In addition, Elsevier is now enabling full text and data mining of this growing body of knowledge through channels such as the NIH’s PubMed Central and the WHO database for free and without copyright limitations.

The site is curated by clinical experts from our Global Health business and updated frequently to keep pace with evolving knowledge.

Elsevier, Cell Press and The Lancet have also joined the signatories on the Wellcome Trust’s Statement designed to ensure that research findings and data relevant to this outbreak are shared rapidly and openly to inform the public health response and help save lives.

If you're looking for information on our other free and low-cost access programs, including resources for patients and caregivers, you can find that here.

• Episode 6: Pulmonary Aspects of COVID-19 Disease: Clinical Course, Ventilation and Related Issues features Esther Barreiro Portela, MD, PhD, Associate Professor, Pompeu Fabra University, Hospital del Mar Respiratory Medicine Department, Hospital del Mar Medical Research Institute (IMIM), Barcelona. With host Margaret Trexler Hessen, MD, Director, Point of Care, for Elsevier, Dr. Barreiro discusses the pulmonary aspects of COVID-19, such as clinical course and pathophysiology of lung involvement, noninvasive vs. mechanical ventilation based on observed phenotypes, and myocardial dysfunction, coagulopathy and other issues relating to the pathophysiology and critical care of these patients.

• Episode 5: Biomedical Ethics During COVID-19 Pandemic, Part 2: Triaging Care, Allocation of Equipment, and Protecting the Providers features Prof. Don Postema, PhD, Program Director of Medical Ethics, HealthPartners, Center for Bioethics, University of Minnesota, and host Dr. Julibeth Lauren, PhD, APRN, ACNS-BC, VP, Editor-in-Chief, Nursing and Allied Health Practice and Patient Education at Elsevier. They discuss issues arising with patients and families, including restrictions on inpatient visiting, end-of-life issues, and how patients and families can obtain support from hospital biomedical ethics teams.

• Episode 4: Biomedical Ethics During COVID-19 Pandemic: Part 1: Supporting Patients and Families in Times of Crisis features Prof. Don Postema, PhD, Program Director of Medical Ethics, HealthPartners, Center for Bioethics, University of Minnesota, and host Dr. Julibeth Lauren, PhD, APRN, ACNS-BC, VP, Editor-in-Chief, Nursing and Allied Health Practice and Patient Education at Elsevier. They discuss issues arising with patients and families, including restrictions on inpatient visiting, end-of-life issues, and how patients and families can obtain support from hospital biomedical ethics teams.

• Episode 3: Medical Informatics and COVID-19: Role and Utility of Hospital Digital Resources and Telemedicine in Managing the Pandemic features Dr. Rebecca G. Mishuris, MD, Associate Chief Medical Information Officer at Boston Medical Center and Assistant Professor of Medicine at the Boston University School of Medicine. In this episode, Dr. Mishuris highlights the current state of EHR use to identify and track patients on the population level, advising and alerting patients, the role of telemedicine, and communication between providers and hospitals. The host is Dr. Sandy Falk, MD, Executive Editor-in-Chief, Clinical Practice Content at Elsevier.

• Episode 2: COVID-19 and Emergency Preparedness: Emergency Nursing Best Practices and Standards of Patient Care features Dr. Daniel Nadworny, DNP, RN, Director of Emergency and Patient Care Services at Beth Israel Deaconess Milton Hospital in Massachusetts. Dr. Nadworny, an expert on emergency crisis preparedness, discusses best practices and standards of care regarding isolations, triage, testing, the changing dynamic of PPE, and supporting patients, families and staff in the current crisis. The host is Dr. Julibeth Lauren, PhD, APRN, ACNS-BC, VP, Editor-in-Chief, Nursing and Allied Health Practice and Patient Education at Elsevier.

• Episode 1: SARS-CoV-2 Infection: Transmissibility, Pathophysiology and Treatment features Dr. Stanley Perlman, MD, PhD, Professor of Microbiology and Immunology and  Professor of Pediatrics at the University of Iowa Hospitals and Clinics. Dr. Perlman, who has studied coronaviruses for 4 decades, discusses mode of transmission of SARS-CoV-2 during asymptomatic and presymptomatic state, pathophysiology and disease progression, and touches on treatment options and vaccine development. He is interviewed by Margaret Trexler Hessen, MD, Director, Point of Care, for Elsevier.

