How COVID-19 is Accelerating the Digital Revolution: Challenges and Opportunities

This book explores how digital technologies have proved to be a useful and necessary tool to help ensure that local and regional governments on the frontline of the emergency can continue to provide essential public services during the COVID-19 crisis. Indeed, as the demand for digital technologies grows, local and regional governments are increasingly committed to improving the lives of their citizens under the principles of privacy, freedom of expression and democracy. The Digital Revolution began between the late 1950s and 1970s and represents the evolution of technology from the mechanical and analog to the digital. The advent of digital technology has also changed how humans communicate today using computers, smartphones and the internet. Further, the digital revolution has made a tremendous wealth of information accessible to virtually everyone. In turn, the book focuses on key challenges for local and regional governments concerning digital technologies during this crisis, e.g. the balance between privacy and security, the digital divide, and accessibility. Privacy is a challenge in the mitigation of COVID-19, as governments rely on digital technologies like contact-tracking apps and big data to help trace peoples patterns and movements. While these methods are controversial and may infringe on rights to privacy, they also appear to be effective measures for rapidly controlling and limiting the spread of the virus. Next, the book discusses the 10 technology trends that can help build a resilient society, as well as their effects on how we do business, how we work, how we produce goods, how we learn, how we seek medical services and how we entertain ourselves. Lastly, the book addresses a range of diversified technologies, e.g. Online Shopping and Robot Deliveries, Digital and Contactless Payments, Remote Work, Distance Learning, Telehealth, Online Entertainment, Supply Chain 4.0, 3D Printing, Robotics and Drones, 5G, and Information and Communications Technology (ICT). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation;2) object detection;and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework's usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.

IOT-Based Telemedicine Network Design Implementation of a Smart Health Monitoring System in COVID-19

The role of telemedicine and big data analysis in the COVID-19 pandemic is highlighted in this chapter along with some real-life applications. The IoT and modern healthcare background exhibit the improvement of technology in the health sector with continuous research and innovation. The primary objectives of this discussion are to portray the impacts of telemedicine in the continuation of advanced healthcare approaches and help individuals to become familiar with modern healthcare. Along with expanding new research opportunities and extending telemedicine principles throughout the future medical sector, health services will become stronger and more easily accessible to everyone. © 2023 selection and editorial matter, Ben Othman Soufene, Chinmay Chakraborty, Faris. A. Almalki. All rights reserved.

An Intelligent Collaborative Image-Sensing System for Disease Detection

With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection. © 2001-2012 IEEE.

Performance Evaluation of RT-qPCR and RT-dPCR Platforms for the wastewater surveillance of SARS-CoV-2

Background. The COVID-19 pandemic is an ongoing global health emergency. Wastewater-based epidemiology is a valuable tool for supplementing clinical testing in identifying infected individuals early thus containing disease transmission. To assess early detection of COVID-19, a building-level wastewater-based surveillance pilot project was implemented within VHA. Here, we report the results from 2 methods of polymerase chain reaction (PCR) testing of 1073 wastewater samples from VHA CLCs (i.e., nursing homes). Methods. Daily (Monday-Friday) wastewater samples were collected (January 11, 2021, to July 2, 2021) at eight CLCs located across the US and shipped overnight for processing. The samples were heat inactivated by incubating samples in a 65+/-1degreeC heating circulating water bath for 90 minutes. The virus in the wastewater was concentrated using InnovaPrep concentrating pipette select, and RNA was isolated from the concentrate and subjected to reverse transcription quantitative PCR (RT-qPCR) and RT-digital PCR. If SARS-CoV-2 was detected in the wastewater within the prior 10 days of a virus-positive occupant, the wastewater positivity was regarded as an early warning. Results. Twenty-seven positives and 7 inconclusive results were reported by RT-qPCR during the surveillance. Among the 27, 15 wastewater positives qualified as early warning and 12 positives were not verified by occupant positivity. Digital PCR with a cutoff value of 0.25 copies/uL of RNA for defining positivity had 28 positives qualifying as early warnings, and 115 positives were not verified by occupant positivity (Figure 1). Conclusion. The overall viral loads of the wastewater samples were very low corresponding to the dip in cases seen in the US during the pilot period. Although sensitivity of digital PCR appears (based on 0.25 copies/uL of RNA for defining positivity) higher than that of RT-qPCR, there were more occurrences of unverified early warning that could impact precision. The cut-off selected for RT-digital PCR reported here is arbitrary and lacks industry consensus. More controlled studies are needed to determine sensitivity and precision as well as to standardize RT-digital PCR cutoffs to define positivity for routine use.

