The history of science and medicine in the context of COVID-19

This spotlight issue encourages reflection on the current COVID-19 pandemic, not simply through comparisons with previous epidemics, but also by illustrating that epidemics deserve study within their broader cultural, political, scientific, and geographic contexts. Epidemics are not solely a function of pathogens; they are also a function of how society is structured, how political power is wielded in the name of public health, how quantitative data is collected, how diseases are categorised and modelled, and how histories of disease are narrated. Each of these activities has its own history. As historians of science and medicine have long pointed out, even the most basic methodologies that underpin scientific research—observation, trust in numbers, the use of models, even the experimental method itself—have a history. They should not be taken as a given, but understood as processes, or even strategies, that were negotiated, argued for and against, and developed within particular historical contexts and explanatory schemes. Knowing the history of something—whether of numbers, narratives, or disease—enables us to see a broader range of trajectories available to us. These varied histories also remind us that we are currently in the midst of a chaotic drama of uncertainty, within our own unstable and unfolding narrative.     

Layers of epidemy: Present pasts during the first weeks of COVID-19 in western Kenya

The epidemic of COVID-19 appears to be reshaping the world, separating before and after, present and past. Its perceived novelty raises the question of what role the past might play in the present epidemic and in responses to it. Taking the view that the past has not passed, but is present in is material and immaterial remains, and continuously emerging from these, we argue that it should not be studied as closed narration but through the array of its traces, which constitute the texture of the present. To that end, and building on long-term ethnographic research on past and present epidemics in western Kenya, we assemble here some preliminary observations on the first weeks of COVID-19 in Kenya. We explore how the acute epidemic crisis currently unfolding is intertwined with layered fragments of earlier epidemic events, attending to material infrastructures, institutional practices, and ritual responses, to the presence of virally loaded bodies, pharmaceuticals, and their residues, and to the resurgence of often painful memories and emotions. People in this region have experienced a long century of epidemics and anti-epidemic measures of varying duration and intensity, from colonial and postcolonial sleeping sickness and smallpox to HIV/AIDS and more recently cancer, alongside actual or anticipated outbreaks of cholera and Ebola. This local perception of one long epidemic qualifies the notion of a radical temporal break that the COVID-19 pandemic is often associated with in European conversations.     

Nanoparticle approaches against SARS-CoV-2 infection

Coronavirus disease 2019 (COVID-19), caused by the highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become the worst pandemic disease of the current millennium. To address this crisis, therapeutic nanoparticles, including inorganic nanoparticles, lipid nanoparticles, polymeric nanoparticles, virus-like nanoparticles, and cell membrane-coated nanoparticles, have all offered compelling antiviral strategies. This article reviews these strategies in three categories: (1) nanoparticle-enabled detection of SARS-CoV-2, (2) nanoparticle-based treatment for COVID-19, and (3) nanoparticle vaccines against SARS-CoV-2. We discuss how nanoparticles are tailor-made to biointerface with the host and the virus in each category. For each nanoparticle design, we highlight its structure–function relationship that enables effective antiviral activity. Overall, nanoparticles bring numerous new opportunities to improve our response to the current COVID-19 pandemic and enhance our preparedness for future viral outbreaks.     

A simple criterion to design optimal non-pharmaceutical interventions for mitigating epidemic outbreaks

For mitigating the COVID-19 pandemic, much emphasis is made on implementing non-pharmaceutical interventions to keep the reproduction number below one. However, using that objective ignores that some of these interventions, like bans of public events or lockdowns, must be transitory and as short as possible because of their significant economic and societal costs. Here, we derive a simple and mathematically rigorous criterion for designing optimal transitory non-pharmaceutical interventions for mitigating epidemic outbreaks. We find that reducing the reproduction number below one is sufficient but not necessary. Instead, our criterion prescribes the required reduction in the reproduction number according to the desired maximum of disease prevalence and the maximum decrease of disease transmission that the interventions can achieve. We study the implications of our theoretical results for designing non-pharmaceutical interventions in 16 cities and regions during the COVID-19 pandemic. In particular, we estimate the minimal reduction of each region’s contact rate necessary to control the epidemic optimally. Our results contribute to establishing a rigorous methodology to design optimal non-pharmaceutical intervention policies for mitigating epidemic outbreaks.     