• Stress, Adult
• Mindfulness-Based Stress Reduction
• Caring For Your Mental Health
• Healthy Eating
• Exercising to Stay Healthy, Adult
• Quality Sleep Information, Adult
• Healthy Relationship Information, Adult

• Depression Screening
• Perinatal Depression
• Coping with Depression
• How to Help Your Child Cope with Depression

• Depression in Adolescent Patients – Skill
• Depression in Adolescent Patients – Checklist

• Depression in Older Adult Patients – Skill
• Depression in Older Adult Patients – Checklist

• Suicide Assessment and Precautions – Skill
• Suicide Assessment and Precautions – Checklist

The turning point in Wuhan was reached about a month ago as all patients were admitted to be cared for by medical professionals. Among the institutions involved was Fangcang Shelter Hospitals. In this video, Dr. Chen Wang, President of the Chinese Academy of Medical Sciences and Peking Union Medical College and prominent expert on respiratory and critical care medicine — who proposed the Fangcang idea — shows how they operate on the front lines. Watch the video

临床综述：冠状病毒：新型冠状病毒（COVID-19）感染

临床综述是按照专科和疾病开发的的简明诊断治疗内容，帮助医生在临床工作中遇到问题时快速获取答案。

爱思唯尔专业临床内容编辑团队负责临床综述的开发，医学编辑进行最初的审查和修改，再由专科领域的专家进行同行审核。生产编辑负责确保风格、语法和标点符号的一致性。最终由主编审核通过后发布。

冠状病毒：新型冠状病毒（COVID-19）感染

3D4Medical 视频揭示新冠病毒从传播到入侵的全过程

3D4Medical的旗舰平台Complete Anatomy，是世界上最先进的解剖学平台。2019年注册用户超过了120万，有超过2500万的下载量，在160个国家的苹果、微软和谷歌的应用商店中被选为排名第一的医疗应用。

全球冠状病毒研究机构及研究人员分布图

基于爱思唯尔独有的全球最大的引文摘要数据库Scopus，聚焦冠状病毒疫情爆发及疫情控制研究。通过关键词检索分析，得到了自1996年至今已发表的22000多篇文献。在科研表现世界地图中，展示了全球表现突出的500多所科研机构（发文量超过10篇含）。其中，中国的科研机构有200多所。在这200多所中国科研机构中，中国大陆有150所，中国香港有15所，中国台湾有43所。

中国的科研机构按照发文量来看，香港大学以2888篇占据首位，远远高于排在第二位的香港中文大学（1766篇），中国农科院以1519篇论文紧随其后。其他占据发文前十的科研机构还包括中国医学科学院、国立台湾大学、中科院微生物所、中国疾病预防控制中心、北京大学、华中农业大学和首都医科大学。除此之外，复旦大学、中国军事医学科学院、清华大学、华南农业大学、中山大学、中科院武汉病毒所也分别发表了超过400篇的相关科研论文。

通过互动地图，不仅可以概览全球特别是中国的相关研究机构分布，同时还可以通过放大或缩小地图来聚焦某个研究机构，靶定该机构的研究人员来进行深入分析，以获取更多有价值的信息。

注：按照发文量进行排名，地图中蓝色标记的为全球冠状病毒研究排名前500的机构，红色标记为中国排名前200的机构，紫色标记为排在全球前500机构中的中国机构。

Because Google is not available in China and various other countries, we are offering a local version of the map, enabling you also to download the data.

• Top 500 global institutions
• Top 200 Chinese institutions

View the interactive version on Baidu

爱思唯尔数据揭示全球传染病研究趋势

在过去的二十年里，世界爆发了几次传染病疫情。爱思唯尔通过1996年至2018年的SciVal和Scopus数据，来分析学术成果、趋势主题和顶级研究机构的工作，进一步增进我们对传染病疫情及应对方式的了解。

下载《传染病疫情研究：见解和趋势》报告

中国的科研机构按照发文量来看，香港大学以2888篇占据首位，远远高于排在第二位的香港中文大学（1766篇），中国农科院以1519篇论文紧随其后。其他占据发文前十的科研机构还包括中国医学科学院、国立台湾大学、中科院微生物所、中国疾病预防控制中心、北京大学、华中农业大学和首都医科大学。除此之外，复旦大学、中国军事医学科学院、清华大学、华南农业大学、中山大学、中科院武汉病毒所也分别发表了超过400篇的相关科研论文。

通过互动地图，不仅可以概览全球特别是中国的相关研究机构分布，同时还可以通过放大或缩小地图来聚焦某个研究机构，靶定该机构的研究人员来进行深入分析，以获取更多有价值的信息。

注：按照发文量进行排名，地图中蓝色标记的为全球冠状病毒研究排名前500的机构，红色标记为中国排名前200的机构，紫色标记为排在全球前500机构中的中国机构。

Because Google is not available in China and various other countries, we are offering a local version of the map, enabling you also to download the data.

