SAUNet++: an automatic segmentation model of COVID-19 lesion from CT slices

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide and the healthcare system is in crisis. Accurate, automated and rapid segmentation of COVID-19 lesion in computed tomography (CT) images can help doctors diagnose and provide prognostic information. However, the variety of lesions and small regions of early lesion complicate their segmentation. To solve these problems, we propose a new SAUNet++ model with squeeze excitation residual (SER) module and atrous spatial pyramid pooling (ASPP) module. The SER module can assign more weights to more important channels and mitigate the problem of gradient disappearance;the ASPP module can obtain context information by atrous convolution using various sampling rates. In addition, the generalized dice loss (GDL) can reduce the correlation between lesion size and dice loss, and is introduced to solve the problem of small regions segmentation of COVID-19 lesion. We collected multinational CT scan data from China, Italy and Russia and conducted extensive comparative and ablation studies. The experimental results demonstrated that our method outperforms state-of-the-art models and can effectively improve the accuracy of COVID-19 lesion segmentation on the dice similarity coefficient (our: 87.38% vs. U-Net++: 84.25%), sensitivity (our: 93.28% vs. U-Net++: 89.85%) and Hausdorff distance (our: 19.99 mm vs. U-Net++: 26.79 mm), respectively.

Individual preferences, government policy, and COVID-19: A game-theoretic epidemiological analysis.

Purpose: The ongoing COVID-19 pandemic imposes serious short-term and long-term health costs on populations. Restrictive government policy measures decrease the risks of infection, but produce similarly serious social, mental health, and economic problems. Citizens have varying preferences about the desirability of restrictive policies, and governments are thus forced to navigate this tension in making pandemic policy. This paper analyses the situation facing government using a game-theoretic epidemiological model. Methodology: We classify individuals into health-centered individuals and freedom-centered individuals to capture the heterogeneous preferences of citizens. We first use the extended Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) model (adding individual preferences) and the signaling game model (adding government) to analyze the strategic situation against the backdrop of a realistic model of COVID-19 infection. Findings: We find the following: 1. There exists two pooling equilibria. When health-centered and freedom-centered individuals send anti-epidemic signals, the government will adopt strict restrictive policies under budget surplus or balance. When health-centered and freedom-centered individuals send freedom signals, the government chooses not to implement restrictive policies. 2. When governments choose not to impose restrictions, the extinction of an epidemic depends on whether it has a high infection transmission rate; when the government chooses to implement non-pharmacological interventions (NPIs), whether an epidemic will disappear depends on how strict the government's restrictions are. Originality/value: Based on the existing literature, we add individual preferences and put the government into the game as a player. Our research extends the current form of combining epidemiology and game theory. By using both we get a more realistic understanding of the spread of the virus and combine that with a richer understanding of the strategic social dynamics enabled by game theoretic analysis. Our findings have important implications for public management and government decision-making in the context of COVID-19 and for potential future public health emergencies.

A fuzzy fine-tuned model for COVID-19 diagnosis.

The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning, such as transfer learning and ensemble learning, have been presented. However, these techniques cannot deal with noises and their propagation in different layers. In addition, many of the datasets already being used are imbalanced, and most techniques have used binary classification, COVID-19, from normal cases. To address these issues, we use the blind/referenceless image spatial quality evaluator to filter out inappropriate data in the dataset. In order to increase the volume and diversity of the data, we merge two datasets. This combination of two datasets allows multi-class classification between the three states of normal, COVID-19, and types of pneumonia, including bacterial and viral types. A weighted multi-class cross-entropy is used to reduce the effect of data imbalance. In addition, a fuzzy fine-tuned Xception model is applied to reduce the noise propagation in different layers. Quantitative analysis shows that our proposed model achieves 96.60% accuracy on the merged test set, which is more accurate than previously mentioned state-of-the-art methods.

COVID-19 Disease Prediction Utilizing Dilated Convolution Neural Network Based Levy Flight Tunicate Swarm Optimization.

