Trends in MERS-CoV, SARS-CoV, and SARS-CoV-2 (COVID-19) Diagnosis Strategies: A Patent Review

What, Where, When and How of COVID-19 Patents Landscape: A Bibliometrics Review

Two years after COVID-19 came into being, many technologies have been developed to bring highly promising bedside methods to help fight this epidemic disease. However, owing to viral mutation, how far the promise can be realized remains unclear. Patents might act as an additional source of information for informing research and policy and anticipating important future technology developments. A comprehensive study of 3741 COVID-19-related patents (3,543 patent families) worldwide was conducted using the Derwent Innovation database. Descriptive statistics and social network analysis were used in the patent landscape. The number of COVID-19 applications, especially those related to treatment and prevention, continued to rise, accompanied by increases in governmental and academic patent assignees. Although China dominated COVID-19 technologies, this position is worth discussing, especially in terms of the outstanding role of India and the US in the assignee collaboration network as well as the outstanding invention portfolio in Italy. Intellectual property barriers and racist treatment were reduced, as reflected by individual partnerships, transparent commercial licensing and diversified portfolios. Critical technological issues are personalized immunity, traditional Chinese medicine, epidemic prediction, artificial intelligence tools, and nucleic acid detection. Notable challenges include balancing commercial competition and humanitarian interests. The results provide a significant reference for decision-making by researchers, clinicians, policymakers, and investors with an interest in COVID-19 control.

Improving the efficiency and effectiveness of an industrial SARS-CoV-2 diagnostic facility

On 11th March 2020, the UK government announced plans for the scaling of COVID-19 testing, and on 27th March 2020 it was announced that a new alliance of private sector and academic collaborative laboratories were being created to generate the testing capacity required. The Cambridge COVID-19 Testing Centre (CCTC) was established during April 2020 through collaboration between AstraZeneca, GlaxoSmithKline, and the University of Cambridge, with Charles River Laboratories joining the collaboration at the end of July 2020. The CCTC lab operation focussed on the optimised use of automation, introduction of novel technologies and process modelling to enable a testing capacity of 22,000 tests per day. Here we describe the optimisation of the laboratory process through the continued exploitation of internal performance metrics, while introducing new technologies including the Heat Inactivation of clinical samples upon receipt into the laboratory and a Direct to PCR protocol that removed the requirement for the RNA extraction step. We anticipate that these methods will have value in driving continued efficiency and effectiveness within all large scale viral diagnostic testing laboratories.

Estimated SARS-CoV-2 Infection and Seroprevalence in Firefighters from a Northeastern Brazilian State: A Cross-Sectional Study

The new coronavirus has been affecting health worldwide and essential service workers are continually exposed to this infectious agent, increasing the chance of infection and the development of the disease. Thus, this study aimed to estimate the frequency of infection and seroprevalence for SARS-CoV-2 in military firefighters in a city in Northeastern Brazil in January 2021. An observational cross-sectional study was carried out with 123 firefighters who answered a brief questionnaire to collect socio-epidemiological data and underwent RT-PCR and immunofluorescence test (IgM and IgG). The results found reveal a positive seroprevalence, with a high rate of infection in this class of workers, since they are essential service professionals who are exposed to risk due to their working hours, in addition to sharing some spaces and work materials. Besides, there were significant associations between positivity for IgG and IgM, as well as for positive RT-PCR prior to the study and the presence of IgG, with odd ratios of 3.04 and 4.9, respectively. These findings reinforce the need for immunization in this category, whose line of service hinders the adoption of distancing measures, since in many situations physical contact is inevitable.

Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model

Background: The outbreak of COVID-19 in 2019 has rapidly swept the world, causing irreparable loss to human beings. The pandemic has shown that there is still a delay in the early response to disease outbreaks and needs a method for unknown disease outbreak detection. The study's objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection. Methods: The study defined abnormal values with diagnostic significances from clinical data as the Features, and defined the Features as the antecedents of inference rules to match with knowledge bases, achieved in detecting known or emerging infectious disease outbreaks. Meanwhile, the study built a syndromic surveillance base to capture the target cases' Features to improve the reliability and fault-tolerant ability of the system. Results: The study combined the method with Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and early COVID-19 outbreaks as empirical studies. The results showed that with suitable surveillance guidelines, the method proposed in this study was capable to detect outbreaks of SARS, MERS, and early COVID-19 pandemics. The quick matching accuracies of confirmed infection cases were 89.1, 26.3-98%, and 82%, and the syndromic surveillance base would capture the Features of the remaining cases to ensure the overall detection accuracies. Based on the early COVID-19 data in Wuhan, this study estimated that the median time of the early COVID-19 cases from illness onset to local authorities' responses could be reduced to 7.0-10.0 days. Conclusions: This study offers a new solution to transfer traditional medical knowledge into structured data and form diagnosis rules, enables the representation of doctors' logistic thinking and the knowledge transmission among different users. The results of empirical studies demonstrate that by constantly inputting medical knowledge into the system, the proposed method will be capable to detect unknown diseases from existing ones and perform an early response to the initial outbreaks.

Global landscape of patents related to human coronaviruses

At present, the COVID-19 pandemic is running rampant, having caused 2.18 million deaths. Characterizing the global patent landscape of coronaviruses is essential not only for informing research and policy, given the current pandemic crisis, but also for anticipating important future developments. While patents are a promising indicator of technological knowledge production widely used in innovation research, they are often an underused resource in biological sciences. In this study, we present a patent landscape for the seven coronaviruses known to infect humans. The information included in this paper provides a strong intellectual groundwork for the ongoing development of therapeutic agents and vaccines along with a deeper discussion of intellectual property rights under epidemic conditions. The results show that there has been a rapid increase in human coronavirus patents, especially COVID-19 patents. China and the United States play an outstanding role in global cooperation and patent application. The leading role of academic institutions and government is increasingly apparent. Notable technological issues related to human coronaviruses include pharmacochemical treatment, diagnosis of viral infection, viral-vector vaccines, and traditional Chinese medicine. Furthermore, a critical challenge lies in balancing commercial competition, enterprise profit, knowledge sharing, and public interest.