How epidemics end

As COVID‐19 drags on and new vaccines promise widespread immunity, the world's attention has turned to predicting how the present pandemic will end. How do societies know when an epidemic is over and normal life can resume? What criteria and markers indicate such an end? Who has the insight, authority, and credibility to decipher these signs? Detailed research on past epidemics has demonstrated that they do not end suddenly; indeed, only rarely do the diseases in question actually end. This article examines the ways in which scholars have identified and described the end stages of previous epidemics, pointing out that significantly less attention has been paid to these periods than to origins and climaxes. Analysis of the ends of epidemics illustrates that epidemics are as much social, political, and economic events as they are biological; the “end,” therefore, is as much a process of social and political negotiation as it is biomedical. Equally important, epidemics end at different times for different groups, both within one society and across regions. Multidisciplinary research into how epidemics end reveals how the end of an epidemic shifts according to perspective, whether temporal, geographic, or methodological. A multidisciplinary analysis of how epidemics end suggests that epidemics should therefore be framed not as linear narratives—from outbreak to intervention to termination—but within cycles of disease and with a multiplicity of endings.     

On the Detection of COVID-19 from Chest X-Ray Images Using CNN-Based Transfer Learning

Coronavirus disease (COVID-19) is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death. There has already been some research in dealing with coronavirus using machine learning algorithms, but few have presented a truly comprehensive view. In this research, we show how convolutional neural network (CNN) can be useful to detect COVID-19 using chest X-ray images. We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19. In this regard, we evaluate performance of five different pre-trained models with fine-tuning the weights from some of the top layers. We also develop an ensemble model where the predictions from all chosen pre-trained models are combined to generate a single output. The models are evaluated through 5-fold cross validation using two publicly available data repositories containing healthy and infected (both COVID-19 and other pneumonia) chest X-ray images. We also leverage two different visualization techniques to observe how efficiently the models extract important features related to the detection of COVID- 19 patients. The models show high degree of accuracy, precision, and sensitivity. We believe that the models will aid medical professionals with improved and faster patient screening and pave a way to further COVID-19 research.     

Resilience strategies for sustainable supply chains under budget constraints in the post COVID-19 era

The COVID-19 outbreak has caused uncertainty risk surges, increased sustainable supply chain vulnerabilities, and challenges to sustainable supply chain resilience (SSCR) management. Therefore, improving SSCR is necessary to alleviate vulnerabilities, and SSCR management must generate large capital investments. However, the economic downturn brought about by the COVID-19 epidemic has made some companies have limited budgets that can be used to improve SSCR. Therefore, the design of resilience solutions needs to fully consider the constraints of budgetary costs. Most of the existing related literature only discusses optimal resilience solutions under certain cost constraints, so such resilience solutions cannot be applied to most enterprises. In this study, we set the cost constraint as a variable quantity, using resilience efficiency and customer satisfaction as indicators, to determine the changing laws of optimal resilience strategies when cost constraints change. These rules can be applied to enterprises with different budgeted costs. Our findings suggest that companies should prioritize sacrificing resilience measures (RMs) related to adaptive capacity when budget costs gradually decline, and RMs related to absorptive capacity are indispensable at all budget levels. Furthermore, the pursuit of environmental and social sustainability cannot be abandoned, no matter how limited the flexible budget may be.     

How scientific research reacts to international public health emergencies: a global analysis of response patterns

As of the middle of April 2020, the unprecedented COVID-19 pandemic has claimed more than 137,000 lives (https://coronavirus.jhu.edu/map.html). Because of its extremely fast spreading, the attention of the global scientific community is now focusing on slowing down, containing and finally stopping the spread of this disease. This requires the concerted action of researchers and practitioners of many related fields, raising, as always in such situations the question, of what kind of research has to be conducted, what are the priorities, how has research to be coordinated and who needs to be involved. In other words, what are the characteristics of the response of the global research community on the challenge? In the present paper, we attempt to characterise, quantify and measure the response of academia to international public health emergencies in a comparative bibliometric study of multiple outbreaks. In addition, we provide a preliminary review of the global research effort regarding the defeat of the COVID-19 pandemic. From our analysis of six infectious disease outbreaks since 2000, including COVID-19, we find that academia always responded quickly to public health emergencies with a sharp increase in the number of publications immediately following the declaration of an outbreak by the WHO. In general, countries/regions place emphasis on epidemics in their own region, but Europe and North America are also concerned with outbreaks in other, developed and less developed areas through conducting intensive collaborative research with the core countries/regions of the outbreak, such as in the case of Ebola in Africa. Researches in the fields of virology, infectious diseases and immunology are the most active, and we identified two characteristic patterns in global science distinguishing research in Europe and America that is more focused on public health from that conducted in China and Japan with more emphasis on biomedical research and clinical pharmacy, respectively. Universities contribute slightly less than half to the global research output, and the vast majority of research funding originates from the public sector. Our findings on how academia responds to emergencies could be beneficial to decision-makers in research and health policy in creating and adjusting anti-epidemic/-pandemic strategies.

