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1.
International Review of Financial Analysis ; : 102139, 2022.
Article in English | ScienceDirect | ID: covidwho-1773404

ABSTRACT

This paper studies the tail dependence among carbon prices, green and non-green cryptocurrencies. Using daily closing prices of carbon, green and non-green cryptocurrencies from 2017 to 2021 and a quantile connectedness framework, we find evidence of asymmetric tail dependence among these markets, with stronger dependence during highly volatile periods. Moreover, carbon prices are largely disconnected from cryptocurrencies during periods of low volatilities, while Bitcoin and Ethereum exhibit time-varying spillovers to other markets. Our results also show that green cryptocurrencies are weakly connected to Bitcoin and Ethereum, and their net connectedness are close to 0, except during the COVID-19 pandemic. Finally, we find a significant influence of macroeconomic and financial factors on the tail dependence among carbon, green and non-green cryptocurrency markets. Our results highlight the time-varying diversification benefits across carbon, green and non-green cryptocurrencies and have important implications for investors and policymakers.

2.
Energy Econ ; 109: 105962, 2022 May.
Article in English | MEDLINE | ID: covidwho-1739711

ABSTRACT

With many studies highlighting the heterogeneous impact of the COVID-19 pandemic on different commodity markets, this study provides evidence of quantile connectedness between energy, metals, and agriculture commodity markets before and during the COVID-19 outbreak. Since mean-based measures of connectedness are not necessarily suitable to measure connectedness in the crisis period, especially in the tails of the return distribution, thus in this study, we use the newly developed approach of quantile-based connectedness. The full-sample analysis results show that return shocks only propagate within the energy commodity group. The findings manifest that transmission of return spillovers is stronger in the left and right tails of the conditional return distribution. In addition, the results unveil that degree of tail-dependence between energy, metals, and agriculture commodities are time-varying. Meanwhile, our sub-sample analysis clearly shows that the commodity market return connectedness demonstrates a significant shift over time due to COVID-19 shocks. There is evidence of strong transmission of return shocks between energy, metals, and agriculture commodities during the COVID-19 fiasco. Finally, the results also illustrate that softs and livestock commodities hold significant diversification benefits for energy market investors.

3.
Environ Sci Pollut Res Int ; 2022 Mar 14.
Article in English | MEDLINE | ID: covidwho-1739403

ABSTRACT

Since markets are undergoing severe turbulent economic periods, this study investigates the information transmission of energy stock markets of five regions including North America, South America, Europe, Asia, and Pacific where we differentiated the regional energy markets based on their developing and developed state of economy. We employed time-frequency domain from Jan 1995 to May 2021 and found that energy stocks of developed regions are highly connected. The energy markets of North America, South America, and Europe are the net transmitters of spillovers, whereas the Asian and Pacific energy markets are the net receivers of spillovers. The results also reveal that the connectedness of regional energy markets is time and frequency dependent. Regional energy stocks were highly connected following the Asian financial crisis (AFC), global financial crisis (GFC), European debt crisis (EDC), shale oil revolution (SOR), and COVID-19 pandemic. Time-dependent results reveal that high spillovers formed during stress periods and frequency domain show the higher connectedness of regional energy stock markets in the short run followed by an extreme economic condition. These results have significant implications for policymakers, regulators, investors, and regional controlling bodies to adopt effective strategies during short run to avoid economic downturns and information distortions.

4.
Journal of Medical Internet Research Vol 23(5), 2021, ArtID e23792 ; 23(5), 2021.
Article in English | APA PsycInfo | ID: covidwho-1733103

ABSTRACT

Background: Many previous studies have explored socialization-oriented social media (SM), but their reach has been limited to the context of information exchange for common personal interests. This study focuses on work-oriented SM, which can enhance organizational networking and productivity levels in the context of public hospitals. Objective: This study aims to provide a theoretical framework to explain how the use of SM can enhance the skills of health professionals and levels of organizational productivity in uncertain environments. Methods: A total of 2 distinct forms of data collection techniques were combined: focus groups and semistructured interviews. Both were conducted with doctors and nurses in Saudi public sector hospitals. Results: The findings reveal that the use of SM can create professional socialization at the level of the institution, and this can enhance skills, knowledge, decision making, and the overall level of organizational productivity. The increasing use of SM creates collaboration between health experts (particularly endocrinologists and pulmonologists in this case) who arrange video calls to share best practices in terms of medication, diet, and health care plans for patients with multiple diseases. Many of these patients are particularly vulnerable, given the wider context of the current global pandemic. Conclusions: This study culminates in the Social Media Organizational Productivity model, which provides insights into how SM has increased the accessibility of health professionals through the use of technology. Access to such professionals creates a patient-centric approach and a culture of shared communication for dealing with high-risk patients during the current global pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