Evaluation of antibiotics utilization in an intensive and critical care unit of a tertiary care teaching hospital in West Bengal: An observational study

Background: Resistance to antibiotics is rising markedly. Factor which contributes to resistance is the rampant irrational use of antibiotics. The trend of prescribing antibiotics in intensive care units (ICUs) of eastern India is less explored. Aims and Objectives: The aim of the study was to describe and analyze the utilization of antibiotics as per the WHO/INRUD prescribing core indicators in an intensive and critical care unit (CCU) of a tertiary center in eastern India. Material(s) and Method(s): A prospective observational study was carried out on prescription pattern of antibiotics. Case records of patients with restricted antibiotic therapy were reviewed and evaluated using descriptive statistics. A total of 353 prescriptions were evaluated and analyzed. Result(s): Among the total 353 patients most common age group admitted was 41-60 years. Males were more in numbers. Myocardial infarction and post-operative complications were the most common cause of admission in ICU and CCU, respectively. Ceftriaxone (44%) and meropenem (37%) were the drugs used rampantly in ICU and CCU. More than 90% of prescriptions had injections and drugs were written in generic names. Almost 90% of patients in CCU had antibiotics in their prescription. Adverse events occurred in 14.7% of patients and thrombophlebitis was the most common adverse event occurred. Conclusion(s): The study has given us an overall impression of the antibiotics usage pattern in ICU and CCU of this teaching institution. The study has shown that antibiotics should be used judiciously in ICU and CCU. Policy can be made on the basis of the result of this study. Copyright © 2022 Pratap Chatterjee, et al.

Micronutrients-The crucial dietary elements in Covid-19 pandemic: A review

The mutation of the SARS-CoV-2 virus, which cause person-to-person transmission, is the pivotal reason for the pandemic outbreak in the year 2020. Infection symptoms include fever, dry cough, lethargy, severe pneumonia, respiratory distress syndrome, and death. COVID-19 induces a systemic inflammatory reaction that impairs the immune system, commonly known as cytokine release syndrome. Pro-inflammatory cytokines and chemokines are abundant in COVID-19 sufferers' bodies. COVID-19 has a disproportionate impact on the elderly, both directly and through several comorbidities associated with age. Nutrition is without hesitation, a crucial factor in maintaining good health. Some nutrients are essential for the immune system's health and function, exhibiting synergistic actions in critical immune response steps. Vitamin D, C, and Zinc stand out among these nutrients because they have immunomodulatory properties and help to maintain physical tissue barriers. Considering the viability of the virus, nutrients that boost the immunity henceforth the severity of viral infections declines with improved prognosis become important. As a result, the purpose of this review is to provide a complete outline of vitamins D, C, and zinc's involvement during the immune response towards infection, and to enlighten their commensal action of maintaining physical barriers including integument and mucous membrane. Appropriate vitamin D, C, and zinc consumption may represent a feasible pharmacological intervention during the COVID-19 pandemic due to the high surge in population interaction and the commencement of inflammation. Copyright © 2022, Indian Association of Biomedical Scientists. All rights reserved.

