scholarly journals Collaborating in the time of COVID-19: the scope and scale of innovative responses to a global pandemic (Preprint)

2020 ◽  
Author(s):  
Theresa Marie Bernardo ◽  
Kurtis Edward Sobkowich ◽  
Russell Othmer Forrest ◽  
Luke Stewart ◽  
Marcelo D'Agostino ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Small Businesses ◽  
Technology Policy ◽  
Vaccine Development ◽  
Nobel Laureate ◽  
Contact Tracing ◽  
Governmental Organizations ◽  
Data Propagation ◽  
Novel Coronavirus ◽  
Collaborative Efforts

UNSTRUCTURED Introduction: The emergence of COVID-19 spurred the formation of myriad teams to tackle every conceivable aspect of the virus and thwart its spread. Collaboration has become a constant theme throughout the 2019 novel coronavirus pandemic and has resulted in expedition of the scientific process (including vaccine development), rapid consolidation of global outbreak data and statistics, as well as experimentation with novel partnerships. Enabling these collaborative efforts is a state of global connectedness where data travels between countries in fractions of a second, allowing for partnerships and information sharing to occur virtually, with no need for physical proximity or even prior knowledge of your collaborators. The objective of this article is to document the evolution of these collaborative efforts, using illustrative examples collected by the authors throughout the pandemic and supplemented with publications from the JMIR COVID-19 Special Issue on coronavirus. Main Themes: Over 60 projects rooted in collaboration are categorized into five main themes: knowledge dissemination; data propagation; crowdsourcing; artificial intelligence; and hardware design and development. They highlight the numerous ways that citizens, industry professionals, researchers, and academics have come together globally to consolidate information and produce products geared towards combating the COVID-19 pandemic. With the overwhelming quantity of information, it can be challenging to gauge quality and detect misinformation, which is exacerbated by the inability to rapidly collect and share robust public health data. Initially, researchers and citizen scientists scrambled to pull together any accessible data. As global curated data sets started to emerge, numerous derivative works, such as visualizations or models, were developed that depended on the consistency of that data and which would fail when there were unanticipated changes. Crowdsourcing was used to collect and analyze data, aid in contact tracing, and to produce personal protective equipment (PPE) by sharing open designs for 3D printing. National and international consortia of entrepreneurs collaborated with researchers, including a Nobel Laureate, to create a ventilator that received rapid government approval and which was based on an open-source design. An equally impressive coalition of NGOs and governmental organizations led by the White House Office of Science and Technology Policy created a shared open resource of over 200,000 research publications about COVID-19 and subsequently challenged experts in artificial intelligence to answer 17 key questions, offering cash prizes for the best solutions. Conclusions: A thread of collaboration weaved throughout the pandemic response, which represents more than a series of random events. Thrust upon us, it will shape future efforts, pandemic or non-pandemic related. Novel partnerships, combining citizens, entrepreneurs, small businesses, corporations, academia, and governmental and non-governmental organizations will cross boundaries to create new processes, products and better solutions to consequential societal challenges.

scholarly journals Collaborating in the Time of COVID-19: The Scope and Scale of Innovative Responses to a Global Pandemic

10.2196/25935 ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e25935
Author(s):  
Theresa Bernardo ◽  
Kurtis Edward Sobkowich ◽  
Russell Othmer Forrest ◽  
Luke Silva Stewart ◽  
Marcelo D'Agostino ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Technology Policy ◽  
Vaccine Development ◽  
Contact Tracing ◽  
Quality Data ◽  
Data Sets ◽  
White House ◽  
Governmental Organizations ◽  
Global Pandemic ◽  
Data Propagation

The emergence of COVID-19 spurred the formation of myriad teams to tackle every conceivable aspect of the virus and thwart its spread. Enabled by global digital connectedness, collaboration has become a constant theme throughout the pandemic, resulting in the expedition of the scientific process (including vaccine development), rapid consolidation of global outbreak data and statistics, and experimentation with novel partnerships. To document the evolution of these collaborative efforts, the authors collected illustrative examples as the pandemic unfolded, supplemented with publications from the JMIR COVID-19 Special Issue. Over 60 projects rooted in collaboration are categorized into five main themes: knowledge dissemination, data propagation, crowdsourcing, artificial intelligence, and hardware design and development. They highlight the numerous ways that citizens, industry professionals, researchers, and academics have come together worldwide to consolidate information and produce products to combat the COVID-19 pandemic. Initially, researchers and citizen scientists scrambled to access quality data within an overwhelming quantity of information. As global curated data sets emerged, derivative works such as visualizations or models were developed that depended on consistent data and would fail when there were unanticipated changes. Crowdsourcing was used to collect and analyze data, aid in contact tracing, and produce personal protective equipment by sharing open designs for 3D printing. An international consortium of entrepreneurs and researchers created a ventilator based on an open-source design. A coalition of nongovernmental organizations and governmental organizations, led by the White House Office of Science and Technology Policy, created a shared open resource of over 200,000 research publications about COVID-19 and subsequently offered cash prizes for the best solutions to 17 key questions involving artificial intelligence. A thread of collaboration weaved throughout the pandemic response, which will shape future efforts. Novel partnerships will cross boundaries to create better processes, products, and solutions to consequential societal challenges.

