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.

Data standardization implementation and applications within and among diagnostic laboratories: integrating and monitoring enteric coronaviruses.

Every day, thousands of samples from diverse populations of animals are submitted to veterinary diagnostic laboratories (VDLs) for testing. Each VDL has its own laboratory information management system (LIMS), with processes and procedures to capture submission information, perform laboratory tests, define the boundaries of test results (i.e., positive or negative), and report results, in addition to internal business and accounting applications. Enormous quantities of data are accumulated and stored within VDL LIMSs. There is a need for platforms that allow VDLs to exchange and share portions of laboratory data using standardized, reliable, and sustainable information technology processes. Here we report concepts and applications for standardization and aggregation of data from swine submissions to multiple VDLs to detect and monitor porcine enteric coronaviruses by RT-PCR. Oral fluids, feces, and fecal swabs were the specimens submitted most frequently for enteric coronavirus testing. Statistical algorithms were used successfully to scan and monitor the overall and state-specific percentage of positive submissions. Major findings revealed a consistently recurrent seasonal pattern, with the highest percentage of positive submissions detected during December-February for porcine epidemic diarrhea virus, porcine deltacoronavirus, and transmissible gastroenteritis virus (TGEV). After 2014, very few submissions tested positive for TGEV. Monitoring VDL data proactively has the potential to signal and alert stakeholders early of significant changes from expected detection. We demonstrate the importance of, and applications for, data organized and aggregated by using LOINC and SNOMED CTs, as well as the use of customized messaging to allow inter-VDL exchange of information.

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.

Pediatric Asthma Health Care Utilization, Viral Testing, and Air Pollution Changes During the COVID-19 Pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused dramatic changes in daily routines and health care utilization and delivery patterns in the United States. Understanding the influence of these changes and associated public health interventions on asthma care is important to determine effects on patient outcomes and identify measures that will ensure optimal future health care delivery. OBJECTIVE: We sought to identify changes in pediatric asthma-related health care utilization, respiratory viral testing, and air pollution during the COVID-19 pandemic. METHODS: For the time period January 17 to May 17, 2015 to 2020, asthma-related encounters and weekly summaries of respiratory viral testing data were extracted from Children's Hospital of Philadelphia electronic health records, and pollution data for 4 criteria air pollutants were extracted from AirNow. Changes in encounter characteristics, viral testing patterns, and air pollution before and after Mar 17, 2020, the date public health interventions to limit viral transmission were enacted in Philadelphia, were assessed and compared with data from 2015 to 2019 as a historical reference. RESULTS: After March 17, 2020, in-person asthma encounters decreased by 87% (outpatient) and 84% (emergency + inpatient). Video telemedicine, which was not previously available, became the most highly used asthma encounter modality (61% of all visits), and telephone encounters increased by 19%. Concurrently, asthma-related systemic steroid prescriptions and frequency of rhinovirus test positivity decreased, although air pollution levels did not substantially change, compared with historical trends. CONCLUSIONS: The COVID-19 pandemic in Philadelphia was accompanied by changes in pediatric asthma health care delivery patterns, including reduced admissions and systemic steroid prescriptions. Reduced rhinovirus infections may have contributed to these patterns.

Development of a TaqMan-probe-based multiplex real-time PCR for the simultaneous detection of emerging and reemerging swine coronaviruses.

With the outbreak of the recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019, coronaviruses have become a global research hotspot in the field of virology. Coronaviruses mainly cause respiratory and digestive tract diseases, several coronaviruses are responsible for porcine diarrhea, such as porcine epidemic diarrhea virus (PEDV), porcine deltacoronavirus (PDCoV), and emerging swine acute diarrhea syndrome coronavirus (SADS-CoV). Those viruses have caused huge economic losses and are considered as potential public health threats. Porcine torovirus (PToV) and coronaviruses, sharing similar genomic structure and replication strategy, belong to the same order Nidovirales. Here, we developed a multiplex TaqMan-probe-based real-time PCR for the simultaneous detection of PEDV, PDCoV, PToV, and SADS-CoV for the first time. Specific primers and TaqMan fluorescent probes were designed targeting the ORF1a region of PDEV, PToV, and SADS-CoV and the ORF1b region of PDCoV. The method showed high sensitivity and specificity, with a detection limit of 1 × 102 copies/µL for each pathogen. A total of 101 clinical swine samples with signs of diarrhea were analyzed using this method, and the result showed good consistency with conventional reverse transcription PCR (RT-PCR). This method improves the efficiency for surveillance of these emerging and reemerging swine enteric viruses and can help reduce economic losses to the pig industry, which also benefits animal and public health.

Recent discovery and development of inhibitors targeting coronaviruses.

Human coronaviruses (CoVs) are enveloped viruses with a positive-sense single-stranded RNA genome. Currently, six human CoVs have been reported including human coronavirus 229E (HCoV-229E), OC43 (HCoV-OC43), NL63 (HCoV-NL63), HKU1 (HCoV-HKU1), severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV), and MiddleEast respiratory syndrome (MERS) coronavirus (MERS-CoV). They cause moderate to severe respiratory and intestinal infections in humans. In this review, we focus on recent advances in the research and development of small-molecule anti-human coronavirus therapies targeting different stages of the CoV life cycle.