IMPACT OF COVID-19 ON MANUFACTURING INDUSTRIES IN INDIA: Networks and trade dependence

The Covid pandemic has resulted in an economic shock. However, not all sectors are affected uniformly. We study two channels on the supply side through which the shock is amplified differentially across industries. First, we look at the dependence on global value chain (GVC), measured as share of imports in total inputs, which measures an industry’s integration with the GVC. A disruption in GVC will amplify the shock more for a higher value of the import share in inputs. Second, we construct a network of industries based on inputs used and calculated a dependency score for each industry. Higher the dependency score, greater is the chance for disruption. We find that both these factors are correlated with output share of the industries, implying amplification and a significant impact. Using IIP data for the months post the onset of pandemic, we find that disruption is higher in industries with higher import share and higher dependency score. Finally, we create a vulnerability index using these two variables. Industries with higher VI score are likely to face greater challenges in the recovery process. © 2022 selection and editorial matter, Rajib Bhattacharyya, Ananya Ghosh Dastidar and Soumyen Sikdar;individual chapters, the contributors.

An Intelligent Collaborative Image-Sensing System for Disease Detection

With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, as well as the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices as well as cloud computing services, as well as basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, as well as generate multivariate data to provide just-in-time healthcare services. In this paper, we present a novel collaborative disease detection system based on IoMT as well as captured image data. The system can be based on intelligent agents, where each and every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared to baseline solutions for disease detection. IEEE

A Critical Study on Violation of a Decent Burial/Cremation Right of a Dead Person During COVID-19: Special Reference to India

This chapter broadly focuses on the rights of a dead person relating to his/her burial or cremation. Recently the ongoing pandemic has brought to the forefront many appalling incidences in India that have forced us to think that whether even a dead person has any right after death? In this chapter, the author is going to discuss about the law relating to the rights of a dead person, giving special attention to the right for a decent burial or cremation in the context of both Indian and international legal systems, as well as in such pandemic situations. This chapter further focuses on the issues of violation of a dead person’s right to a decent burial/cremation in India through various case studies and their psychologic impact on the society. This discussion will also shed light on the pros and cons of the laws and guidelines relating to dead body management during a pandemic as prescribed by the WHO and the Indian government for the benefit of the society as a whole. © 2022 Elsevier Inc. All rights reserved.

CHANGES IN U.S. SAFETY-NET HOSPITAL FINANCES IN THE FIRST YEAR OF THE COVID-19 PANDEMIC

BACKGROUND: The Covid-19 pandemic introduced myriad financial challenges for hospitals nationally. These challenges may have been particularly for midable for safety-net hospitals that disproportionately serve lowincome patients, given the baseline financial constraints of these hospitals and the burden of Covid-19-related illness among low-income patients. Understanding how safety-net hospitals have financially fared during the pandemic is critical to informing how policymakers can bolster these hospitals. METHODS: We conducted a national retrospective cohort study of safety-net hospitals, defined as hospitals with the largest shares of Medicaid patients, and non-safety-net hospitals. Using data from Medicare Cost Reports, 2015-2020, we examined differential changes across three measures of hospital finances (operating margins, total margins, share of uncompensated care), between safety-net and non-safety-net hospitals, before (pre-2020) and after the onset of the pandemic (post-2020). In main analyses, the sample was limited to hospitals with the most complete reporting of 2020 Cost Reports;in sensitivity analyses, all hospitals were included regardless of the completeness of 2020 reporting. We performed additional sensitivity analyses using an alternate definition for “safety-net hospitals” based on the disproportionate share hospital index. RESULTS: The sample included 230 safety-net hospitals and 1,858 nonsafety-net hospitals. Operating margins at safety-net hospitals differentially declined since the beginning of 2020 compared to non-safety-net hospitals (3.3% pre-2020 vs. 3.8% post-2020 among safety-net hospitals;3.2% pre2020 vs. 5.3% post-2020 among non-safety-net hospitals;-1.2 percentage point change, p=0.02). Similar patterns were evident for total margins (5.0% pre-2020 vs. 6.2% post-2020 among safety-net hospitals;4.7% pre-2020 vs. 7.0% post-2020 among non-safety-net hospitals;-0.9 percentage point change, p=0.07). There were also non-significant differential increases in uncompensated care at safety-net hospitals compared to non-safety-net hospitals since 2020 (6.8% pre-2020 vs. 7.4% post-2020 among safety-net hospitals;6.9% pre-2020 vs. 7.1% post-2020 among non-safety-net hospitals;0.4 percentage point change, p=0.08). Results were consistent when using a different definition for “safety-net hospitals”, as well as when the sample included hospitals with less complete financial reporting in 2020 (707 safety-net hospitals vs. 4,205 non-safety-net hospitals). CONCLUSIONS: Safety-net hospitals experienced differential declines in operating margins compared to non-safety-net hospitals since the onset of the pandemic in 2020. These hospitals provide essential care for low-income patients but have faced longstanding financial challenges that were only exacerbated by the pandemic. Understanding drivers of these declines will be critical for the viability of these hospitals as well for policymakers seeking to bolster safety-net health care delivery in the pandemic era.