     

What is a proof?

To those brought up in a logic-based tradition there seems to be a simple and clear definition of proof. But this is largely a twentieth century invention; many earlier proofs had a different nature. We will look particularly at the faulty proof of Euler's Theorem and Lakatos' rational reconstruction of the history of this proof. We will ask: how is it possible for the errors in a faulty proof to remain undetected for several years-even when counter-examples to it are known? How is it possible to have a proof about concepts that are only partially defined? And can we give a logic-based account of such phenomena? We introduce the concept of schematic proofs and argue that they offer a possible cognitive model for the human construction of proofs in mathematics. In particular, we show how they can account for persistent errors in proofs.     

Does online technology provide sustainable HE or aggravate diploma disease? Evidence from Bangladesh—a comparison of conditions before and during COVID-19

Higher education (HE) serves to produce well trained and job-ready graduates. Despite this belief, whether HE produces certificates/qualifications or job-ready graduates—this debate remains unsettled. To date, the current COVID-19 pandemic which erupted in late 2019 continues to create much economic, social and political dislocation throughout the world. Consequently, one outcome for HE during this crisis is the much greater dependence on online/digital technology to deliver courses and programs. Although it is not considered to be a complete substitute, critics argue that education delivered through online technology has expended the ‘diploma disease’ crisis and subsequently may pose a threat to the viability of producing HE graduates. It has been suggested that this can result in significant long-term problems that may be impossible to recover from. This study was based on a ‘empirical survey’ where the sample incorporated 240 people in two groups (before and during COVID-19, for a total of 120 in each), and the objective was to examine academic and job-readiness of graduates in greater depth. Findings demonstrate that pre-pandemic students scored poorly academically compared to their post-pandemic classmates. Pre-pandemic graduates, on the other hand, had higher work readiness scores in terms of both aptitude and practical aspects. Furthermore, both groups outperformed their job-readiness scores in terms of learning performance. This raises the question: is it the job of higher education to promote sustainable production of graduates or deliver certificates and degrees? COVID-19 appears to have been utilised by the HE system as an excuse to exacerbate the “diploma disease crisis,” a scenario that must be resolved by developing a proper policy framework that allows HE to play the necessary role in an emergency. Meaningful measures should be taken so that online technology is employed properly in this situation.     

Emerging diseases,re-emerging histories

The notion of “emerging infectious diseases” (EID) as a category of global health concerns was created in the 1990s to acknowledge that, although public health interventions, vaccines, and antibiotics since the late 19th century had given wealthier parts of the world control over most infectious diseases, the experience of Ebola and HIV/AIDS showed that new human diseases could still arise. “Emerging diseases” have clear histories, and the task the field of EID set itself has been to make those histories as short as possible—to catch emerging diseases as close to their origin as possible and snuff them out before they can become pandemics. In contrast, many of the known infectious diseases, those that were allegedly “conquered” by the rise of biomedicine in the late 19th and early 20th centuries and which (aside from smallpox) nonetheless still persist today, were assumed to have existed “since time immemorial.” Their histories had no specific time-depth, no documentable origins, and few discernible narrative arcs. The advent of a new field of research, palaeogenetics, has made possible an evolutionary perspective on pathogenic microorganisms, bringing hitherto unimaginable specificity to their documentable histories. Many origin stories, and nearly all globalization stories, fall within periods that historians and archaeologists study intensively. I argue that the diseases that have proved best suited to global expansion since the advent of the Holocene—those that became pandemics, like COVID-19—are those that have best exploited humans' global networks and behaviors in a given age. This recognition, in turn, gives the fields of both global health and history of medicine a new agenda.     

