A validation study comparing the sensitivity and specificity of the new Dr. KSU H1N1 RT-PCR kit with real-time RT-PCR for diagnosing influenza A (H1N1)

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

The emergence of a new coronavirus (SARS-CoV-2) outbreak represents a challenge for the diagnostic laboratories responsible for developing test kits to identify those infected with SARS-CoV-2. Methods with rapid and accurate detection are essential to control the sources of infection, to prevent the spread of the disease and to assist decision-making by public health managers. Currently, there is a wide variety of tests available with different detection methodologies, levels of specificity and sensitivity, detection time, and with an extensive range of prices. This review therefore aimed to conduct a patent search in relation to tests for the detection of SARS-CoV, MERS-CoV, and SARS-CoV-2. The greatest number of patents identified in the search were registered between 2003 and 2011, being mainly deposited by China, the Republic of Korea, and the United States. Most of the patents used the existing RT-PCR, ELISA, and isothermal amplification methods to develop simple, sensitive, precise, easy to use, low-cost tests that reduced false-negative or false-positive results. The findings of this patent search show that an increasing number of materials and diagnostic tests for the coronavirus are being produced to identify infected individuals and combat the growth of the current pandemic; however, there is still a question in relation to the reliability of the results of these tests.

Socioeconomic costs of influenza complications in hospitalized children

Introduction

Influenza may be correlated with a high number of complications and generate high costs of treatment. The study aimed to estimate the socioeconomic significance of hospitalized influenza cases.

Material and methods

In the 2015-2016 flu season 157 children (median age 17 months) were hospitalized in the Department of Pediatrics. The diagnosis was confirmed with the rapid influenza diagnostic test (RIDT), polymerase chain reaction (PCR) or both. The study assessed the direct and indirect costs of hospitalization, including the cost of treatment, work absence and the related income lost.

Results

The frequency of complications among the children hospitalized was 57.3% (90/157), mainly due to pneumonia (31%) and bronchitis (23%). Patients with complications required longer hospital treatment (8 vs. 6 days, p < 0.01) and generated a higher total cost (€ 1042 vs. € 779, p < 0.01), including the patient's and systemic costs (€123 vs. € 94, p < 0.01 and € 916 vs. € 690, p < 0.01, respectively). Patients with complications had a 3.5-fold higher risk of generating higher (i.e., above median) costs. The difference in the costs between children aged under 2 and those over 2 years old was greater than the difference between children aged under 5 and those over 5 years old (€ 358 vs. € 253).

Conclusions

Influenza complications generate higher systemic and patient's costs, both direct and indirect. The group of children for whom the difference is especially marked is under 2 years of age.

Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA

BACKGROUND

Despite widespread use, the accuracy of the diagnostic test for SARS-CoV-2 infection is poorly understood. The aim of our work was to better quantify misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves using publicly available surveillance data from Alberta, Canada and Philadelphia, USA.

METHODS

We examined time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, the sensitivity and specificity of the diagnostic test.

RESULTS

Our analysis revealed that the data were compatible with near-perfect specificity, but it was challenging to gain information about sensitivity. We applied these insights to uncertainty/bias analysis of epidemic curves under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the adjusted epidemic curves likely falls within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among the tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate.

CONCLUSION

The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves.

It can be dangerous to take epidemic curves of COVID-19 at face value

During an epidemic with a new virus, we depend on modelling to plan the response: but how good are the data? The aim of our work was to better understand the impact of misclassification errors in identification of true cases of COVID-19 on epidemic curves. Data originated from Alberta, Canada (available on 28 May 2020). There is presently no information of sensitivity (Sn) and specificity (Sp) of laboratory tests used in Canada for the causal agent for COVID-19. Therefore, we examined best attainable performance in other jurisdictions and similar viruses. This suggested perfect Sp and Sn 60-95%. We used these values to re-calculate epidemic curves to visualize the potential bias due to imperfect testing. If the sensitivity improved, the observed and adjusted epidemic curves likely fall within 95% confidence intervals of the observed counts. However, bias in shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. These issues are minor early in the epidemic, but hundreds of undiagnosed cases are likely later on. It is therefore hazardous to judge progress of the epidemic based on observed epidemic curves unless quality of testing is better understood.

Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: Case study of COVID-19 in Alberta, Canada and Philadelphia, USA

The aim of our work was to better understand misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves. We examined publically available time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, about the sensitivity and specificity of the PCR-based diagnostic test. Data originated from Alberta, Canada (available on 3/28/2020) and city of Philadelphia, USA (available on 3/31/2020). Our analysis revealed that the data were compatible with near-perfect specificity but it was challenging to gain information about sensitivity (prior and posterior largely overlapped). We applied these insights to uncertainty/bias analysis of epidemic curves into jurisdictions under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the observed and adjusted epidemic curves likely fall within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate. The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves, a task for which the Bayesian method we presented is well-suited.

Challenges in estimating influenza vaccine effectiveness

Introduction: Influenza vaccination is regarded as the most effective way to prevent influenza infection. Due to the rapid genetic changes that influenza viruses undergo, seasonal influenza vaccines must be reformulated and re-administered annually necessitating the evaluation of influenza vaccine effectiveness (VE) each year. The estimation of influenza VE presents numerous challenges. Areas Covered: This review aims to identify, discuss, and, where possible, offer suggestions for dealing with the following challenges in estimating influenza VE: different outcomes of interest against which VE is estimated, study designs used to assess VE, sources of bias and confounding, repeat vaccination, waning immunity, population level effects of vaccination, and VE in at-risk populations. Expert Opinion: The estimation of influenza VE has improved with surveillance networks, better understanding of sources of bias and confounding, and the implementation of advanced statistical methods. Future research should focus on better estimates of the indirect effects of vaccination, the biological effects of vaccination, and how vaccines interact with the immune system. Specifically, little is known about how influenza vaccination impacts an individual's infectiousness, how vaccines wane over time, and the impact of repeated vaccination.

Influenza virus RNA polymerase may be activated inside the virion

Influenza A and B virions are packaged with their polymerases to catalyse RNA-dependent RNA polymerase activity. Since there is no evidence to rule in or out the permissiveness of influenza virions to triphosphate ribonucleotides, we functionally evaluated this. We found the means to stimulate influenza A and B RNA polymerase activity inside the virion, called natural endogenous RNA polymerase (NERP) activity. Stimulation of NERP activity increased up to 3 log10 viral RNA content, allowing the detection of influenza virus in otherwise undetectable clinical samples. NERP activation also improved our capacity to sequence misidentified regions of the influenza genome from clinical samples. By treating the samples with the ribavirin triphosphate we inhibited NERP activity, which confirms our hypothesis and highlights that this assay could be used to screen antiviral drugs. Altogether, our data show that NERP activity could be explored to increase molecular diagnostic sensitivity and/or to develop antiviral screening assays.

Comparison of the Roche RealTime ready Influenza A/H1N1 Detection Set with CDC A/H1N1pdm09 RT-PCR on samples from three hospitals in Ho Chi Minh City, Vietnam

Real-time polymerase chain reaction (PCR) can be considered the gold standard for detection of influenza viruses due to its high sensitivity and specificity. Roche has developed the RealTime ready Influenza A/H1N1 Detection Set, consisting of a generic influenza virus A PCR targeting the M2 gene (M2 PCR) and a specific PCR targeting the hemagglutinin (HA) of A/H1N1-pdm09 (HA PCR, 2009 H1N1), with the intention to make a reliable, rapid, and simple test to detect and quantify 2009 H1N1 in clinical samples. We evaluated this kit against the US Centers for Disease Control and Prevention (USCDC)/World Health Organization real-time PCR for influenza virus using 419 nose and throat swabs from 210 patients collected in 3 large hospitals in Ho Chi Minh City, Vietnam. In the per-patient analysis, when compared to CDC PCR, the sensitivity and specificity of the M2 PCR were 85.8% and 97.6%, respectively; the sensitivity and specificity of HA PCR were 88.2% and 100%, respectively. In the per-sample analysis, the sensitivity and specificity in nose swabs were higher than those in throat swabs for both M2 and HA PCRs. The viral loads as determined with the M2 and HA PCRs correlated well with the Ct values of the CDC PCR. Compared with the CDC PCR, the kit has a reasonable sensitivity and very good specificity for the detection and quantification of influenza A virus and A/H1N1-pdm09. However, given the current status of 2009 H1N1, a kit that can detect all circulating seasonal influenza viruses would be preferable.