The worldwide pandemic of COVID-19 illness has wreaked havoc on the health and lives of countless individuals in more than 200 countries. More than 44 million individuals have been afflicted by October 2020, with over 1,000,000 fatalities reported. This disease, which is classified as a pandemic, is still being researched for diagnosis and therapy. It is critical to diagnose this condition early in order to save a person's life. Diagnostic investigations based on deep learning are speeding up this procedure. As a result, in order to contribute to this sector, our research proposes a deep learning-based technique that may be employed for illness early detection. Based on this insight, gaussian filter is applied to the collected CT images and the filtered images are subjected to the proposed tunicate dilated convolutional neural network, whereas covid and non-covid disease are categorized to improve the accuracy requirement. The hyperparameters involved in the proposed deep learning techniques are optimally tuned using the proposed levy flight based tunicate behaviour. To validate the proposed methodology, evaluation metrics are tested and shows superiority of the proposed approach during COVID-19 diagnostic studies.

COVID-19 Sample Pooling: From RNA Extraction to Quantitative Real-time RT-PCR.

The COVID-19 pandemic requires mass screening to identify those infected for isolation and quarantine. Individually screening large populations for the novel pathogen, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is costly and requires a lot of resources. Sample pooling methods improve the efficiency of mass screening and consume less reagents by increasing the capacity of testing and reducing the number of experiments performed, and are therefore especially suitable for under-developed countries with limited resources. Here, we propose a simple, reliable pooling strategy for COVID-19 testing using clinical nasopharyngeal (NP) and/or oropharyngeal (OP) swabs. The strategy includes the pooling of 10 NP/OP swabs for extraction and subsequent testing via quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR), and may also be applied to the screening of other pathogens.

Scenario design for macrofinancial stress testing

This paper provides a novel empirical approach to scenario design for selecting a stress scenario for international macrofinancial variables. The scenario design framework is composed of several building blocks. First, multiple scenarios on the risk factors are generated by simulating a multi-country large Bayesian vector autoregression. Second, we take the perspective of a representative investor who aims to select a severe-yet-plausible scenario for a set of systematic risk factors following a factor-investing strategy. Moreover, we compare the stress scenarios selected under different approaches to measure plausibility (the Mahalanobis distance and entropy pooling under subjective views with a clear economic narrative). Finally, we compare our scenario design approach with a historical scenario approach in terms of its ability to select a stress scenario in the run-up to a rare adverse event such as the Covid-19 pandemic. We give evidence that our framework is suitable for the selection of a proper forward-looking severe-yet-plausible macrofinancial stress scenario.

Deep Rank-Based Average Pooling Network for Covid-19 Recognition

(Aim) To make a more accurate and precise COVID-19 diagnosis system, this study proposed a novel deep rank-based average pooling network (DRAPNet) model, i.e., deep rank-based average pooling network, for COVID-19 recognition. (Methods) 521 subjects yield 1164 slice images via the slice level selection method. All the 1164 slice images comprise four categories: COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control. Our method firstly introduced an improved multiple-way data augmentation. Secondly, an n-cony rank-based average pooling module (NRAPM) was proposed in which rank-based pooling-particularly, rank-based average pooling (RAP)-was employed to avoid overfitting. Third, a novel DRAPNet was proposed based on NRAPM and inspired by the VGG network. Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis. (Results) Our DRAPNet achieved a micro-averaged F1 score of 95.49% by 10 runs over the test set. The sensitivities of the four classes were 95.44%, 96.07%, 94.41%, and 96.07%, respectively. The precisions of four classes were 96.45%, 95.22%, 95.05%, and 95.28%, respectively. The F1 scores of the four classes were 95.94%, 95.64%, 94.73%, and 95.67%, respectively. Besides, the confusion matrix was given. (Conclusions) The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases. The RAP gives better results than four other methods: strided convolution, l(2)-norm pooling, average pooling, and max pooling.