scholarly journals Emergence of Novel Coronavirus 2019-nCoV: Need for Rapid Vaccine and Biologics Development

Pathogens ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 148 ◽  
Author(s):  
Balamurugan Shanmugaraj ◽  
Ashwini Malla ◽  
Waranyoo Phoolcharoen
Keyword(s):  
Vaccine Development ◽  
Plant Biotechnology ◽  
Respiratory Illness ◽  
The United States ◽  
Diagnostic Tools ◽  
Emerging Pathogen ◽  
Novel Coronavirus ◽  
Short Time ◽  
Collaborative Efforts ◽  
Severe Respiratory Illness

Novel Coronavirus (2019-nCoV) is an emerging pathogen that was first identified in Wuhan, China in late December 2019. This virus is responsible for the ongoing outbreak that causes severe respiratory illness and pneumonia-like infection in humans. Due to the increasing number of cases in China and outside China, the WHO declared coronavirus as a global health emergency. Nearly 35,000 cases were reported and at least 24 other countries or territories have reported coronavirus cases as early on as February. Inter-human transmission was reported in a few countries, including the United States. Neither an effective anti-viral nor a vaccine is currently available to treat this infection. As the virus is a newly emerging pathogen, many questions remain unanswered regarding the virus’s reservoirs, pathogenesis, transmissibility, and much more is unknown. The collaborative efforts of researchers are needed to fill the knowledge gaps about this new virus, to develop the proper diagnostic tools, and effective treatment to combat this infection. Recent advancements in plant biotechnology proved that plants have the ability to produce vaccines or biopharmaceuticals rapidly in a short time. In this review, the outbreak of 2019-nCoV in China, the need for rapid vaccine development, and the potential of a plant system for biopharmaceutical development are discussed.

scholarly journals Application of Artificial Intelligence to Address Issues Related to the COVID-19 Virus

2021 ◽  
pp. 247263032098381
Author(s):  
M. Senthilraja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Vaccine Development ◽  
De Novo ◽  
Biologically Active ◽  
Treatment Regimens ◽  
Synthesis Planning ◽  
Novel Coronavirus ◽  
Near Future

Artificial intelligence (AI) plays a major role in addressing novel coronavirus 2019 (COVID-19)-related issues and is also used in computer-aided synthesis planning (CASP). AI, including machine learning, is used by artificial neural networks such as deep neural networks and recurrent networks. AI has been used in activity predictions like physicochemical properties. Machine learning in de novo design explores the generation of fruitful, biologically active molecules toward expected or finished products. Several examples establish the strength of machine learning or AI in this field. AI techniques can significantly improve treatment consistency and decision making by developing useful algorithms. AI is helpful not only in the treatment of COVID-19-infected patients but also for their proper health monitoring. It can track the crisis of COVID-19 at different scales, such as medical, molecular, and epidemiological applications. It is also helpful to facilitate the research on this virus by analyzing the available data. AI can help in developing proper treatment regimens, prevention strategies, and drug and vaccine development. Combination with synthesis planning and ease of synthesis are feasible, and more and more automated drug discovery by computers is expected in the near future to eradicate the COVID-19 virus.

scholarly journals Applications and challenges of AI-based algorithms in the COVID-19 pandemic

2021 ◽  
Vol 7 (2) ◽  
pp. 387-398
Author(s):  
Danai Khemasuwan ◽  
Henri G Colt
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
Performance Metrics ◽  
Vaccine Development ◽  
Digital Transformation ◽  
Outbreak Detection ◽  
Contact Tracing ◽  
Real World Problem

The COVID-19 pandemic is shifting the digital transformation era into high gear. Artificial intelligence (AI) and, in particular, machine learning (ML) and deep learning (DL) are being applied on multiple fronts to overcome the pandemic. However, many obstacles prevent greater implementation of these innovative technologies in the clinical arena. The goal of this narrative review is to provide clinicians and other readers with an introduction to some of the concepts of AI and to describe how ML and DL algorithms are being used to respond to the COVID-19 pandemic. First, we describe the concept of AI and some of the requisites of ML and DL, including performance metrics of commonly used ML models. Next, we review some of the literature relevant to outbreak detection, contact tracing, forecasting an outbreak, detecting COVID-19 disease on medical imaging, prognostication and drug and vaccine development. Finally, we discuss major limitations and challenges pertaining to the implementation of AI to solve the real-world problem of the COVID-19 pandemic. Equipped with a greater understanding of this technology and AI’s limitations, clinicians may overcome challenges preventing more widespread applications in the clinical management of COVID-19 and future pandemics.

