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Fulltext Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

Albahri AS, Hamid RA, Alwan JK, Al-Qays ZT, Zaidan AA, Zaidan BB, et al.

J Med Syst, 2020 May 25;44(7):122.
PMID: 32451808 DOI: 10.1007/s10916-020-01582-x

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Matched MeSH terms: Pneumonia, Viral/diagnosis*
Fulltext Diagnostic techniques for COVID-19 and new developments

Sheikhzadeh E, Eissa S, Ismail A, Zourob M

Talanta, 2020 Dec 01;220:121392.
PMID: 32928412 DOI: 10.1016/j.talanta.2020.121392

COVID-19 pandemic is a serious global health issue today due to the rapid human to human transmission of SARS-CoV-2, a new type of coronavirus that causes fatal pneumonia. SARS -CoV-2 has a faster rate of transmission than other coronaviruses such as SARS and MERS and until now there are no approved specific drugs or vaccines for treatment. Thus, early diagnosis is crucial to prevent the extensive spread of the disease. The reverse transcription-polymerase chain reaction (RT-PCR) is the most routinely used method until now to detect SARS-CoV-2 infections. However, several other faster and accurate assays are being developed for the diagnosis of COVID-19 aiming to control the spread of infection through the identification of patients and immediate isolation. In this review, we will discuss the various detection methods of the SARS-CoV-2 virus including the recent developments in immunological assays, amplification techniques as well as biosensors.

Matched MeSH terms: Pneumonia, Viral/diagnosis*
Fulltext Vesicular drug delivery systems as theranostics in COVID-19

Satija S, Mehta M, Sharma M, Prasher P, Gupta G, Chellappan DK, et al.

Future Med Chem, 2020 09;12(18):1607-1609.
PMID: 32589055 DOI: 10.4155/fmc-2020-0149
Matched MeSH terms: Pneumonia, Viral/diagnosis*
Fulltext Providing a laboratory diagnostic service for pandemic SARS-CoV-2 in a developing country

Sam IC, Chong J, Kamarudin R, Jafar FL, Lee LM, Bador MK, et al.

Trans R Soc Trop Med Hyg, 2020 08 01;114(8):553-555.
PMID: 32497211 DOI: 10.1093/trstmh/traa037
Matched MeSH terms: Pneumonia, Viral/diagnosis*
COVID-19 - How prepared are emergency departments in embracing the new norm?

Iqhbal KM, Ahmad NH

Med J Malaysia, 2020 09;75(5):585-586.
PMID: 32918431

No abstract provided.

Matched MeSH terms: Pneumonia, Viral/diagnosis
Fulltext Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models

Singh S, Murali Sundram B, Rajendran K, Boon Law K, Aris T, Ibrahim H, et al.

J Infect Dev Ctries, 2020 09 30;14(9):971-976.
PMID: 33031083 DOI: 10.3855/jidc.13116

INTRODUCTION: The novel coronavirus infection has become a global threat affecting almost every country in the world. As a result, it has become important to understand the disease trends in order to mitigate its effects. The aim of this study is firstly to develop a prediction model for daily confirmed COVID-19 cases based on several covariates, and secondly, to select the best prediction model based on a subset of these covariates.
METHODOLOGY: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase).
RESULTS: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model.
CONCLUSIONS: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.

Matched MeSH terms: Pneumonia, Viral/diagnosis
Fulltext A call for a global COVID-19 Neuro Research Coalition

Winkler AS, Knauss S, Schmutzhard E, Leonardi M, Padovani A, Abd-Allah F, et al.

Lancet Neurol, 2020 06;19(6):482-484.
PMID: 32470416 DOI: 10.1016/S1474-4422(20)30150-2
Matched MeSH terms: Pneumonia, Viral/diagnosis*
Fulltext A novel strategy for community screening of SARS-CoV-2 (COVID-19): Sample pooling method

Lim KL, Johari NA, Wong ST, Khaw LT, Tan BK, Chan KK, et al.

PLoS One, 2020;15(8):e0238417.
PMID: 32857823 DOI: 10.1371/journal.pone.0238417

The rapid global spread of the coronavirus disease (COVID-19) has inflicted significant health and socioeconomic burden on affected countries. As positive cases continued to rise in Malaysia, public health laboratories experienced an overwhelming demand for COVID-19 screening. The confirmation of positive cases of COVID-19 has solely been based on the detection of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) using real-time reverse transcription polymerase chain reaction (qRT-PCR). In efforts to increase the cost-effectiveness and efficiency of COVID-19 screening, we evaluated the feasibility of pooling clinical Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens during nucleic acid extraction without a reduction in sensitivity of qRT-PCR. Pools of 10 specimens were extracted and subsequently tested by qRT-PCR according to the WHO-Charité protocol. We demonstrated that the sample pooling method showed no loss of sensitivity. The effectiveness of the pooled testing strategy was evaluated on both retrospective and prospective samples, and the results showed a similar detection sensitivity compared to testing individual sample alone. This study demonstrates the feasibility of using a pooled testing strategy to increase testing capacity and conserve resources, especially when there is a high demand for disease testing.

