehensive mapping and data quality.

Title: An Interactive Tool to Forecast US Hospital Needs in the Coronavirus 2019 Pandemic Content: Hospital enterprises are currently faced with anticipating the spread of COVID-19 and the effects it will have on visits, admissions, bed needs, and crucial supplies. While many studies have focused on understanding the basic epidemiology of the disease, few open source tools have been made available to aid hospitals in their planning. We developed a web-based application for US states and territories that allows users to choose from a suite of models already employed in characterizing the spread of COVID-19. Users can obtain forecasts for hospital visits and admissions as well as anticipated needs for ICU and non-ICU beds, ventilators, and personal protective equipment supplies. Users can also customize a large set of inputs, view the variability in forecasts over time, and download forecast data. We describe our web application and its models in detail and provide recommendations and caveats for its use. Our application is primarily designed for hospital leaders, healthcare workers, and government official who may lack specialized knowledge in epidemiology and modeling. However, specialists can also use our open source code as a platform for modification and deeper study. As the dynamics of COVID-19 within the US change, our application will also change to meet emerging needs and questions of the healthcare community.

Title: Evaluation of a rapid diagnostic assay for detection of SARS CoV-2 antigen in nasopharyngeal swab. Content: Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the virus causing causing Coronavirus disease 2019 (COVID-19) was reported for the first time in Wuhan (Hubei, China) in December 2019 (1, 2) and has become a major public health concern all over the world.….

Title: Concerns and Fears of Indian Dentists on Professional Practice During the Coronavirus Disease-2019 (COVID-19) Pandemic Content: The Coronavirus disease-2019 (COVID-19) pandemic, due to the Severe Acute Respiratory Syndrome Virus 2 (SARS-CoV-2) was declared a public health emergency of international concern by the World Health Organization on 30th of January 2020 (World Health Organization, 2020). The first case of COVID-19 was reported in India on the same day, and at the time of writing, on 20th May 2020, India has reported over 106,000 patients, with over 3,300 related deaths. The country is under a state of lockdown since 25th March, 2020, and over 600 health care providers have been infected.

Title: How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic Content: SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.

Title: Global Preparedness Against COVID-19: We Must Leverage the Power of Digital Health Content: The coronavirus disease (COVID-19) pandemic has revealed many areas of public health preparedness that are lacking, especially in lower- and middle-income countries. Digital interventions provide many opportunities for strengthening health systems and could be vital resources in the current public health emergency. We provide several use cases for infection control, home-based diagnosis and screening, empowerment through information, public health surveillance and epidemiology, and leveraging crowd-sourced data. A thoughtful, concerted effort—leveraging existing experience and robust enterprise-grade technologies—can have a substantive impact on the immediate and distal consequences of COVID-19.

Title: Go local: The key to controlling the COVID-19 pandemic in the post lockdown era Content: The UK government announced its first wave of lockdown easing on 10 May 2020, two months after the non-pharmaceutical measures to reduce the spread of COVID-19 were first introduced on 23 March 2020. Analysis of reported case rate data from Public Health England and aggregated and anonymised crowd level mobility data shows variability across local authorities in the UK. A locality-based approach to lockdown easing is needed, enabling local public health and associated health and social care services to rapidly respond to emerging hotspots of infection. National level data will hide an increasing heterogeneity of COVID-19 infections and mobility, and new ways of real-time data presentation to the public are required. Data sources (including mobile) allow for faster visualisation than more traditional data sources, and are part of a wider trend towards near real-time analysis of outbreaks needed for timely, targeted local public health interventions. Real time data visualisation may give early warnings of unusual levels of activity which warrant further investigation by local public health authorities.

Title: Challenges and opportunities brought by COVID-19: Understanding and prevention of COVID-19 Content: The ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome (SARS) coronavirus 2 was first reported in Wuhan, Hubei Province at the end of December 2019. The number of confirmed cases is increasing every day, causing more deaths than the SARS outbreak in China from 2002 to 2003. COVID-19 has become a public health emergency of global concern, and all governments and medical institutions are on high alert. Therefore, we summarize the genomics, epidemiology, clinical manifestations, diagnosis, treatment, and other aspects of COVID-19 based on the currently available data.