Achieving maximum speedup in multi-level acceleration for massive coronavirus testing

It is well and widely known that sample pooling could provide an effective and efficient way for fast coronavirus testing among massive asymptomatic individuals. The method of multi-level acceleration for asymptomatic COVID-19 screening has been introduced, and for one and two levels, the optimal group sizes have been obtained. However, there are still multiple challenges. First, it is not clear how to find the optimal group sizes for three or more levels. Second, there is lack of closed-form expressions for the optimal group sizes for two or more levels. Third, it is not clear how to determine the optimal number of levels. And last, it is not known what the maximum achievable speedup is. The motivation of this paper is to address all the above challenges. The optimization of a hierarchical pooling strategy includes its number of levels and the group size of each level. In this paper, based on multi-variable optimization and Taylor approximation, we are able to derive closed-form expressions for the optimal number of levels (Formula presented.), the optimal group sizes (Formula presented.), (Formula presented.),…, (Formula presented.), and the maximum possible speedup of a hierarchical pooling strategy of (Formula presented.), where (Formula presented.) is the fraction of infected people. The above speedup is nearly a linear function of the reciprocal of (Formula presented.), in the sense that it is asymptotically greater than any sub-linear function (Formula presented.) of the reciprocal of (Formula presented.) for any small (Formula presented.). Using the results in this paper, we can quickly and easily predict the performance of an optimal hierarchical pooling strategy. For instance, if the fraction of infected people is 0.0001, an 8-level hierarchical pooling strategy can achieve speedup of nearly 400. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Une nouvelle solidarité européenne ?

La solidarité financière européenne en période de crise a été inscrite dans les traités dès les débuts de la construction européenne et a été mise en œuvre à partir des années 1970. Les instruments de solidarité mis en place, adaptés à des crises classiques, se sont avérés insuffisants pour faire face à une crise d'un genre nouveau telle que la crise de la Covid-19. L'Union européenne et l'Union économique et monétaire ont alors usé d'expédients pour manifester une solidarité de circonstance, qui a cependant ouvert la voie à une solidarité renforcée, sur le moyen terme, qui n'apparaît cependant pas pérenne.Alternate : European financial solidarity in times of crisis was enshrined in the treaties from the very beginnings of European construction and was implemented from the 1970s. The instruments of solidarity put in place, adapted to classic crises, have proven to be true insufficient to cope with a crisis of a new kind such as the covid-19 crisis. The European Union and the Economic and Monetary Union then used all the ingredients to show solidarity for the occasion, which however opened the way to reinforced solidarity in the medium term, which does not appear to be sustainable.

A review of pooled-sample strategy: Does complexity lead to better performance?

The outbreak of coronavirus disease 2019 (COVID-19, caused by SARS-Cov-2) is a big challenge for global health systems and the economy. Rapid and accurate tests are crucial at early stages of this pandemic. Reverse transcription-quantitative real-time polymerase chain reaction is the current gold standard method for detection of SARS-Cov-2. It is impractical and costly to test individuals in large-scale population screens, especially in low- and middle-income countries due to their shortage of nucleic acid testing reagents and skilled staff. Accordingly, sample pooling, such as for blood screening for syphilis, is now widely applied to COVID-19. In this paper, we survey and review several different pooled-sample testing strategies, based on their group size, prevalence, testing number, and sensitivity, and we discuss their efficiency in terms of reducing cost and saving time while ensuring sensitivity. © 2022 The Authors. VIEW published by Shanghai Fuji Technology Consulting Co., Ltd, authorized by Professional Community of Experimental Medicine, National Association of Health Industry and Enterprise Management (PCEM) and John Wiley & Sons Australia, Ltd.

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

Background: There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases. Methods: A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases. Results: The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists. Conclusions: This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

Effectiveness Of Pcr Pool Testing For Screening Of Covid-19 Infection In Pakistan.

