Development of Serological Assays and Seroprevalence Studies of the New Coronavirus 2019 (COVID-19): Reports from Saudi Arabia

Serological assays are valuable tools for tracking COVID-19 spread, estimation of herd immunity, and evaluation of vaccine effectiveness. Several reports from Saudi Arabia describe optimized in-house protocols that enable detection of SARS-CoV-2 specific antibodies and measurement of their neutralizing activity. Notably, there were variations in the approaches utilized to develop and validate these immunoassays in term of sample size, validation methodologies, and statistical analyses. The developed enzyme-linked immunoassays (ELISAs) were based on the viral full-length spike (S), S1 subunit, and nucleocapsid (NP), and enabled detection of IgM and/or IgG. ELISAs were evaluated and validated against a microneutralization assay utilizing a local SARS-CoV-2 clinical isolate, FDA-approved commercially available immunoassays, and/or real-time polymerase chain reaction (RT-PCR). Overall, the performance of the described assays was high, reaching up to 100% sensitivity and 98.9% specificity with no cross-reactivity with other coronaviruses. In-house immunoassays, along with commercially available kits, were subsequently applied in a number of sero-epidemiological studies aiming to estimate sero-positivity status among local populations including healthcare workers, COVID-19 patients, non-COVID-19 patients, and healthy blood donors. The reported seroprevalence rates differed widely among these studies, ranging from 0.00% to 32.2%. These variations are probably due to study period, targeted population, sample size, and performance of the immunoassays utilized. Indeed, lack of sero-positive cases were reported among healthy blood donors during the lockdown, while the highest rates were reported when the number of COVID-19 cases peaked in the country, particularly among healthcare workers working in referral hospitals and quarantine sites. In this review, we aim to (1) provide a critical discussion about the developed in-house immunoassays, and (2) summarize key findings of the sero-epidemiological studies and highlight strengths and weaknesses of each study.

Doppler Spread Estimation Based on Machine Learning for an OFDM System

In this paper, we propose a Doppler spread estimation approach based on machine learning for an OFDM system. We present a carefully designed neural network architecture to achieve good performance in a mixed-channel scenario in which channel characteristic variables such as Rician K factor, azimuth angle of arrival (AOA) width, mean direction of azimuth AOA, and channel estimation errors are randomly generated. When preprocessing the channel state information (CSI) collected under the mixed-channel scenario, we propose averaged power spectral density (PSD) sequence as high-quality training data in machine learning for Doppler spread estimation. We detail intermediate mathematical derivatives of the machine learning process, making it easy to graft the derived results into other wireless communication technologies. Through simulation, we show that the machine learning approach using the averaged PSD sequence as training data outperforms the other machine learning approach using the channel frequency response (CFR) sequence as training data and two other existing Doppler estimation approaches.

Hierarchical Virtual Bitmaps for Spread Estimation in Traffic Measurement

This paper introduces a hierarchical traffic model for spread measurement of network traffic flows. The hierarchical model, which aggregates lower level flows into higher-level flows in a hierarchical structure, will allow us to measure network traffic at different granularities at once to support diverse traffic analysis from a grand view to fine-grained details. The spread of a flow is the number of distinct elements (under measurement) in the flow, where the flow label (that identifies packets belonging to the flow) and the elements (which are defined based on application need) can be found in packet headers or payload. Traditional flow spread estimators are designed without hierarchical traffic modeling in mind, and incur high overhead when they are applied to each level of the traffic hierarchy. In this paper, we propose a new Hierarchical Virtual bitmap Estimator (HVE) that performs simultaneous multi-level traffic measurement, at the same cost of a traditional estimator, without degrading measurement accuracy. We implement the proposed solution and perform experiments based on real traffic traces. The experimental results demonstrate that HVE improves measurement throughput by 43% to 155%, thanks to the reduction of perpacket processing overhead. For small to medium flows, its measurement accuracy is largely similar to traditional estimators that work at one level at a time. For large aggregate and base flows, its accuracy is better, with up to 97% smaller error in our experiments.

Randomized error removal for online spread estimation in data streaming

Measuring flow spread in real time from large, high-rate data streams has numerous practical applications, where a data stream is modeled as a sequence of data items from different flows and the spread of a flow is the number of distinct items in the flow. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a data stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs that incur much smaller processing overhead and query overhead than the state of the art, yet achieves significant accuracy improvement in spread estimation. We formally analyze the performance of these new designs. We implement them in both hardware and software, and use real-world data traces to evaluate their performance in comparison with the state of the art. The experimental results show that our best sketch significantly improves over the best existing work in terms of estimation accuracy, data item processing throughput, and online query throughput.