• Top 500 global institutions
• Top 200 Chinese institutions

View the interactive version on Baidu

Predictive models – a basis for decision-making

Mathematical models are used to forecast the course of the epidemic, ICU bed requirements and deaths and to predict the effect of measures like quarantine or social distancing. Despite extensive experience gained in China³ and a constantly growing amount of data, there are uncertainties about the properties of COVID-19. It therefore should be borne in mind that predictions concerning COVID-19 are still vague.

In epidemiology, it is well known that the course of a virus epidemic is influenced by the reproduction number of the virus (average number of secondary cases each case generates), R, the susceptibility of the population and the duration and severity of the disease.

There are two fundamental strategies dealing with an epidemic:

• Suppression aims at reducing the reproduction number to below 1, hence reversing epidemic growth. Suppression policies are best triggered early in an epidemic.
• Mitigation focuses on slowing the epidemic spread (reducing R, but not below 1). In this case population immunity builds up through the epidemic leading to an eventual decline in case numbers.

Models for Germany¹, the US and the UK² demonstrate that mitigation strategies for COVID-19 are not sufficient to prevent the national healthcare systems emergency surge capacity from  exceeding their limits over many times (Fig.1).

The authors of the studies therefore consider suppression the only viable strategy for their countries at the current time. Suppression requires social distancing of the entire population, isolation of cases and household quarantine of their family members.

Can population-based models be generalized?

Population specific conditions such as age distribution and geographical particularities must be considered if the models are to be transferred to other populations. The choice of interventions depends on the relative feasibility of their implementation and their likely effectiveness in different social contexts.

How long must interventions be maintained in force?

The drastic measures taken worldwide to contain the pandemic are unique. Public health interventions and their economic and social consequences have to be constantly evaluated and flexibly adapted. In the evaluation of interventions, a time lag of 2 to 3 weeks due to the delay between the infection and its consequences (e.g., ICU admission) must be taken into account.

Once interventions are eased, infections will begin to rise again. The extent of a second epidemic peak depends on the extent and the mode of withdrawal of interventions, and on the immunity of the population acquired during the epidemic. Reliable information on the immunity status of the population is therefore urgently needed.

References

1. An der Heiden M, Buchholz U: Modellierung von Beispielszenarien der SARS-CoV-2-Epidemie 2020 in Deutschland.
2. Ferguson NM, Laydon D, Nedjati-Gilani G, et al on behalf of the Imperial College COVID-19 Response Team: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand.
3. Novel Coronavirus Pneumonia Emergency Response Epidemiology Team: The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) — China, 2020, China CDC Weekly (retrieved March 27, 2020)

Ursula von Schenck, MD, is a board-certified pediatrician with over 25 years of clinical practice and research experience. Prior to joining Elsevier in 2019, she worked as a senior researcher in epidemiology and health monitoring at the national German public health institute, the Robert Koch Institute. She is leading the medical development of predictive and explanatory models based on anonymized health data for Elsevier 's Health Analytics team in Berlin.

The knights lowered their swords and turned towards him. “It is well known that this is the shortest crossing point of the river, but this disreputable man believes that a shorter route lies around that bend”, one of them explained, pointing along the river.

The Friar realized that he could intervene and prevent one of the knights dying needlessly. He said “But gentlemen, you could settle this dispute by simply measuring the two distances, to test which of you is correct.” He felt quite superior about this undeniable logic and tried not to look too smug about it.

He did not expect what happened next: the knights turned their swords towards him, felled him to the ground, and resumed their argument.

Tests are the unsung heroes of science, engineering and medicine. Even Einstein’s famous thought experiments had to be tested. Usually they assume a low profile, but occasionally they receive intense focus from the public, as now. Suddenly, governments and the media are intensely interested in tests for Covid-19 (SARS-CoV-2), for its immediate presence during infection and for evidence of immunity acquired through a previous infection. It is not just the WHO that comprehends the importance of testing, testing and more testing right now.