5.
Finance Research Letters ; : 102782, 2022.
Article in English | ScienceDirect | ID: covidwho-1719762

ABSTRACT

We estimate the asymmetric time- and frequency connectedness across 11 Asia-Pacific exchange rates using daily data from Jan 1995 to Mar 2021. Our results reveal that in terms of static spillover, Asia-Pacific currencies are mainly disconnected except Australian Dollar and Singapore Dollar during normal times. The currencies form contagions during crisis periods. The currencies form positive and negative clusters during both short and long run. In terms of time-domain spillover, the pattern of daily return connectedness shows that the currencies of developed (emerging) economies are net transmitters (receivers) of shocks. We observe positive short run contagions (devaluation) of sampled currencies during Asian Financial Crisis and COVID19 pandemic. There are negative (appreciation) long run spillovers during Argentinean debt crisis and Chinese financial market crisis and positive long run contagions during Global Financial Crisis. The study carries important implications for policy makers and investors.

6.
Economic Research-Ekonomska Istraživanja ; : 1-19, 2022.
Article in English | Taylor & Francis | ID: covidwho-1703141
7.
Comput Biol Med ; 143: 105298, 2022 Feb 20.
Article in English | MEDLINE | ID: covidwho-1693721

ABSTRACT

The COVID-19 (coronavirus disease 2019) pandemic affected more than 186 million people with over 4 million deaths worldwide by June 2021. The magnitude of which has strained global healthcare systems. Chest Computed Tomography (CT) scans have a potential role in the diagnosis and prognostication of COVID-19. Designing a diagnostic system, which is cost-efficient and convenient to operate on resource-constrained devices like mobile phones would enhance the clinical usage of chest CT scans and provide swift, mobile, and accessible diagnostic capabilities. This work proposes developing a novel Android application that detects COVID-19 infection from chest CT scans using a highly efficient and accurate deep learning algorithm. It further creates an attention heatmap, augmented on the segmented lung parenchyma region in the chest CT scans which shows the regions of infection in the lungs through an algorithm developed as a part of this work, and verified through radiologists. We propose a novel selection approach combined with multi-threading for a faster generation of heatmaps on a Mobile Device, which reduces the processing time by about 93%. The neural network trained to detect COVID-19 in this work is tested with a F1 score and accuracy, both of 99.58% and sensitivity of 99.69%, which is better than most of the results in the domain of COVID diagnosis from CT scans. This work will be beneficial in high-volume practices and help doctors triage patients for the early diagnosis of COVID-19 quickly and efficiently.

8.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-317684

ABSTRACT

Background. Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. Methods. In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. Results. Statistical measures i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for model accuracy. The values of MAPE for the best selected models for confirmed, recovered and deaths cases are 0.407, 0.094 and 0.124 respectively, which falls under the category of highly accurate forecasts. In addition, we computed fifteen days ahead forecast for the daily deaths, recover, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting decision making of evolving short term policies.

9.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-305760

ABSTRACT

Against the backdrop of the Covid-19 pandemic, this study explores the hedging and safe-haven potential of green bonds for conventional equity, fixed income, commodity, and forex investments. We use the cross-quantilogram approach that provides a better understanding of the dynamic relationship between assets under different market conditions. Our full sample results show that the green bond index could serve as a diversifier asset for medium- and long-term equity investors. Besides, it can also serve as a hedging and safe haven instrument for currency and commodity investments. Moreover, the sub-sample analysis of the pandemic crisis period shows a heightened short- and medium-term lead-lag association between the green bond index and conventional investment returns. However, the green bond index emerges as a significant hedging and safe-haven asset for the long-term investors of conventional financial assets. Our results offer insights for long-term investors whose portfolios comprise conventional assets such as equities, commodities, forex, and fixed income securities. Further, our findings reveal the potential role that the green bond investments could play in global financial recovery efforts without compromising the low-carbon transition targets.