Title: COVID-19: air pollution remains low as people stay at home Content: Coronavirus diseases 2019 (COVID-19) is transmitted worldwide in over a very short time, as it was originated in late 2019 from Wuhan city, China. To reduce the possible effects due to COVID-19, some sort of lockdown activities have been applied in many countries. In this regard, the outcomes reported bonus benefits to the natural environment showing a significant decrease in air pollution worldwide due to COVID-19. The National Aeronautics and Space Administration (NASA) and European Space Agency (ESA) released air pollution data for Asian and European countries to assess the significant changes in air quality. The main objective of the study is to compare the air quality data released by international agencies before and after the novel coronavirus pandemic.

Title: The geographic spread of COVID-19 correlates with structure of social networks as measured by Facebook Content: We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19"hotspots"(Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

Title: Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death Content: "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", the novel coronavirus, is responsible for the ongoing worldwide pandemic. "World Health Organization (WHO)" assigned an "International Classification of Diseases (ICD)" code-"COVID-19"-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with "Middle East Respiratory Syndrome (MERS-CoV)", "Severe Acute Respiratory Syndrome (SARS-CoV)", and now with this new virus, namely "SARS-CoV-2", or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a "spread factor (f)", and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in "ourworldindata.org (Oxford University database)", and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months - January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.

Title: Infodemiological Study Using Google Trends on Coronavirus Epidemic in Wuhan, China Content: The recent emergence of a new coronavirus (COVID-19) has gained a high cover in public media and worldwide news. The virus has caused a viral pneumonia in tens of thousands of people in Wuhan, a central city of China. This short paper gives a brief introduction on how the demand for information on this new epidemic is reported through Google Trends. The reported period is 31 December 2020 to 20 March 2020. The authors draw conclusions on current infodemiological data on COVID-19 using three main search keywords: coronavirus, SARS and MERS. Two approaches are set. First is the worldwide perspective, second - the Chinese one, which reveals that in China this disease in the first days was more often referred to SARS then to general coronaviruses, whereas worldwide, since the beginning, it is more often referred to coronaviruses.

Title: Coinfection with SARS-CoV-2 and other respiratory pathogens in COVID-19 patients in Guangzhou, China Content: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; previously provisionally named 2019 novel coronavirus or 2019-nCoV) disease (COVID-19) in China at the end of 2019 has caused a global pandemic and remains as a major public health issue This article is protected by copyright. All rights reserved.

Title: Where we are in fighting against COVID-19 Content: On March 11, 2020, the World Health Organization announced the novel coronavirus disease (COVID-19) as a pandemic. Despite an increasing number of international attempts using maps to present and communicate COVID-19-related information in different organizations, most map products have only used the presentation function of maps. Against this backdrop, we offer an automatically daily-updated, color-blind-friendly, Tableau-based interactive dashboard to demonstrate where and how different countries are fighting against COVID-19. The dashboard allows users to specify countries they want to compare and aggregate relevant data on a daily, weekly, or monthly basis.

Title: First-principles machine learning modelling of COVID-19 Content: The coronavirus disease 2019 (COVID-19) has changed the world since the World Health Organization declared its outbreak on 30th January 2020, recognizing the outbreak as a pandemic on 11th March 2020. As often said by politicians and scientific advisors, the objective is"to flatten the curve", or"push the peak down", or similar wording, of the virus spreading. Central to the official advice are mathematical models and data, which provide estimates on the evolution of the number of infected, recovered and deaths. The accuracy of the models is improved day by day by inferring the contact, recovery, and death rates from data (confirmed cases). A data-driven model trained with {\it both} data {\it and} first principles is proposed. The model can quickly be re-trained any time that new data becomes available. The method can be applied to more detailed epidemic models with virtually no conceptual modification.

Title: Public Concern and the Financial Markets during the COVID-19 outbreak Content: We measure the public concern during the outbreak of COVID-19 disease using three data sources from Google Trends (YouTube, Google News, and Google Search). Our findings are three-fold. First, the public concern in Italy is found to be a driver of the concerns in other countries. Second, we document that Google Trends data for Italy better explains the stock index returns of France, Germany, Great Britain, the United States, and Spain with respect to their country-based indicators. Finally, we perform a time-varying analysis and identify that the most severe impacts in the financial markets occur at each step of the Italian lock-down process.