Matched MeSH terms: Pneumonia, Viral/diagnosis*
Upper respiratory tract sampling in COVID-19

Mawaddah A, Gendeh HS, Lum SG, Marina MB

Malays J Pathol, 2020 Apr;42(1):23-35.
PMID: 32342928

INTRODUCTION: To review the present literature on upper respiratory tract sampling in COVID-19 and provide recommendations to improve healthcare practices and directions in future studies.
METHODS: Twelve relevant manuscripts were sourced from a total of 7288 search results obtained using PubMed, Medline and Google Scholar. The search keywords used were COVID-19, nasopharyngeal, oropharyngeal, swabs, SARS and CoV2. Original manuscripts were obtained and analysed by all authors. The review included manuscripts which have not undergone rigorous peer-review process in view of the magnitude of the topic discussed.
RESULTS: The viral load of SARS-CoV-2 RNA in the upper respiratory tract was significantly higher during the first week and peaked at 4-6 days after onset of symptoms, during which it can be potentially sampled. Nasopharyngeal swab has demonstrated higher viral load than oropharyngeal swab, where the difference in paired samples is best seen at 0-9 days after the onset of illness. Sensitivity of nasopharyngeal swab was higher than oropharyngeal swabs in COVID-19 patients. Patient self-collected throat washing has been shown to contain higher viral load than nasopharyngeal or oropharyngeal swab, with significantly higher sensitivity when compared with paired nasopharyngeal swab.
RECOMMENDATIONS: Routine nasopharyngeal swab of suspected COVID-19 infection should take anatomy of the nasal cavity into consideration to increase patient comfort and diagnostic yield. Routine oropharyngeal swab should be replaced by throat washing which has demonstrated better diagnostic accuracy, and it is safe towards others.

Matched MeSH terms: Pneumonia, Viral/diagnosis*
Challenges of Covid-19 testing

Tan GC, Cheong SK

Malays J Pathol, 2020 Apr;42(1):1.
PMID: 32342925

No abstract available.

Matched MeSH terms: Pneumonia, Viral/diagnosis*
Fulltext Nipah@20: Lessons Learned from Another Virus with Pandemic Potential

Gómez Román R, Wang LF, Lee B, Halpin K, de Wit E, Broder CC, et al.

mSphere, 2020 07 08;5(4).
PMID: 32641430 DOI: 10.1128/mSphere.00602-20

Nipah disease is listed as one of the WHO priority diseases that pose the greatest public health risk due to their epidemic potential. More than 200 experts from around the world convened in Singapore last year to mark the 20th anniversary of the first Nipah virus outbreaks in Malaysia and Singapore. Most of these experts are now involved in responding to the coronavirus disease 2019 (COVID-19) pandemic. Here, members of the Organizing Committee of the 2019 Nipah Virus International Conference review highlights from the Nipah@20 Conference and reflect on key lessons learned from Nipah that could be applied to the understanding of the COVID-19 pandemic and to preparedness against future emerging infectious diseases (EIDs) of pandemic potential.

Matched MeSH terms: Pneumonia, Viral/diagnosis
Fulltext An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

Serena Low WC, Chuah JH, Tee CATH, Anis S, Shoaib MA, Faisal A, et al.

Comput Math Methods Med, 2021;2021:5528144.
PMID: 34194535 DOI: 10.1155/2021/5528144

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.

Matched MeSH terms: Pneumonia, Viral/diagnosis
Fulltext Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A

Comput Biol Med, 2020 Jun;121:103795.
PMID: 32568676 DOI: 10.1016/j.compbiomed.2020.103795

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.

Matched MeSH terms: Pneumonia, Viral/diagnosis*
Fulltext Restructuring burns management during the COVID-19 pandemic: A Malaysian experience

Ilenghoven D, Hisham A, Ibrahim S, Mohd Yussof SJ

Burns, 2020 08;46(5):1236-1239.
PMID: 32471558 DOI: 10.1016/j.burns.2020.05.008
Matched MeSH terms: Pneumonia, Viral/diagnosis
COVID-19 detected from targeted contact tracing, attempting to see the pattern in random happenings: early lessons in Malaysia

Ng BH, Andrea YLB, Nuratiqah NA, Faisal AH, Soo CI, Najma K, et al.

Med J Malaysia, 2020 09;75(5):582-584.
PMID: 32918430

The world feels strange as we face what is for most of us our first ever pandemic. The number of newly diagnosed cases rises daily in many parts of the world, and we are faced with the reality that there are still many things to learn about this new disease. We share here our experience of treating our first 199 COVID-19 patients in the Hospital Canselor Tuanku Muhriz, Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM).