BACKGROUND: We tested the utility of mini-pool PCR testing for the rational use of PCR consumables in screening for CoViD-19. METHODS: After pilot experiments, 3-samples pool size was selected. One step RT-PCR was performed. The samples in the mini-pool having COVID gene amplification were tested individually. RESULTS: 1548 samples tested in 516 mini-pools resulted 396 mini-pools as negative and 120 as positive. Upon individual testing, 110 samples tested positive and 9 were inconclusive. 876 PCR reactions were performed to test 1548 samples, saving 43% PCR reagents. Centres with low prevalence resulted in most saving on reagents (50%), while centres with high prevalence resulted in more test reactions. Testing of individual samples resulted in delays in reporting. CONCLUSIONS: Pooling can increase lab capacity, however, pooling delays results and cause degradation of samples.

Evaluation of Four Strategies for SARS-CoV-2 Detection: Characteristics and Prospects.

The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed an enormous burden on the global public health system and has had disastrous socioeconomic consequences. Currently, single sampling tests, 20-in-1 pooling tests, nucleic acid point-of-care tests (POCTs), and rapid antigen tests are implemented in different scenarios to detect SARS-CoV-2, but a comprehensive evaluation of them is scarce and remains to be explored. In this study, 3 SARS-CoV-2 inactivated cell culture supernatants were used to evaluate the analytical performance of these strategies. Additionally, 5 recombinant SARS-CoV-2 nucleocapsid (N) proteins were also used for rapid antigen tests. For the wild-type (WT), Delta, and Omicron strains, the lowest inactivated virus concentrations to achieve 100% detection rates of single sampling tests ranged between 1.28 × 102 to 1.02 × 103, 1.28 × 102 to 4.10 × 103, and 1.28 × 102 to 2.05 × 103 copies/mL. The 20-in-1 pooling tests ranged between 1.30 × 102 to 1.04 × 103, 5.19 × 102 to 2.07 × 103, and 2.59 × 102 to 1.04 × 103 copies/mL. The nucleic acid POCTs were all 1.42 × 103 copies/mL. The rapid antigen tests ranged between 2.84 × 105 to 7.14 × 106, 8.68 × 104 to 7.14 × 106, and 1.12 × 105 to 3.57 × 106 copies/mL. For the WT, Delta AY.2, Delta AY.1/AY.3, Omicron BA.1, and Omicron BA.2 recombinant N proteins, the lowest concentrations to achieve 100% detection rates of rapid antigen tests ranged between 3.47 to 142.86, 1.74 to 142.86, 3.47 to 142.86, 3.47 to 142.86, and 5.68-142.86 ng/mL, respectively. This study provided helpful insights into the scientific deployment of tests and recommended the full-scale consideration of the testing purpose, resource availability, cost performance, result rapidity, and accuracy to facilitate a profound pathway toward the long-term surveillance of coronavirus disease 2019 (COVID-19). IMPORTANCE In the study, we reported an evaluation of 4 detection strategies implemented in different scenarios for SARS-CoV-2 detection: single sampling tests, 20-in-1 pooling tests, nucleic acid point-of-care tests, and rapid antigen tests. 3 SARS-CoV-2-inactivated SARS-CoV-2 cell culture supernatants and 5 recombinant SARS-CoV-2 nucleocapsid proteins were used for evaluation. In this analysis, we found that for the WT, Delta, and Omicron supernatants, the lowest concentrations to achieve 100% detection rates of single sampling tests ranged between 1.28 × 102 to 1.02 × 103, 1.28 × 102 to 4.10 × 103, and 1.28 × 102 to 2.05 × 103 copies/mL. The 20-in-1 pooling tests ranged between 1.30 × 102 to 1.04 × 103, 5.19 × 102 to 2.07 × 103, and 2.59 × 102 to 1.04 × 103 copies/mL. The nucleic acid POCTs were all 1.42 × 103 copies/mL. The rapid antigen tests ranged between 2.84 × 105 to 7.14 × 106, 8.68 × 104 to 7.14 × 106, and 1.12 × 105 to 3.57 × 106 copies/mL. For the WT, Delta AY.2, Delta AY.1/AY.3, Omicron BA.1, and Omicron BA.2 recombinant N proteins, the lowest concentrations to achieve 100% detection rates of rapid antigen tests ranged between 3.47 to 142.86, 1.74 to 142.86, 3.47 to 142.86, 3.47 to 142.86, and 5.68 to 142.86 ng/mL, respectively.