Application of Inherent Risk of Contagion (IRC) framework & modelling to aid local Covid-19 response & mitigation: Viewpoint (Preprint)

2020 ◽  
Author(s):  
Helmi Zakariah ◽  
Fadzilah bt Kamaluddin ◽  
Choo-Yee Ting ◽  
Hui-Jia Yee ◽  
Shereen Allaham ◽  
...  
Keyword(s):  
Public Health ◽  
Public Health Problem ◽  
Analytical Framework ◽  
Vulnerable Population ◽  
Contact Tracing ◽  
Global Public Health ◽  
Inherent Risk ◽  
Transmission Area ◽  
Current Outbreak ◽  
Novel Coronavirus

UNSTRUCTURED The current outbreak of coronavirus disease 2019 (COVID-19) caused by the novel coronavirus named SARS-CoV-2 has been a major global public health problem threatening many countries and territories. Mathematical modelling is one of the non-pharmaceutical public health measures that plays a crucial role for mitigating the risk and impact of the pandemic. A group of researchers and epidemiologists have developed a machine learning-powered inherent risk of contagion (IRC) analytical framework to georeference the COVID-19 with an operational platform to plan response & execute mitigation activities. This framework dataset provides a coherent picture to track and predict the COVID-19 epidemic post lockdown by piecing together preliminary data on publicly available health statistic metrics alongside the area of reported cases, drivers, vulnerable population, and number of premises that are suspected to become a transmission area between drivers and vulnerable population. The main aim of this new analytical framework is to measure the IRC and provide georeferenced data to protect the health system, aid contact tracing, and prioritise the vulnerable.

scholarly journals Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Brandon Malone ◽  
Boris Simovski ◽  
Clément Moliné ◽  
Jun Cheng ◽  
Marius Gheorghe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Monte Carlo ◽  
Cell Surface ◽  
Antigen Presentation ◽  
Human Population ◽  
Host Immune System ◽  
T Cell Epitopes ◽  
Digital Twin ◽  
Novel Coronavirus ◽  
Global Population

AbstractThe global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

scholarly journals Mapping value sensitive design onto AI for social good principles

AI and Ethics ◽  
2021 ◽  
Author(s):  
Steven Umbrello ◽  
Ibo van de Poel
Keyword(s):  
Artificial Intelligence ◽  
Contact Tracing ◽  
Use Case ◽  
Value Sensitive Design ◽  
Specific Design ◽  
Established Method ◽  
Social Good ◽  
Do No Harm ◽  
Design Requirements ◽  
Whole Life Cycle

AbstractValue sensitive design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: (1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; (2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good, and (3) extending the VSD process to encompass the whole life cycle of an AI technology to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

scholarly journals COVID-19 in conflict border regions: a case of South Kordofan, Sudan

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Quraish Sserwanja ◽  
Mohammed Bashir Adam ◽  
Joseph Kawuki ◽  
Emmanuel Olal
Keyword(s):  
Case Fatality ◽  
Mechanical Ventilators ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
Bed Capacity ◽  
Displaced People ◽  
Set Up ◽  
One Year ◽  
Novel Coronavirus ◽  
South Kordofan

AbstractThe novel coronavirus disease (COVID-19) was first reported in Sudan on 13 March 2020. Since then, Sudan has experienced one of the highest rates of COVID-19 spread and fatalities in Africa. One year later, as per 22 March 2021, Sudan had registered 29,661 confirmed cases and 2,028 deaths with a case fatality rate (CFR) of 6.8 %. By 12 December 2020, of the 18 states in Sudan, South Kordofan had the fifth highest CFR of 17.4 %, only surpassed by the other conflict affected North (57.5 %), Central (50.0 %) and East (31.8 %) Darfur States. By late March 2021, just three months from December 2020, the number of cases in South Kordofan increased by 100 %, but with a significant decline in the CFR from 17.4 to 8.5 %. South Kordofan is home to over 200,000 poor and displaced people from years of destructive civil unrests. To date, several localities such as the Nubba mountains region remain under rebel control and are not accessible. South Kordofan State Ministry of Health in collaboration with the federal government and non-governmental organizations set up four isolation centres with 40 total bed capacity, but with only two mechanical ventilators and no testing centre. There is still need for further multi-sectoral coalition and equitable allocation of resources to strengthen the health systems of rural and conflict affected regions. This article aims at providing insight into the current state of COVID-19 in South Kordofan amidst the second wave to address the dearth of COVID-19 information in rural and conflict affected regions.

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

scholarly journals COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society

2021 ◽  
pp. 0272989X2110030
Author(s):  
Serin Lee ◽  
Zelda B. Zabinsky ◽  
Judith N. Wasserheit ◽  
Stephen M. Kofsky ◽  
Shan Liu
Keyword(s):  
Large City ◽  
Face Mask ◽  
School Closure ◽  
Contact Tracing ◽  
The Novel ◽  
Agent Based ◽  
Social Distancing ◽  
Variable Compliance ◽  
Novel Coronavirus ◽  
Nonpharmaceutical Interventions

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

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