Evaluation of the Dynamics of Large Scale COVID-19 Related Literature through Bibliometric Analysis from a Mathematical Standpoint

This study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms. The PubMed, ScienceDirect, and ResearchGate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed "all articles" and ScienceDirect published articles. Analyzed databases were (1A) PubMed (01/12/2019-12/06/2020) "all_articles" (16) PubMed Review articles) and (1C) PubMed Clinical Trials (2) ScienceDirect all publications (01/12/2019- 25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020 - 30/4/2020). Total publications in the observation period for PubMed, ScienceDirect, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, ScienceDirect and RG were 70.0 +/- 128.6, 77.6 +/- 125.3 and 255.6 +/- 205.8 respectively. PubMed shows an avalanche in the number of publications around May 10, the number of publications jumped from 6.0 +/- 8.4/day to 282.5 +/- 110.3/ day. The average doubling time for PubMed, ScienceDirect, and RG was 10.3 +/- 4 days, 20.6 days, and 2.3 +/- 2.0 days respectively. The average number of publications per author for PubMed, ScienceDirect, and RG was 1.2 +/- 1.4, 1.3 +/- 0.9, and 1.1 +/- 0.4 respectively. Subgroup analysis, PubMed review articles mean review <0 vertical bar 17 +/- 17 vertical bar 77> days: and reducing at a rate of -0.21 days (count)/day. The number of publications related to the COVID-19 until now is huge and growing very fast with time. It is essential to rationalize and limit the publications.

Covid Waste Management Using IoT: A Smart Framework

The influence of Covid-19 is reshaping our daily life. Along with protecting lives, management of contaminated covid wastes is very essential to reduce environmental and human health risks. Inappropriate management of covid wastes can cause serious possibilities of disease transmission to health workers, waste pickers, patients, and the community in general through spreading of Corona virus. Poor management of these infected wastes has created huge problems in healthcare waste handling as the amount of waste generated due to pandemic is excessively large. In this paper, we have presented an Internet of Things(IoT)-based smart framework for covid waste management. We have proposed two types of smart covid waste bins here, one for collecting covid wastes for home isolated patients and other for covid medical wastes, generated from hospitals/nursing homes, etc. These intelligent bins can capture waste information automatically by attached sensors, and the collected data is sent wirelessly to remote municipality server for further analysis. Municipality officials can take decisions such as number of bins to be deployed, at which time intervals wastes are to be cleared, etc., based on those information. The proposed framework minimizes the human interaction with the covid waste and restricts the spreading of virus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