COVID-19 Detection Using CNN-ResNet-50 Model

COVID-19 is a deadly virus that originated in 2019 and could be easily transmitted from one geographical area to another. It affected the integral world, resulting in severe mortality due to its contagious effect on human life. The infection rate is continuously growing and it is becoming unmanageable since the virus moves easily from one human to another. Once we detect the COVID-19 virus in its early stages, we can easily reduce the death rate. The most common and widely used method of diagnosing COVID is through reverse transcription polymerase chain (RT-PCR). But the RT-PCR test is time consuming, inaccurate, and expensive. In this situation, the time period for the detection of viruses is valuable. Keeping these limitations in mind, we use an X-ray image of the chest to identify the COVID-19 infected patient. This procedure is achieved by using convolution neural network (CNN) in deep learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Transfer Learning Approach to Detect Face Mask in COVID-19 Pandemic

COVID-19 is tumultuous creating our life so unpredictable. There has no solution of this contagious disease rather than vaccination and prevention. The first and foremost preventative step is using face masks. Face mask can hindrance its droplet from one to another. So this paper has focused the detection of facial mask from image processing using Transfer Learning. For this purpose, total 1376 images have been collected where 690 images of with mask and 686 images of without a mask. Here transfer learning is chosen for the reason of its capability to produce best accurate regardless the limited size of the image dataset. Here, multifarious transfer learning models have been trained to find out the best fitting model. Finally, We have found the VGG16 model with the best accuracy where training accuracy is 98.25% and testing accuracy is 96.38%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Horizontal collaboration in the last mile distribution: gauging managerial response to disruption and abnormal demand

Purpose: Last mile distribution is a crucial element of any supply chain network, and its complexity has challenged established practices and frameworks in the management literature. This is particularly evident when demand surges, as with recent lockdowns due to the COVID-19 pandemic and subsequent demand for home delivery services. Given the importance of this critical component, this study recommends horizontal collaboration as a possible solution for retailers seeking to improve the quality of their services. Design/methodology/approach: This study investigates whether horizontal collaboration should be considered as an option for faster and greener distribution of groceries ordered online. Using the United Kingdom and Greek grocery markets that differ in terms of online grocery penetration, distribution network structure and delivery times, the study discusses how the effectiveness of pooling resources can create positive spillover effects for consumers, businesses and society. Findings: Despite their differences, both markets indicate the need for horizontal collaboration in the highly topical issue of last mile delivery. Originality/value: Taking a theoretical and practical view in cases of disruption and constant pressure in last mile distribution, horizontal collaboration supports retailers to coordinate routes, increase fleet and vehicle utilisation, reduce traffic and carbon emissions while improving customer satisfaction. © 2022, Emerald Publishing Limited.

Evaluation of pooling strategy of SARS-CoV-2 RT-PCR in limited resources setting in Egypt at low prevalence.

Sample pooling testing for SARS-COV-2 can be an effective tool in COVID-19 screening when resources are limited, yet it is important to assess the performance before implementation as pooling has its limitations. Our objective was to assess the efficacy of pooling samples for coronavirus 2019 (COVID-19) compared to an individual analysis by using commercial platforms for nucleic acid testing. A total of 2200 nasopharyngeal swabs for SARS-COV-2 were tested individually and in pools of 4, 8, and 10. The cycle threshold (Ct) values of the positive pooled samples were compared to their corresponding individual positive samples. In pool size 10 samples, an estimated increase of 3-Ct was obtained, which led to false negative results in low viral load positive samples. Pooling SARS COV-2 samples is an effective strategy of screening to increase laboratories' capacity and reduce costs without affecting diagnostic performance. A pool size of 8 is recommended.

