Current challenges in identification of clinical characteristics and detection of COVID-19: A comprehensive review

Reconstructing COVID-19 incidences from positive RT-PCR tests by deconvolution

BACKGROUND

The emergency of new COVID-19 variants over the past three years posed a serious challenge to the public health. Cities in China implemented mass daily RT-PCR tests by pooling strategies. However, a random delay exists between an infection and its first positive RT-PCR test. It is valuable for disease control to know the delay pattern and daily infection incidences reconstructed from RT-PCR test observations.

METHODS

We formulated the convolution model between daily incidences and positive RT-PCR test counts as a linear inverse problem with positivity restrictions. Consequently, the Richard-Lucy deconvolution algorithm was used to reconstruct COVID-19 incidences from daily PCR tests. A real-time deconvolution was further developed based on the same mathematical principle. The method was applied to an Omicron epidemic data set of a bar outbreak in Beijing and another in Wuxi in June 2022. We estimated the delay function by maximizing likelihood via an E-M algorithm.

RESULTS

The delay function of the bar-outbreak in 2022 differs from that reported in 2020. Its mode was shortened to 4 days by one day. A 95% confidence interval of the mean delay is [4.43,5.55] as evaluated by bootstrap. In addition, the deconvolved infection incidences successfully detected two associated infection events after the bar was closed. The application of the real-time deconvolution to the Wuxi data identified all explosive incidence increases. The results revealed the progression of the two COVID-19 outbreaks and provided new insights for prevention and control strategies, especially for the role of mass daily RT-PCR testing.

CONCLUSIONS

The proposed deconvolution method is generally applicable to other infectious diseases if the delay model can be assumed to be approximately valid. To ensure a fair reconstruction of daily infection incidences, the delay function should be estimated in a similar context in terms of virus variant and test protocol. Both the delay estimate from the E-M algorithm and the incidences resulted from deconvolution are valuable for epidemic prevention and control. The real-time feedback is particularly useful during the epidemic's acute phase because it can help the local disease control authorities modify the control measures more promptly and precisely.

Development and Analytical Evaluation of a Point-of-Care Electrochemical Biosensor for Rapid and Accurate SARS-CoV-2 Detection

The COVID-19 pandemic has underscored the critical need for rapid and accurate screening and diagnostic methods for potential respiratory viruses. Existing COVID-19 diagnostic approaches face limitations either in terms of turnaround time or accuracy. In this study, we present an electrochemical biosensor that offers nearly instantaneous and precise SARS-CoV-2 detection, suitable for point-of-care and environmental monitoring applications. The biosensor employs a stapled hACE-2 N-terminal alpha helix peptide to functionalize an in situ grown polypyrrole conductive polymer on a nitrocellulose membrane backbone through a chemical process. We assessed the biosensor's analytical performance using heat-inactivated omicron and delta variants of the SARS-CoV-2 virus in artificial saliva (AS) and nasal swab (NS) samples diluted in a strong ionic solution, as well as clinical specimens with known Ct values. Virus identification was achieved through electrochemical impedance spectroscopy (EIS) and frequency analyses. The assay demonstrated a limit of detection (LoD) of 40 TCID50/mL, with 95% sensitivity and 100% specificity. Notably, the biosensor exhibited no cross-reactivity when tested against the influenza virus. The entire testing process using the biosensor takes less than a minute. In summary, our biosensor exhibits promising potential in the battle against pandemic respiratory viruses, offering a platform for the development of rapid, compact, portable, and point-of-care devices capable of multiplexing various viruses. The biosensor has the capacity to significantly bolster our readiness and response to future viral outbreaks.

Electronic field effect detection of SARS-CoV-2 N-protein before the onset of symptoms

As part of the efforts to contain the pandemic, researchers around the world have raced to develop testing platforms to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the Coronavirus disease 2019 (COVID-19). Within the different detection platforms studied, the field effect transistor (FET) is a promising device due to its high sensitivity and fast detection capabilities. In this work, a graphene-based FET which uses a boron and nitrogen co-doped graphene oxide gel (BN-GO gel) transducer functionalized with nucleoprotein antibodies, has been investigated for the detection of SARS-CoV-2 nucleocapsid (N)-protein in buffer. This biosensor was able to detect the viral protein in less than 4 min, with a limit of detection (LOD) as low as 10 ag/mL and a wide linear detection range stretching over 11 orders of magnitude from 10 ag/mL-1 μg/mL. This represents the lowest LOD and widest detection range of any COVID-19 sensor and thus can potentially enable the detection of infected individuals before they become contagious. In addition to its potential use in the COVID-19 pandemic, our device serves as a proof-of-concept of the ability of functionalized BN-GO gel FETs to be used for ultrasensitive yet robust biosensors.