10.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-313822

ABSTRACT

Background: Reliable differentiation between uncomplicated and self-limiting acute respiratory tract infections (ARIs) and more severe bacterial respiratory tract infections remains challenging, due to the non-specific clinical manifestations in both systemic bacterial or viral infections. The current COVID-19 pandemic is putting extraordinary strain on healthcare resources. To date, molecular testing is available but has a long turnaround time and therefore cannot provide results at the point-of-care, leading to a delay in results thereby exposing patients to cross-infection and delay in diagnosis (1-3). Methods: We prospectively evaluated the utility of FebriDx®, a point-of-care fingerstick blood test that can differentiate viral from bacterial ARIs through simultaneous detection of both Myxovirus-resistance protein A (MxA) and C-reactive protein (CRP), in rapidly determining viral cases requiring immediate isolation and confirmatory molecular testing, from non-infectious patients or bacterial infections that require antibacterial therapy. Results: 75 consecutive patients were assessed and 48 eligible cases were tested with FebriDx®. Overall, 35 patients had FebriDx® test viral positive. All 35 patients had either positive rt-PCR (n=30) for COVID-19 or clinical picture highly suggestive of COVID-19 infection (PPV of 100% in a pandemic situation)[AB1] . In the 13 cases it was viral negative, rRT-PCR was also negative in all cases. In one case of LRTI, it was not possible to determine the exact cause of infection and a viral infection couldn’t be excluded. Including this patient, the NPV was 12/13 (92%) exceeding the NPV of rRt-PCR at 71% (12/17). Sensitivity was conservatively calculated at 97% (35/36) compared to 85.7% (30[RS2] /35) for rRt-PCR. Similarly the specificity of both FebriDx®and rRt-PCR was 100% (12/12). Conclusions: In the current COVID-19, FebriDx® shows potential as a reliable POC test and a proxy marker of COVID-19 infection amongst inpatients in a secondary care setting. [AB1]35/35 equates to a sensitivity and specificity of 100% for COVID, would you be willing to say that instead of ‘near 100% ppv)? [RS2]I believe PCR was 85.7% (30/35), because PCR only detects the COVID cases

11.
Journal of Chemistry ; 2021, 2021.
Article in English | ProQuest Central | ID: covidwho-1615804

ABSTRACT

Topological indices are numerical numbers assigned to the graph/structure and are useful to predict certain physical/chemical properties. In this paper, we give explicit expressions of novel Banhatti indices, namely, first K Banhatti index B1G, second K Banhatti index B2G, first K hyper-Banhatti index HB1G, second K hyper-Banhatti index HB2G, and K Banhatti harmonic index HbG for hyaluronic acid curcumin and hydroxychloroquine. The multiplicative version of these indices is also computed for these structures.

12.
Economic Analysis and Policy ; 2021.
Article in English | ScienceDirect | ID: covidwho-1587931

ABSTRACT

This paper examines the dynamic and frequency spillovers between global Green Bonds (GBs), WTI oil and G7 stock markets using the time-frequency spillover index by Baruník and Křehlík (2018) and wavelet coherency approach. The results show that the spilllovers is dynamic and crisis-sensitive. Furthermore, adding GBs and oil futures to stock portfolio reduces the spillover size during turmoil periods. The short-term spillovers (up to five trading days) represent the largest proportion of the total spillovers. A significant jump in spillovers is observed in the early of COVID-19 outbreak (March-April 2020). Interestingly, Canada, France, Germany, Italy, and UK are the net transmitters of spillovers, whereas Japan and GBs are the net recipients of the spillovers, irrespective of time horizons. Oil and US stock market shift from net contributors in short term to net receipts in medium and long terms. Wavelet coherence analysis reveals significant co-movements between G7 stock markets and both oil and GBs. The co-movements are more pronounced in both medium and long terms and during COVID-19 spread where both oil and GBs lead stock markets. GBs provide higher diversification benefits to G7 investors than oil in the short-term. The hedging is expensive at the long term for GBs and intermediate term for WTI oil. Finally, the hedge effectiveness of crude oil is higher than GBs, irrespective of time horizons.

13.
J Environ Manage ; 305: 114358, 2022 Mar 01.
Article in English | MEDLINE | ID: covidwho-1587286

ABSTRACT

Green bonds (GB) are gaining a prominent role in sustainable development because of their ability to fund environment-friendly projects. This study aims to investigate if investors can benefit from the risk diversification properties of including GB with other assets, particularly within the context of the ongoing COVID-19 pandemic. To do so, we utilize a quantile-connectedness approach to examine a set of GB and traditional assets, i.e., commodities, stocks, and bonds, from 2008 to 2020. We find higher total time-varying risk spillovers during extreme high volatility periods than those with average and low volatility. For pairwise risk spillovers, GB offers more diversification opportunities when volatility is very low. Nevertheless, the diversification benefits increase during the COVID period. The strong bidirectional risk spillovers between GB and conventional bonds imply that GB can be considered a good alternative to traditional bonds while benefiting from their diversification potential, particularly with energy and agriculture. Our findings are useful for investors wishing to implement green diversification portfolio strategies in extreme volatility periods and act as an encouragement to policymakers to establish efficient policies to promote green finance.