As a practical example, in the midst of the Covid-19 outbreak, a man in Taiwan had a cough for a week, and then woke up with terrible chest pains. He went to the hospital fearing he was infected with coronavirus but was diagnosed as just having a bad cold. According to this man, who reported his experience on Quora (the question and answer website), the Taiwanese National Health Command Centre tests and quarantines anyone with symptoms. If a person tests positive, they trace their previous interactions to identify potential victims so they can test them. Only 348 of the 23 million residents had tested positive as of April 2nd, with 5 deaths, despite a huge regular passage of workers to and from countries with high infection rates.

Insufficient Covid-19 virus tests are available, even in California, a hub of the biotechnology industry, exacerbating an international crisis resulting in many deaths and significant economic damage. Tests for antibody to the Covid-19 virus (anti-SARS-CoV-2) will also be required in great quantities. It is essential that both types of test be developed, manufactured and carried out with relevant skills and extensive experience. Organizational capability and financing are also required. Ideally, existing platforms should be used, as this avoids reinventing the wheel.

The fields of infectious disease management and blood donor screening have been revolutionized by tests for viruses and the antibodies raised against them, but there are risks and pitfalls that need to be recognized. The current effectiveness of tests for HIV and hepatitis, for example, took years to achieve and several generations of tests.

The clinical performance of diagnostic tests for infectious diseases can be evaluated in a number of ways. Two of the simplest measures to understand are clinical sensitivity and clinical specificity.

• Clinical sensitivity (or the detection rate) is the percentage of truly infected patients who are detected by the test. This is critical. If the sensitivity is 99%, 1% of infected patients will be missed i.e. are false negatives, and it cannot be assumed that every person who tests negative is free of the disease. In some cases, patients with symptoms of Covid-19 infection have been tested two or three times before a positive result is indicated. The sensitivity achieved by the test depends on the stage of the disease. Asymptomatic individuals who can infect others may be more difficult to detect in the test. The clinical sensitivity should therefore be estimated using a patient population mix appropriate to the intended application.

• Clinical specificity is the percentage of truly uninfected test subjects who are correctly identified as being free of the virus. If the specificity is 99%, 1% of uninfected subjects show up as being infected in the test (false positives). A clinical specificity of 99% may be acceptable if the test is being applied to symptomatic individuals, but if applied to the general population with a real infection rate of 0.1%, 91% of the infections reported would be false positives.

In theory, reverse transcription polymerase chain reaction (RT-PCR) tests should achieve high levels of clinical sensitivity and specificity when performed by experienced personnel, but they are complex and there are multiple factors to consider.

Sampling technique is important, to ensure that the infected materials are collected and preserved. This may involve training hundreds of sample collection personnel. Good sample processing is critical to avoid patient mix-ups between sampling and the communication of results. The avoidance of cross-contamination is absolutely crucial for RT-PCR tests as the RNA from minute traces of virus from another sample or air-borne droplet can be amplified causing a false-positive result. False negative results may be caused by contamination, e.g. from powder in gloves when they are changed over, or reagents that have lost potency or become contaminated.

Quality control samples should be run frequently to check assay performance.

The other type of test receiving much attention is testing for antibodies to the virus to establish prior infection and, hopefully, immunity. This is a simple concept to grasp, but the development of a reliable test, with high levels of clinical sensitivity and specificity, is demanding. It might be considered that false negatives, i.e. inability to detect Covid-19 antibodies when they are present, is less serious than false positives, which could lead a person to become exposed to infection, because they mistakenly think they are immune. But if a test consistently underestimates the number of immune patients it could mislead public health organizations that are tracking the outbreak and lead to policy errors. Detectable antibodies do not necessarily equate to full immunity from infection, and the duration of immunity after detection by this test will not be known for some time. Immunity can be short-lived or non-existent after some coronavirus infections. For SARS, immunity typically lasted about 2-3 years.

Soon after infection, the body generates IgM antibodies to fight the virus. For SARS, these were detectable in many patients after 1-2 weeks, but undetectable in some patients. Lagging behind this, IgG antibodies are produced, which provide immunity or partial immunity to future infections. Initial detectability of IgG antibodies in SARS patients varied from 1-14 weeks after infection. To achieve good clinical sensitivity the test must detect IgM and IgG antibodies (although sometimes separate tests are used) and detect a large number of different variants in the complementarity-determining regions of the subjects’ antibodies, as each immunized patient has a unique antibody population, with varying degrees of affinity for the virus. RNA viruses like SARS-CoV-2 have a higher rate of mutation than DNA viruses so the virus against which the patient antibodies are generated may be subtly different from one patient to another.

