Meta-analysis of diagnostic performance of serological tests for SARS-CoV-2 antibodies up to 25 April 2020 and public health implications.

We reviewed the diagnostic accuracy of SARS-CoV-2 serological tests. Random-effects models yielded a summary sensitivity of 82% for IgM, and 85% for IgG and total antibodies. For specificity, the pooled estimate were 98% for IgM and 99% for IgG and total antibodies. In populations with ≤ 5% of seroconverted individuals, unless the assays have perfect (i.e. 100%) specificity, the positive predictive value would be ≤ 88%. Serological tests should be used for prevalence surveys only in hard-hit areas.

Current methods for diagnosis of human coronaviruses: pros and cons.

The current global fight against coronavirus disease (COVID-19) to flatten the transmission curve is put forth by the World Health Organization (WHO) as there is no immediate diagnosis or cure for COVID-19 so far. In order to stop the spread, researchers worldwide are working around the clock aiming to develop reliable tools for early diagnosis of severe acute respiratory syndrome (SARS-CoV-2) understanding the infection path and mechanisms. Currently, nucleic acid-based molecular diagnosis (real-time reverse transcription polymerase chain reaction (RT-PCR) test) is considered the gold standard for early diagnosis of SARS-CoV-2. Antibody-based serology detection is ineffective for the purpose of early diagnosis, but a potential tool for serosurveys, providing people with immune certificates for clearance from COVID-19 infection. Meanwhile, there are various blooming methods developed these days. In this review, we summarise different types of coronavirus discovered which can be transmitted between human beings. Methods used for diagnosis of the discovered human coronavirus (SARS, MERS, COVID-19) including nucleic acid detection, gene sequencing, antibody detection, antigen detection, and clinical diagnosis are presented. Their merits, demerits and prospects are discussed which can help the researchers to develop new generation of advanced diagnostic tools for accurate and effective control of human coronavirus transmission in the communities and hospitals.

RespiCoV: Simultaneous identification of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and 46 respiratory tract viruses and bacteria by amplicon-based Oxford-Nanopore MinION sequencing.

Since December 2019 the world has been facing the outbreak of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Identification of infected patients and discrimination from other respiratory infections have so far been accomplished by using highly specific real-time PCRs. Here we present a rapid multiplex approach (RespiCoV), combining highly multiplexed PCRs and MinION sequencing suitable for the simultaneous screening for 41 viral and five bacterial agents related to respiratory tract infections, including the human coronaviruses NL63, HKU1, OC43, 229E, Middle East respiratory syndrome coronavirus, SARS-CoV, and SARS-CoV-2. RespiCoV was applied to 150 patient samples with suspected SARS-CoV-2 infection and compared with specific real-time PCR. Additionally, several respiratory tract pathogens were identified in samples tested positive or negative for SARS-CoV-2. Finally, RespiCoV was experimentally compared to the commercial RespiFinder 2SMART multiplex screening assay (PathoFinder, The Netherlands).

Surveillance of SARS-CoV-2 virus circulation using Acute Respiratory Infections sentinel system of Catalonia (PIDIRAC) during the 2019-2020 season: A retrospective observational study.

BACKGROUND: In the context of COVID-19 pandemic in Catalonia (Spain), the present study analyses respiratory samples collected by the primary care network using Acute Respiratory Infections Sentinel Surveillance System (PIDIRAC) during the 2019-2020 season to complement the pandemic surveillance system in place to detect SARS-CoV-2. The aim of the study is to describe whether SARS-CoV-2 was circulating before the first confirmed case was detected in Catalonia, on February 25th, 2020. METHODS: The study sample was made up of all samples collected by the PIDIRAC primary care network as part of the Influenza and Acute Respiratory Infections (ARI) surveillance system activities. The study on respiratory virus included coronavirus using multiple RT-PCR assays. All positive samples for human coronavirus were subsequently typed for HKU1, OC43, NL63, 229E. Every respiratory sample was frozen at-80°C and retrospectively studied for SARS-CoV-2 detection. A descriptive study was performed, analysing significant differences among variables related to SARS-CoV- 2 cases comparing with rest of coronaviruses cases through a bivariate study with Chi-squared test and statistical significance at 95%. RESULTS: Between October 2019 and April 2020, 878 respiratory samples from patients with acute respiratory infection or influenza syndrome obtained by PIDIRAC were analysed. 51.9% tested positive for influenza virus, 48.1% for other respiratory viruses. SARS-CoV-2 was present in 6 samples. The first positive SARS-CoV-2 case had symptom onset on 2 March 2020. These 6 cases were 3 men and 3 women, aged between 25 and 50 years old. 67% had risk factors, none had previous travel history nor presented viral coinfection. All of them recovered favourably. CONCLUSION: Sentinel Surveillance PIDIRAC enhances global epidemiological surveillance by allowing confirmation of viral circulation and describes the epidemiology of generalized community respiratory viruses' transmission in Catalonia. The system can provide an alert signal when identification of a virus is not achieved in order to take adequate preparedness measures.