Subject(s)
COVID-19 , Financial Management , Agriculture , Humans , Pandemics , SARS-CoV-2
14.
PeerJ Comput Sci ; 7: e746, 2021.
Article in English | MEDLINE | ID: covidwho-1579902

ABSTRACT

Background: Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. Methods: In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. Results: Statistical measures-Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)-are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.

15.
Journal of International Financial Markets, Institutions and Money ; : 101480, 2021.
Article in English | ScienceDirect | ID: covidwho-1556986

ABSTRACT

Owing to the growing importance of socially responsible investments in the wake of climate change mitigation goals, we estimate the asymmetric time- and frequency-spillovers between global sustainable investments. Additionally, we examine the influence of global risk factors such as US and UK economic policy uncertainties, stock market volatility, US treasury market volatility and infectious diseases related market volatility on the short- and long-run connectedness in these investments. To this end, we use daily returns and volatilities of 14 country-level Dow Jones Sustainability indices from January 2005 to March 2021. By employing the asymmetric versions of Diebold & Yilmaz (2012, 2014) and Barunik & Krehlik (2018) time-frequency connectedness, our study addresses both good and bad contagion among sustainable investments is unexplored in the recent literature. The results reveal significant time-frequency asymmetries in return spillovers across different regions in the short- and long-run. Germany, France, Netherlands, and the UK are the primary transmitters of returns and volatility shocks. We find more intra-regional connectedness among the Asian countries as opposed to inter-regional connectedness. Negative returns propagate more intensely than positive ones, and this contagion is considerably boosted during crises, including the COVID19. The VIX and COVID19 remain influential for financial contagion in the long run. The impact of MOVE is positive in the short-run while negative in the long-run, which shows an overreaction of connectedness to the US treasury market volatility in the short-run. Economic policy uncertainties in the US and the UK increase spillovers more intensely in the short-run. These results are robust to using volatility spillovers, the choice of rolling window and various forecast horizons. Our findings are distinctly important for socially responsible investors as we point out international portfolio diversification opportunities among sustainable investments. Understanding the dynamics of connectedness in sustainable investments can potentially boost financing in this market through portfolio choices and contribute to the climate change mitigation agenda of United Nations.

16.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-296225

ABSTRACT

The COVID-19 (coronavirus disease 2019) pandemic affected more than 186 million people with over 4 million deaths worldwide by June 2021. The magnitude of which has strained global healthcare systems. Chest Computed Tomography (CT) scans have a potential role in the diagnosis and prognostication of COVID-19. Designing a diagnostic system which is cost-efficient and convenient to operate on resource-constrained devices like mobile phones would enhance the clinical usage of chest CT scans and provide swift, mobile, and accessible diagnostic capabilities. This work proposes developing a novel Android application that detects COVID-19 infection from chest CT scans using a highly efficient and accurate deep learning algorithm. It further creates an attention heatmap, augmented on the segmented lung parenchyma region in the CT scans through an algorithm developed as a part of this work, which shows the regions of infection in the lungs. We propose a selection approach combined with multi-threading for a faster generation of heatmaps on Android Device, which reduces the processing time by about 93%. The neural network trained to detect COVID-19 in this work is tested with F1 score and accuracy, both of 99.58% and sensitivity of 99.69%, which is better than most of the results in the domain of COVID diagnosis from CT scans. This work will be beneficial in high volume practices and help doctors triage patients in the early diagnosis of the COVID-19 quickly and efficiently.

17.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-295568

ABSTRACT

The present study aims to configure the extreme quantile dependence between oil shocks and BRIC markets from January 2, 1995 to July 27, 2021. Using the cross-quantilogram technique, the current study first decomposed oil shocks pertaining to demand and supply and analyzed their asymmetric impact on BRIC markets. Our findings manifest positive and persistent dependencies between oil demand shocks and BRIC markets. Meanwhile, substantial cross-quantile dependence is demonstrated among shocks in oil supply and the stock returns of Russia. The recursive cross-quantilogram analysis indicates time-varying characteristics reiterating that oil demand shocks are positively and significantly correlated with BRIC stock returns, particularly after the Global Financial Crisis and COVID-19 pandemic. However, weaker dependencies are observed in the normal market conditions in the absence of financial contagion. Finally, after controlling the impact of idiosyncratic risk shocks, our results remain robust. Our findings are of particular prominence for policymakers, investors, and financial market constituents to restructure their current policies and strategies for avoiding uncertainty in the stock returns.

