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Register a new userRoutine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden
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Yajuan Si
Publication date
2021
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Mathematical Statistics
and research's language is
English
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Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. In the absence of any successful public or academic campaign for comprehensive or random testing, we have developed a proxy method for synthetic random sampling, based on viral RNA testing of patients who present for elective procedures within a hospital system. We present here an approach under multilevel regression and poststratification (MRP) to collecting and analyzing data on viral exposure among patients in a hospital system and performing statistical adjustment that has been made publicly available to estimate true viral incidence and trends in the community. We apply our MRP method to track viral behavior in a mixed urban-suburban-rural setting in Indiana. This method can be easily implemented in a wide variety of hospital settings. Finally, we provide evidence that this model predicts the clinical burden of SARS-CoV-2 earlier and more accurately than currently accepted metrics.
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In the case of SARS-CoV-2 pandemic management, wastewater-based epidemiology aims to derive information on the infection dynamics by monitoring virus concentrations in the wastewater. However, due to the intrinsic random fluctuations of the viral signal in the wastewater (due to e.g., dilution; transport and fate processes in sewer system; variation in the number of persons discharging; variations in virus excretion and water consumption per day) the subsequent prevalence analysis may result in misleading conclusions. It is thus helpful to apply data filtering techniques to reduce the noise in the signal. In this paper we investigate 13 smoothing algorithms applied to the virus signals monitored in four wastewater treatment plants in Austria. The parameters of the algorithms have been defined by an optimization procedure aiming for performance metrics. The results are further investigated by means of a cluster analysis. While all algorithms are in principle applicable, SPLINE, Generalized Additive Model and Friedman Super Smoother are recognized as superior methods in this context (with the latter two having a tendency to over-smoothing). A first analysis of the resulting datasets indicates the influence of catchment size for wastewater-based epidemiology as smaller communities both reveal a signal threshold before any relation with infection dynamics is visible and also a higher sensitivity towards infection clusters.
In $2020$, Korea Disease Control and Prevention Agency reported three rounds of surveys on seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in South Korea. We analyze the seroprevalence surveys using a Bayesian method with an informative prior distribution on the seroprevalence parameter, and the sensitivity and specificity of the diagnostic test. We construct the informative prior using the posterior distribution obtained from the clinical evaluation data based on the plaque reduction neutralization test. The constraint of the seroprevalence parameter induced from the known confirmed coronavirus 2019 cases can be imposed naturally in the proposed Bayesian model. We also prove that the confidence interval of the seroprevalence parameter based on the Raos test can be the empty set, while the Bayesian method renders a reasonable interval estimator. As of the $30$th of October $2020$, the $95%$ credible interval of the estimated SARS-CoV-2 positive population does not exceed $307,448$, approximately $0.6%$ of the Korean population.
The COVID-19 outbreak is asynchronous in US counties. Mitigating the COVID-19 transmission requires not only the state and federal level order of protective measures such as social distancing and testing, but also public awareness of time-dependent risk and reactions at county and community levels. We propose a robust approach to estimate the heterogeneous progression of SARS-CoV-2 at all US counties having no less than 2 COVID-19 associated deaths, and we use the daily probability of contracting (PoC) SARS-CoV-2 for a susceptible individual to quantify the risk of SARS-CoV-2 transmission in a community. We found that shortening by $5%$ of the infectious period of SARS-CoV-2 can reduce around $39%$ (or $78$K, $95%$ CI: $[66$K $, 89$K $]$) of the COVID-19 associated deaths in the US as of 20 September 2020. Our findings also indicate that reducing infection and deaths by a shortened infectious period is more pronounced for areas with the effective reproduction number close to 1, suggesting that testing should be used along with other mitigation measures, such as social distancing and facial mask-wearing, to reduce the transmission rate. Our deliverable includes a dynamic county-level map for local officials to determine optimal policy responses and for the public to better understand the risk of contracting SARS-CoV-2 on each day.
Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.
CovID-19 genetics analysis is critical to determine virus type,virus variant and evaluate vaccines. In this paper, SARS-Cov-2 RNA sequence analysis relative to region or territory is investigated. A uniform framework of sequence SVM model with various genetics length from short to long and mixed-bases is developed by projecting SARS-Cov-2 RNA sequence to different dimensional space, then scoring it according to the output probability of pre-trained SVM models to explore the territory or origin information of SARS-Cov-2. Different sample size ratio of training set and test set is also discussed in the data analysis. Two SARS-Cov-2 RNA classification tasks are constructed based on GISAID database, one is for mainland, Hongkong and Taiwan of China, and the other is a 6-class classification task (Africa, Asia, Europe, North American, South American& Central American, Ocean) of 7 continents. For 3-class classification of China, the Top-1 accuracy rate can reach 82.45% (train 60%, test=40%); For 2-class classification of China, the Top-1 accuracy rate can reach 97.35% (train 80%, test 20%); For 6-class classification task of world, when the ratio of training set and test set is 20% : 80% , the Top-1 accuracy rate can achieve 30.30%. And, some Top-N results are also given.
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