DietSN: A Body Sensor Network for Automatic Dietary Monitoring System

Now a days, E-health or electronic health monitoring system is one of the major applications of wireless body area network (WBAN). Body area network or body sensor network (BSN) is an evolving technology in computer sphere and performs exceedingly effective responsibility in the civilization, primarily in health service industries. BAN eases in examining crucial symptoms of a patient/elderly and can monitor his/her activities in routine life to deliver him/her a precise care. Owing to the epidemic of COVID-19, we are struggling through an unexpected adverse pandemic situation and now healthy lifestyle and disease preclusion are a socio-economic issue. Therefore, it is required to stay healthy and take balanced diet which can help us to gain immunity and protect us from severe ailments. In this paper, we are proposing a body area network for an automatic dietary monitoring system that can gather food intake information through image, audio, accelerometer sensors, and by analyzing these data, the system can measure the food type/volume, nutritional benefit of consumed food, and also the eating behavior of a person. The system is low-cost, scalable, and energy aware. We have implemented a prototype of our proposed BSN, named ‘DietSN.’ © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Change in practice in cervical cancer screening during covid 19 pandemic

Objective: This study aims to analyse changes in practice for cervical cancer screening in rural districts of West Bengal before and after the COVID-19 pandemic led to disruption of preventive healthcare services. Design: Retrospective cross-sectional study. Method: Data from March 2019-March 2020 and April 2020-March 2021 was retrospectively analysed from women aged 30-59 years who underwent screening in rural districts of West Bengal as part of Integrated Programme on Non-Communicable Diseases Prevention project, which has been implemented at our institute since March 2017. After onset of nationwide lockdown due to the pandemic, screening services were withheld for 2 months. From June 2020, HPV testing kits (QIAGEN) were distributed and collected by our team of health workers, while maintaining all precautions. Women who tested positive underwent colposcopy with or without treatment (by thermal ablation or LEEP) at their nearby primary health centre by our clinicians. Results: From April 2019-March 2020, 6748 women were screened using either HPV DNA testing (Hybrid Capture 2) or VIA. Cervical samples for HC2 testing were collected from 5581 women, either by provider (57.28%, n = 3197) or self-sampling (42.71%, n = 2384). VIA was done in 1167 women, 25 were positive (2.14%). From the 200 women (3.58%) who were HC2 test positive, colposcopy was performed on 113 women (56.5%). Treatment was done in 87 (76.99%) women. Thermocoagulation was the most common method used (77.02%) followed by LEEP (21.8%). From June 2020-March 2021, 5875 women were screened using only self-sampling method for HPV testing, of which 268 were HC2 positive (4.56%), of whom 183 (68.28%) underwent colposcopy. Treatment was done in 149 women (81.4%), thermocoagulation being the most common (81.87%) procedure followed by LEEP (16.77%). Conclusions: HPV self-sampling can be promoted during COVID-19 pandemic as it curtails travel and reduces human contact.

Feasibility of self-sampling for HPV Dna detection in a community-based cervical cancer screening project

Objective: Paucity of resources and trained professionals makes it difficult to implement a cervical cancer screening program in India. This study aims to evaluate the feasibility of self-sampling for detection of human papillomavirus (HPV) DNA in a communitybased cervical cancer screening project. Methods: Women (30-60 years) were assigned to do self-sampling or get their samples collected by healthcare workers in outreach clinics. The samples were brought to the institute and were tested by hybrid capture2 (HC2) test for 13 high-risk HPV types. HC2 positive women were brought to the institute where they underwent colposcopy, biopsy and treatment by Thermal ablation, Cryotherapy or Loop Electrosurgical Excision Procedure (LEEP). A focussed group discussion was done with health workers involved with this project in the form of a questionnaire. Results: From May 2017 and December 2020, 15,311 women were recruited. Amongst them, 4916 (32.1%) had self-sampling and 10,395 (67.9%) had health-worker collected sample. The HC2 positivity rates in both groups were 269 (5.5%) and 652 (6.3%), which was not significantly different statistically (P = 0.06). The colposcopy rates and Cervical Intraepithelial Neoplasia (CIN) 2 and 3 detection rates were also similar. All women were comfortable with self-sampling with no sample inadequacy or wastage of collection kits. The health workers rated both procedures as acceptable. The advantage of selfsampling was that no examining table or light source was required and the screened women were less ''shy'' while sample collection. Conclusion: Self-sampling for HPV may increase participation in cervical cancer screening programs, especially in the COVID19 era.