Science,demons, and gods in the battle against the COVID-19 epidemic

This spotlight article reflects on President Xi Jinping's handling of the COVID-19 epidemic and evaluates its specificities by making a brief incursion into the history of Chinese official responses to epidemics. This analysis shows that Xi Jinping's response to the COVID-19 epidemic differs from official responses to the 2003 SARS epidemic and the cerebrospinal meningitis epidemic of 1966–1967, and is used to assert his legitimacy on both the local and the international stage. By sharing data, even if it was not as accurate as claimed, Xi Jinping has presented himself as a trustworthy international partner, leading a country that is at the forefront of scientific research, capable of vigorously implementing epidemic preparedness measures, and destined to become a major player in global health. On the Chinese stage, he showed that the central government has regained control over local public health organizations and that public health is once again a key government priority. As part of his response, Xi Jinping also honored gods of the Chinese pantheon, in a seeming contradiction with communism and science. I argue that, by combining the most advanced technology with a religious heritage, Xi Jinping is skillfully creating an image of himself not only as powerful and modern, but also as a leader undeniably rooted in Chinese tradition.     

Overcoming the limitations of COVID-19 diagnostics with nanostructures,nucleic acid engineering,and additive manufacturing

The COVID-19 pandemic revealed fundamental limitations in the current model for infectious disease diagnosis and serology, based upon complex assay workflows, laboratory-based instrumentation, and expensive materials for managing samples and reagents. The lengthy time delays required to obtain test results, the high cost of gold-standard PCR tests, and poor sensitivity of rapid point-of-care tests contributed directly to society’s inability to efficiently identify COVID-19-positive individuals for quarantine, which in turn continues to impact return to normal activities throughout the economy. Over the past year, enormous resources have been invested to develop more effective rapid tests and laboratory tests with greater throughput, yet the vast majority of engineering and chemistry approaches are merely incremental improvements to existing methods for nucleic acid amplification, lateral flow test strips, and enzymatic amplification assays for protein-based biomarkers. Meanwhile, widespread commercial availability of new test kits continues to be hampered by the cost and time required to develop single-use disposable microfluidic plastic cartridges manufactured by injection molding. Through development of novel technologies for sensitive, selective, rapid, and robust viral detection and more efficient approaches for scalable manufacturing of microfluidic devices, we can be much better prepared for future management of infectious pathogen outbreaks. Here, we describe how photonic metamaterials, graphene nanomaterials, designer DNA nanostructures, and polymers amenable to scalable additive manufacturing are being applied towards overcoming the fundamental limitations of currently dominant COVID-19 diagnostic approaches. In this paper, we review how several distinct classes of nanomaterials and nanochemistry enable simple assay workflows, high sensitivity, inexpensive instrumentation, point-of-care sample-to-answer virus diagnosis, and rapidly scaled manufacturing.     

A Scenario-Based Evaluation of COVID-19-Related Essential Clinical Resource Demands in China

The coronavirus disease 2019 (COVID-19) pandemic is a global crisis, and medical systems in many countries are overwhelmed with supply shortages and increasing demands to treat patients due to the surge in cases and severe illnesses. This study aimed to assess COVID-19-related essential clinical resource demands in China, based on different scenarios involving COVID-19 spreads and interventions. We used a susceptible–exposed–infectious–hospitalized/isolated–removed (SEIHR) transmission dynamics model to estimate the number of COVID-19 infections and hospitalizations with corresponding essential healthcare resources needed. We found that, under strict non-pharmaceutical interventions (NPIs) or mass vaccination of the population, China would be able to contain community transmission and local outbreaks rapidly. However, under scenarios involving a low intensity of implemented NPIs and a small proportion of the population vaccinated, the use of a peacetime–wartime transition model would be needed for medical source stockpiles and preparations to ensure a normal functioning healthcare system. The implementation of COVID-19 vaccines and NPIs in different periods can influence the transmission of COVID-19 and subsequently affect the demand for clinical diagnosis and treatment. An increased proportion of asymptomatic infections in simulations will not reduce the demand for medical resources; however, attention must be paid to the increasing difficulty in containing COVID-19 transmission due to asymptomatic cases. This study provides evidence for emergency preparations and the adjustment of prevention and control strategies during the COVID-19 pandemic. It also provides guidance for essential healthcare investment and resource allocation.     

Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients’ data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy. Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results. In this paper, we used SqueezNet with a modified output layer to classify X-ray images into three groups: COVID-19, normal, and pneumonia. In this study, we propose a deep learning method with enhance the features of X-ray images collected from Kaggle, Figshare to distinguish between COVID-19, Normal, and Pneumonia infection. In this regard, several techniques were used on the selected image samples which are Unsharp filter, Histogram equal, and Complement image to produce another view of the dataset. The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type (COVID-19, Normal and Pneumonia). In the first scenario, the model has been tested without any enhancement on the datasets. It achieved an accuracy of 91%. But, in the second scenario, the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%. The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images. A comparison of the outcomes demonstrated the effectiveness of our DL method for classifying COVID-19 based on enhanced X-ray images.     