18.
Professional Medical Journal ; 28(11):1616-1620, 2021.
Article in English | Academic Search Complete | ID: covidwho-1543136

ABSTRACT

Objective: To assess various clinical and epidemiological characteristics of pediatric and adolescent patients of COVID-19 of Bahawalpur division, to improve their outcome and management. Study design: Descriptive Cohort study. Setting: Department of Pediatrics, Civil Hospital Bahawalpur. Period: 1st March to 30th July 2020. Material & Methods: Data of patients was recovered from hospital record. Data of variables like age, gender, rural or urban living area, symptomatology and need for hospitalization was collected from hospital record. Results: Out of total 516 diagnosed COVID-19 patients, 5.4% patients were of age less than 20 years, 32% from birth to 5 years, 20% from 6 to 10 years of age, 21.4% were 11 to 15 years of age, 28.5% were 16 to 20 years age group. 57.1% were male and 42.8% were female. 42.8% were asymptomatic, 32% patient had respiratory symptoms, 25% had Gastro-intestinal symptoms. 64.2% belonged to urban territory of living, 35.7% belonged to rural. 81.2% patients fall in mild category and 18.7% in moderate. Mortality was Null. Hospitalization was needed in 53.5%, while 46.5% were home quarantined. Mean duration of hospital stay was 14+1 days. Conclusion: Pediatric and adolescent patients have mild to moderate disease severity leading to better outcome of the disease. [ FROM AUTHOR] Copyright of Professional Medical Journal is the property of Professional Medical Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

19.
Energy ; : 122702, 2021.
Article in English | ScienceDirect | ID: covidwho-1531223

ABSTRACT

Motivated by lack of empirical research on volatility linkages among clean-energy stock markets and fossil fuel markets during the recent Covid-19 pandemic, the study examines the volatility connectedness network among clean-energy stocks and fossil fuels such as crude WTI, natural gas, gas oil, and fuel oil. In addition, we also compare the influence of financial crises such as the Global Financial Crisis (GFC), oil crisis, and Covid-19 pandemic crisis is driving the volatility connectedness network of energy markets. We apply Diebold and Yilmaz (2012) [1] time-domain and Barunik and Krehlik (Baruník and Křehlík, 2018) [2] frequency-domain approach. The empirical results uncover weak volatility connections among clean-energy stocks and fossil fuel markets. Also, the empirical results unveil weak volatility linkages among clean energy stocks and conventional energy markets. Meanwhile, we find strong volatility interconnectedness between petroleum markets. Further, the results show that most of the volatility spillovers among energy markets persist in the short-run, whereas the findings display weak volatility transmission among the sample markets in the long run. Furthermore, the findings also unveil that contagion effects between the energy markets increase in the crisis periods, intensifying the volatility interlinkages among the sample energy markets. The findings have important significance for energy policymakers and investors.

20.
Travel Med Infect Dis ; 44: 102169, 2021.
Article in English | MEDLINE | ID: covidwho-1505906

ABSTRACT

BACKGROUND: /Aims: Corona virus disease 2019 (COVID 19) is a pandemic infectious disease of 2020, which often presents with respiratory and gastrointestinal symptoms. The behavior of the virus and its full clinical picture has not been fully studied yet. Many case reports and case series have been running in order to elaborate different presentations and associations. Pulmonary and gastrointestinal features of COVID-19 infection are well outlined; however, neurological manifestations are less defined. CASE PRESENTATION: We report two adult cases of COVID-19 infection presented with acute Guillain-Barre Syndrome (GBS), and a literature review on the causal association between COVID-19 and GBS. CONCLUSION: Our two case reports in addition to literature review of 116 published cases may help offer insight into the clinical course of COVID-19 infection. Our two COVID-19 patients presented with neurological manifestations of GBS which were not preceded with any respiratory, gastrointestinal or other systemic infection. This leads us to raise the possibility of establish direct causal association between COVID-19 infection and GBS. Physicians should have high clinical suspicions when encounter GBS patient during the current COVID-19 pandemic and consider co-existence of COVID-19 infection that may warrant SARS-CoV-2 testing, isolation precautions, and specific treatment for Covid-19 infection.


Subject(s)
COVID-19 , Guillain-Barre Syndrome , Adult , COVID-19 Testing , Guillain-Barre Syndrome/diagnosis , Guillain-Barre Syndrome/epidemiology , Humans , Pandemics , SARS-CoV-2
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