Analysis of Imported Cases of COVID-19 in Taiwan: A Nationwide Study.

In the early stages of the 2019 novel coronavirus disease (COVID-19) pandemic, containment of disease importation from epidemic areas was essential for outbreak control. This study is based on publicly accessible data on confirmed COVID-19 cases in Taiwan extracted from the Taiwan Centers for Disease Control website. We analysed the characteristics, infection source, symptom presentation, and route of identification of the 321 imported cases that were identified from 21 January to 6 April 2020. They were mostly returned Taiwanese citizens who had travelled to one or more of 37 countries for tourism, business, work, or study. Half of these cases developed symptoms before arrival, most of the remainder developed symptoms 1-13 days (mean 4.0 days) after arrival, and 3.4% never developed symptoms. Three-quarters of the cases had respiratory symptoms, 44.9% had fever, 13.1% lost smell or taste, and 7.2% had diarrhoea. Body temperature and symptom screening at airports identified 32.7% of the cases. Of the remainder, 27.7% were identified during home quarantining, 16.2% were identified via contact tracing, and 23.4% were reported by hospitals. Under the strict enforcement of these measures, the incidence of locally acquired COVID-19 cases in Taiwan remains sporadic. In conclusion, proactive border control measures are effective for preventing community transmission of this disease.

Time Course of COVID-19 Cases in Austria.

COVID-19 is an infectious disease caused by a novel coronavirus, which first appeared in China in late 2019, and reached pandemic distribution in early 2020. The first major outbreak in Europe occurred in Northern Italy where it spread to neighboring countries, notably to Austria, where skiing resorts served as a main transmission hub. Soon, the Austrian government introduced strict measures to curb the spread of the virus. Using publicly available data, we assessed the efficiency of the governmental measures. We assumed an average incubation period of one week and an average duration of infectivity of 10 days. One week after the introduction of strict measures, the increase in daily new cases was reversed, and the reproduction number dropped. The crude estimates tended to overestimate the reproduction rate in the early phase. Publicly available data provide a first estimate about the effectiveness of public health measures. However, more data are needed for an unbiased assessment.

A Cross Sectional Sampling Reveals Novel Coronaviruses in Bat Populations of Georgia.

Mammal-associated coronaviruses have a long evolutionary history across global bat populations, which makes them prone to be the most likely ancestral origins of coronavirus-associated epidemics and pandemics globally. Limited coronavirus research has occurred at the junction of Europe and Asia, thereby investigations in Georgia are critical to complete the coronavirus diversity map in the region. We conducted a cross-sectional coronavirus survey in bat populations at eight locations of Georgia, from July to October of 2014. We tested 188 anal swab samples, remains of previous pathogen discovery studies, for the presence of coronaviruses using end-point pan-coronavirus RT-PCR assays. Samples positive for a 440 bp amplicon were Sanger sequenced to infer coronavirus subgenus or species through phylogenetic reconstructions. Overall, we found a 24.5% positive rate, with 10.1% for Alphacoronavirus and 14.4% for Betacoronavirus. Albeit R. euryale, R. ferrumequinum, M. blythii and M. emarginatus were found infected with both CoV genera, we could not rule out CoV co-infection due to limitation of the sequencing method used and sample availability. Based on phylogenetic inferences and genetic distances at nucleotide and amino acid levels, we found one putative new subgenus and three new species of Alphacoronavirus, and two new species of Betacoronavirus.

Molecular detection of bat coronaviruses in three bat species in Indonesia.

Bats are an important reservoir of several zoonotic diseases. However, the circulation of bat coronaviruses (BatCoV) in live animal markets in Indonesia has not been reported. Genetic characterization of BatCoV was performed by sequencing partial RdRp genes. Real-time polymerase chain reaction based on nucleocapsid protein (N) gene and Enzyme-linked immunosorbent assay against the N protein were conducted to detect the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral RNA and antibody, respectively. We identified the presence of BatCoV on Cynopterus brachyotis, Macroglossus minimus, and Rousettus amplexicaudatus. The results showed that the BatCoV included in this study are from an unclassified coronavirus group. Notably, SARS-CoV-2 viral RNA and antibodies were not detected in the sampled bats.