From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes

This paper examines the effects of online campaigns celebrating frontline workers on COVID-19 outcomes regarding new cases, deaths, and vaccinations, using the United Kingdom as a case study. We implement text and sentiment analysis on Twitter data and feed the result into random regression forests and cointegration analysis. Our combined machine learning and econometric approach shows very weak effects of both the volume and the sentiment of Twitter discussions on new cases, deaths, and vaccinations. On the other hand, established relationships (such as between stringency measures and cases/deaths and between vaccinations and deaths) are confirmed. On the contrary, we find adverse lagged effects from negative sentiment to vaccinations and from new cases to negative sentiment posts. As we assess the knowledge acquired from the COVID-19 crisis, our findings can be used by policy makers, particularly in public health, and prepare for the next pandemic.     

“设计之计”——自然灾害危机下的设计

新型冠状病毒肺炎疫情的爆发让人们再次意识到自然灾害的威胁,同时也开始思考如何利用设计抓住机遇,从而充分发挥其力量应对灾害。面对危机,设计该怎么做?本文以问题为导向,以分析案例为方法,通过不同视角来阐释在灾害危机发生前、发生时、发生后三个时间节点中设计应该如何为之。     

Fuzzy Based Decision Making Approach for Evaluating the Severity of COVID-19 Pandemic in Cities of Kingdom of Saudi Arabia

The World Health Organization declared COVID-19 a pandemic on March 11, 2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives. COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise. The information discharged by the WHO till June 15, 2020 reports 8,063,990 cases of COVID-19. As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug, the nations are relentlessly working at the most ideal preventive systems to contain the infection. The Kingdom of Saudi Arabia (KSA) is additionally combating with the COVID-19 danger as the cases announced till June 15, 2020 reached the count of 132,048 with 1,011 deaths. According to the report released by the KSA on June 14, 2020, more than 4,000 cases of COVID-19 pandemic had been registered in the country. Tending to the impending requirement for successful preventive instruments to stem the fatalities caused by the disease, our examination expects to assess the severity of COVID-19 pandemic in cities of KSA. In addition, computational model for evaluating the severity of COVID-19 with the perspective of social influence factor is necessary for controlling the disease. Furthermore, a quantitative evaluation of severity associated with specific regions and cities of KSA would be a more effective reference for the healthcare sector in Saudi Arabia. Further, this paper has taken the Fuzzy Analytic Hierarchy Process (AHP) technique for quantitatively assessing the severity of COVID-19 pandemic in cities of KSA. The discoveries and the proposed structure would be a practical, expeditious and exceptionally precise evaluation system for assessing the severity of the pandemic in the cities of KSA. Hence these urban zones clearly emerge as the COVID-19 hotspots. The cities require suggestive measures of health organizations that must be introduced on a war footing basis to counter the pandemic. The analysis tabulated in our study will assist in mapping the rules and building a systematic structure that is immediate need in the cities with high severity levels due to the pandemic.     

Infection,inflammation and intervention: mechanistic modelling of epithelial cells in COVID-19

While the pathological mechanisms in COVID-19 illness are still poorly understood, it is increasingly clear that high levels of pro-inflammatory mediators play a major role in clinical deterioration in patients with severe disease. Current evidence points to a hyperinflammatory state as the driver of respiratory compromise in severe COVID-19 disease, with a clinical trajectory resembling acute respiratory distress syndrome, but how this ‘runaway train’ inflammatory response emerges and is maintained is not known. Here, we present the first mathematical model of lung hyperinflammation due to SARS-CoV-2 infection. This model is based on a network of purported mechanistic and physiological pathways linking together five distinct biochemical species involved in the inflammatory response. Simulations of our model give rise to distinct qualitative classes of COVID-19 patients: (i) individuals who naturally clear the virus, (ii) asymptomatic carriers and (iii–v) individuals who develop a case of mild, moderate, or severe illness. These findings, supported by a comprehensive sensitivity analysis, point to potential therapeutic interventions to prevent the emergence of hyperinflammation. Specifically, we suggest that early intervention with a locally acting anti-inflammatory agent (such as inhaled corticosteroids) may effectively blockade the pathological hyperinflammatory reaction as it emerges.