To achieve good specificity, antibody tests must not give a positive result due to the presence of antibodies against closely related coronaviruses. This is avoided by choosing clearly differentiated surface antigens or inactivated whole virus as the capture reagent(s) in the tests.

The development of COVID-19 tests involves the handling of positive, infectious samples and in some cases, live virus, so specialized containment facilities are required.

There are various versions of RT-PCR tests, but within each version, there is very little difference between tests for different viral RNA sequences. This is fundamentally different to the situation with tests for antibodies, which require significant skill and experience to develop, and very thorough validation. Antibody tests may be simpler to perform than RT-PCR, but tests for antibodies to new viral infections are much more challenging to develop.

Many of the chapters in my book, The Immunoassay Handbook, provide detailed background information. Immunoassays are tests for antigens, using antibodies as reagents, or tests for antibodies (as in this article) that use antigens as capture reagents. The chapter on antibodies also covers antibody technology relevant to some of the new treatments under development by the pharmaceutical industry. Although this book is directly applicable to antibody tests (or tests for virus using antibody-based reagents), the chapter on Clinical Concepts is also applicable to RT-PCR tests for virus.

Unlike the flu, where there is a defined “epidemic threshold” that can be modeled from years of data, for many recent high-profile outbreaks, no such formal experience or data exists to allow that kind of formal definition and determination. We rely on local, national and global public health organizations and leaders to formally declare an “epidemic” or a “pandemic”, but these determinations have a relative nature to them in many cases.

How severe is the epidemic?

The second complexity early in novel epidemics is the unknown severity or fatality rate of the disease. The severity of the infection clearly influences response and containment measures, but often the severity of a new variant or new pathogen is not known or is based on very few cases that may or may not be representative of a general population. Therefore, determining the average severity of disease or the fatality rate a population might experience is often not clearly known or at least carries a high degree of uncertainty and will take time to ascertain.

How are patients diagnosed and treated? Is there a vaccine?

The third complexity during an epidemic is variability around diagnosis, vaccination and treatment. In recent years, including the current COVID-19 outbreak, widespread testing, vaccination and treatment may not be available and must be developed and deployed over a vast area rapidly, creating both a scientific and logistical challenge. For other outbreaks, widespread testing or treatment may already exist. Moreover, variability of diagnosis, vaccination and treatment interact the prior complexities. For example, delays in diagnosis will lead to incomplete or delayed assessment of the spread (impacting complexity #1) and assessment of its lethality (complexity #2).

How do we respond to epidemics?

Finally, the response to epidemics is not centralized. Consensus emerges as the event grows. Responses occur at the individual level, at the level of providers and healthcare systems and also at the level of local, state and national public health agencies - not to mention the involvement of industry and academic institutions. Delaying or canceling group activities has no hard and fast formula. People make the best decisions they can in the moment and generally try to err on the side of safety. Coordination efforts increase, but fundamentally, especially at early stages, the actions and responses occur at different levels and the responses and reactions to the epidemic themselves take on an epidemic-like quality, gradually coalescing around general guidelines.

As epidemics evolve, all of these factors and many more are interacting, and the entire web of humanity and human organizations are individually and collectively reacting to the situation around them. In the case of Ebola, a single case that occurred in a Texas Hospital lead to people avoiding going to that hospital for any reason, even if there was no direct risk to them, and turning the hospital into a virtual ghost town. (News, n.d.) In the midst of epidemics, people and organizations are taking cues from each other and taking cues from the data as it becomes available. The complexities of the multiple factors at play in rapidly moving large scale events make a honed and practiced general preparedness process essential. That underlying preparedness framework is what allows solutions and consensus emerge. But when so many factors are moving at such a rapid pace, the “perfect response” seems elusive -- in part because the definition of success would be to never have suffering or mortality from an epidemic to begin with. The potential number of failure points are numerous, and success can often only be assessed in retrospect. And hence, the final complexity.

Bibliography

• News, A. (n.d.). Dallas Hospital 'Feels Like a Ghost Town' in Wake of Ebola Cases. Retrieved March 16, 2020 from http://abcnews.go.com/Health/video/dallas-hospital-feels-like-a-ghost-town-in-wake-of-ebola-cases-26275671