CAE-UNet: An Effective Automatic Segmentation Model for CT Images of COVID-19

Since December 2019, COVID-19 has ravaged the world, severely affecting the quality of life and physical health of human society. Computed tomography (CT) imaging is an effective way to detect solid lung lesions as well as pulmonary ground-glass nodules and is an effective way to diagnose COVID-19. The automatic and accurate segmentation of COVID-19 lesion areas from CT images can determine the severity of the disease, which is essential for the diagnosis and treatment of COVID-19. A new model CAE-UNet(Combine-ASPP-ECA-UNet) is proposed in this paper for COVID-19 CT image segmentation based on UNet. The coding structure of UNet is replaced with the improved ResNet50 and incorporated with ECA attention module and atrous spatial pyramid pooling(ASPP). Fusing different sensory fields, global, local and spatial features to enhance the detail segmentation effect of the network. The experimental results on the CC-CCII show that the mIoU of the proposed CAE-UNet reaches 79.53%, which is better than some other mainstream methods. The proposed method achieves automatic and efficient segmentation of COVID-19 CT images. © 2022 IEEE.

CAE-UNet: An Effective Automatic Segmentation Model for CT Images of COVID-19

Since December 2019, COVID-19 has ravaged the world, severely affecting the quality of life and physical health of human society. Computed tomography (CT) imaging is an effective way to detect solid lung lesions as well as pulmonary ground-glass nodules and is an effective way to diagnose COVID-19. The automatic and accurate segmentation of COVID-19 lesion areas from CT images can determine the severity of the disease, which is essential for the diagnosis and treatment of COVID-19. A new model CAE-UNet(Combine-ASPP-ECA-UNet) is proposed in this paper for COVID-19 CT image segmentation based on UNet. The coding structure of UNet is replaced with the improved ResNet50 and incorporated with ECA attention module and atrous spatial pyramid pooling(ASPP). Fusing different sensory fields, global, local and spatial features to enhance the detail segmentation effect of the network. The experimental results on the CC-CCII show that the mIoU of the proposed CAE-UNet reaches 79.53%, which is better than some other mainstream methods. The proposed method achieves automatic and efficient segmentation of COVID-19 CT images. © 2022 IEEE.

Effectiveness of sample pooling strategies for diagnosis of SARS-CoV-2: Specimen pooling vs. RNA elutes pooling.

PURPOSE: The pandemic of SARS-CoV-2 or COVID-19 has hugely created an economic imbalance worldwide. With the exponential increase in the number of cases and to keep in check on the community transmission, there is high demand and acute shortage of diagnostic kits. The pooled-sample strategy turns out to be the promising strategy intended to determine the optimal testing for specimens with limited resources and without losing the test sensitivity and specificity. The study was performed with standard molecular biology graded lab equipment, FDA-approved COVID-19 RNA extraction, and SARS-CoV-2 tests kits. MATERIALS AND METHODS: The study aims to comparatively analyze the pooling strategy of the naso-oropharyngeal specimen sample and RNA extracted from the same patient samples in the pool of 3,5, and 8 with no significant loss in test usability. Another primary focus of the study was detection of low or borderline SARS-CoV-2 positives in the pooling strategy. A total of 300 samples (240 positives and 60 negatives) were tested for 3, 5, and 8 pools of specimen samples and RNA elutes. RESULTS: The comparative analysis determined the sensitivity for three and five pool strategy to be above 98% and eight pool strategy to be 100%. CONCLUSION: The RNA elutes pooling strategy concordance rate is better than that of specimen pooling with 100% specificity. Thus, in the substantial crisis of resources with the global pandemic, pooling approaches for SARS-CoV-2 can be practical in a low prevalence rate of 5%.