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method.

BACKGROUND: Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. METHODS: A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. RESULTS: The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5-25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. CONCLUSIONS: Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.

Traceable surveillance and genetic diversity analysis of coronaviruses in poultry from China in 2019.

Coronavirus disease 2019 (COVID-19), caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first reported in Wuhan, China, and rapidly spread throughout the world. This newly emerging pathogen is highly transmittable and can cause fatal disease. More than 35 million cases have been confirmed, with a fatality rate of about 2.9% to October 9, 2020. However, the original and intermediate hosts of SARS-CoV-2 remain unknown. Here, 3160 poultry samples collected from 14 provinces of China between September and December 2019 were tested for SARS-CoV-2 infection. All the samples were SARS-CoV-2 negative, but 593 avian coronaviruses were detected, including 485 avian infectious bronchitis viruses, 72 duck coronaviruses, and 36 pigeon coronaviruses, with positivity rates of 15.35%, 2.28%, and 1.14%, respectively. Our surveillance demonstrates the diversity of avian coronaviruses in China, with higher prevalence rates in some regions. Furthermore, the possibility that SARS-CoV-2 originated from a known avian-origin coronavirus can be preliminarily ruled out. More surveillance of and research into avian coronaviruses are required to better understand the diversity, distribution, cross-species transmission, and clinical significance of these viruses.

Antiviral Activity of Peptide-Based Assemblies.

The COVID-19 pandemic highlighted the importance of developing surfaces and coatings with antiviral activity. Here, we present, for the first time, peptide-based assemblies that can kill viruses. The minimal inhibitory concentration (MIC) of the assemblies is in the range tens of micrograms per milliliter. This value is 2 orders of magnitude smaller than the MIC of metal nanoparticles. When applied on a surface, by drop casting, the peptide spherical assemblies adhere to the surface and form an antiviral coating against both RNA- and DNA-based viruses including coronavirus. Our results show that the coating reduced the number of T4 bacteriophages (DNA-based virus) by 3 log, compared with an untreated surface and 6 log, when compared with a stock solution. Importantly, we showed that this coating completely inactivated canine coronavirus (RNA-based virus). This peptide-based coating can be useful wherever sterile surfaces are needed to reduce the risk of viral transmission.

Whole genome and phylogenetic analysis of two SARS-CoV-2 strains isolated in Italy in January and February 2020: additional clues on multiple introductions and further circulation in Europe.

Whole genome sequences of SARS-CoV-2 obtained from two patients, a Chinese tourist visiting Rome and an Italian, were compared with sequences from Europe and elsewhere. In a phylogenetic tree, the Italian patient's sequence clustered with sequences from Germany while the tourist's sequence clustered with other European sequences. Some additional European sequences in the tree segregated outside the two clusters containing the patients' sequences. This suggests multiple SARS-CoV-2 introductions in Europe or virus evolution during circulation.

Discovery of Bat Coronaviruses through Surveillance and Probe Capture-Based Next-Generation Sequencing.

Coronaviruses (CoVs) of bat origin have caused two pandemics in this century. Severe acute respiratory syndrome (SARS)-CoV and Middle East respiratory syndrome (MERS)-CoV both originated from bats, and it is highly likely that bat coronaviruses will cause future outbreaks. Active surveillance is both urgent and essential to predict and mitigate the emergence of these viruses in humans. Next-generation sequencing (NGS) is currently the preferred methodology for virus discovery to ensure unbiased sequencing of bat CoVs, considering their high genetic diversity. However, unbiased NGS is an expensive methodology and is prone to missing low-abundance CoV sequences due to the high background level of nonviral sequences present in surveillance field samples. Here, we employ a capture-based NGS approach using baits targeting most of the CoV species. Using this technology, we effectively reduced sequencing costs by increasing the sensitivity of detection. We discovered nine full genomes of bat CoVs in this study and revealed great genetic diversity for eight of them.IMPORTANCE Active surveillance is both urgent and essential to predict and mitigate the emergence of bat-origin CoV in humans and livestock. However, great genetic diversity increases the chance of homologous recombination among CoVs. Performing targeted PCR, a common practice for many surveillance studies, would not reflect this diversity. NGS, on the other hand, is an expensive methodology and is prone to missing low-abundance CoV sequences. Here, we employ a capture-based NGS approach using baits targeting all CoVs. Our work demonstrates that targeted, cost-effective, large-scale, genome-level surveillance of bat CoVs is now highly feasible.