Virtual learning approach to biological engineering courses in Uruguay during COVID-19

Purpose In times when digitized and blended learning paradigms are getting more profuse, the COVID-19 pandemic substantially changed the dynamics of this program, forcing all the courses to migrate to virtual modality. This study highlights the biological engineering courses at the University of the Republic (Universidad de la Republica) in Uruguay pertaining to the adaptations to virtual learning environments during the COVID-19 pandemic and analyzing its impact through the courses taught in the virtual setting. Design/methodology/approach Global education has seen a significant paradigm shift over the last few years, changing from a specialized approach to a broader transdisciplinary approach. Especially in life sciences, different fields of specializations have started to share a common space in the area of applied research and development. Based on this transdisciplinary approach, the Biological Engineering program was designed at the University of the Republic (Universidad de la Republica), Uruguay. Findings The new challenges posed by the virtual modality on the pedagogical areas like course design, teaching methodologies and evaluations and logistical aspects like laboratory-setting have sparked a considerable change in different aspects of the courses. However, despite the changes to virtual modality in this year, the student-performance showed an overall improvement compared to the last year. Originality/value With the changing direction of pedagogy and research in biological engineering across the world, it is quintessential to adapt university courses to the same, promoting an environment where the scientific and engineering disciplines merge and the learning methodologies lead to a dynamic and adaptive ubiquitous learning environment.

Specialized COVID-19 detection techniques with machine learning

COVID-19 has been declared as a pandemic in over 200 countries of the world.COVID-19 is an infectious disease that is primarily caused by severe acute respiratory syndrome coronavirus 2((SARS-CoV-2). According to the latest figures by the world health organization, the number of confirmed cases for the COVID-19 pandemic worldwide is more than 20 million worldwide and the number of fatalities reported is over 700,000. It has been found from several studies that medical imaging coupled with machine learning methods holds great promise in the detection and follow-up of the COVID-19 disease due to the enhanced accuracy in results of the experiments performed by the researchers. Machine Learning (ML)-based solutions can be used to simultaneously analyze multiple input computed tomography (CT) images of chest and lungs. A large number of papers have been published that show the application of machine learning methods in successful detection of the COVID-19 disease. Such applications demonstrate the suitability of feature prediction, identification of involved risks and therefore managing and intercepting the outbreak of such diseases. This paper describes some of the techniques in machine learning that can be used detection of COVID-19 disease. © 2021 Institute of Physics Publishing. All rights reserved.

Efficient and Privacy-Preserving Medical Research Support Platform Against COVID-19: A Blockchain-Based Approach

COVID-19 is a major global public health challenge and difficult to control in a short time completely. To prevent the COVID-19 epidemic from continuing to worsen, global scientific research institutions have actively carried out studies on COVID-19, thereby effectively improving the prevention, monitoring, tracking, control, and treatment of the epidemic. However, the COVID-19 electronic medical records (CEMRs) among hospitals worldwide are managed independently. With privacy consideration, CEMRs cannot be made public or shared, which is not conducive to in-depth and extensive research on COVID-19 by medical research institutions. In addition, even if new research results are developed, the disclosure and sharing process is slow. To address this issue, we propose a blockchain-based medical research support platform, which can provide efficient and privacy-preserving data sharing against COVID-19. First, hospitals and medical research institutions are treated as nodes on the alliance chain, so consensus and data sharing among the nodes is achieved. Then, COVID-19 patients, doctors, and researchers need to be authenticated in various institutes. Moreover, doctors and researchers need to be registered with the Fabric certificate authority. The CEMRs for COVID-19 patients uses the blockchain's pseudonym mechanism to protect privacy. After that, doctors upload CEMRs on the alliance chain, and researchers can obtain CEMRs from the alliance chain for research. Finally, the research results will be published on the blockchain for doctors to use. The experimental results show that the read and write performance and security performance on the alliance chain meet the requirements, which can promote the wide application of scientific research results against COVID-19.