Matched MeSH terms: Pneumonia, Viral/diagnosis*
Pharmacotherapeutic considerations for the management of cardiovascular diseases among hospitalized COVID-19 patients

Kow CS, Thiruchelvam K, Hasan SS

Expert Rev Cardiovasc Ther, 2020 Aug;18(8):475-485.
PMID: 32700573 DOI: 10.1080/14779072.2020.1797492

INTRODUCTION: Cardiovascular diseases (CVDs) are among the most frequently identified comorbidities in hospitalized patients with COVID-19. Patients with CV comorbidities are typically prescribed with long-term medications. We reviewed the management of co-medications prescribed for CVDs among hospitalized COVID-19 patients.
AREAS COVERED: There is no specific contraindication or caution related to COVID-19 on the use of antihypertensives unless patients develop severe hypotension from septic shock where all antihypertensives should be discontinued or severe hyperkalemia in which continuation of renin-angiotensin system inhibitors is not desired. The continuation of antiplatelet or statin is not desired when severe thrombocytopenia or severe transminitis develop, respectively. Patients with atrial fibrillation receiving oral anticoagulants, particularly those who are critically ill, should be considered for substitution to parenteral anticoagulants.
EXPERT OPINION: An individualized approach to medication management among hospitalized COVID-19 patients with concurrent CVDs would seem prudent with attention paid to changes in clinical conditions and medications intended for COVID-19. The decision to modify prescribed long-term CV medications should be entailed by close follow-up to check if a revision on the decision is needed, with resumption of any long-term CV medication before discharge if it is discontinued during hospitalization for COVID-19, to ensure continuity of care.

Matched MeSH terms: Pneumonia, Viral/diagnosis
Fulltext Perception of Health Conditions and Test Availability as Predictors of Adults' Mental Health during the COVID-19 Pandemic: A Survey Study of Adults in Malaysia

Dai H, Zhang SX, Looi KH, Su R, Li J

Int J Environ Res Public Health, 2020 07 30;17(15).
PMID: 32751459 DOI: 10.3390/ijerph17155498

Research identifying adults' mental health during the coronavirus disease 2019 (COVID-19) pandemic relies solely on demographic predictors without examining adults' health condition as a potential predictor. This study aims to examine individuals' perception of health conditions and test availability as potential predictors of mental health-insomnia, anxiety, depression, and distress-during the COVID-19 pandemic. An online survey of 669 adults in Malaysia was conducted during 2-8 May 2020, six weeks after the Movement Control Order (MCO) was issued. We found adults' perception of health conditions had curvilinear relationships (horizontally reversed J-shaped) with insomnia, anxiety, depression, and distress. Perceived test availability for COVID-19 also had curvilinear relationships (horizontally reversed J-shaped) with anxiety and depression. Younger adults reported worse mental health, but people from various religions and ethnic groups did not differ significantly in reported mental health. The results indicated that adults with worse health conditions had more mental health problems, and the worse degree deepened for unhealthy people. Perceived test availability negatively predicted anxiety and depression, especially for adults perceiving COVID-19 test unavailability. The significant predictions of perceived health condition and perceived COVID-19 test availability suggest a new direction for the literature to identify the psychiatric risk factors directly from health-related variables during a pandemic.

Matched MeSH terms: Pneumonia, Viral/diagnosis*
Fulltext Passengers' destinations from China: low risk of Novel Coronavirus (2019-nCoV) transmission into Africa and South America

Haider N, Yavlinsky A, Simons D, Osman AY, Ntoumi F, Zumla A, et al.

Epidemiol Infect, 2020 02 26;148:e41.
PMID: 32100667 DOI: 10.1017/S0950268820000424

Novel Coronavirus (2019-nCoV [SARS-COV-2]) was detected in humans during the last week of December 2019 at Wuhan city in China, and caused 24 554 cases in 27 countries and territories as of 5 February 2020. The objective of this study was to estimate the risk of transmission of 2019-nCoV through human passenger air flight from four major cities of China (Wuhan, Beijing, Shanghai and Guangzhou) to the passengers' destination countries. We extracted the weekly simulated passengers' end destination data for the period of 1-31 January 2020 from FLIRT, an online air travel dataset that uses information from 800 airlines to show the direct flight and passengers' end destination. We estimated a risk index of 2019-nCoV transmission based on the number of travellers to destination countries, weighted by the number of confirmed cases of the departed city reported by the World Health Organization (WHO). We ranked each country based on the risk index in four quantiles (4th quantile being the highest risk and 1st quantile being the lowest risk). During the period, 388 287 passengers were destined for 1297 airports in 168 countries or territories across the world. The risk index of 2019-nCoV among the countries had a very high correlation with the WHO-reported confirmed cases (0.97). According to our risk score classification, of the countries that reported at least one Coronavirus-infected pneumonia (COVID-19) case as of 5 February 2020, 24 countries were in the 4th quantile of the risk index, two in the 3rd quantile, one in the 2nd quantile and none in the 1st quantile. Outside China, countries with a higher risk of 2019-nCoV transmission are Thailand, Cambodia, Malaysia, Canada and the USA, all of which reported at least one case. In pan-Europe, UK, France, Russia, Germany and Italy; in North America, USA and Canada; in Oceania, Australia had high risk, all of them reported at least one case. In Africa and South America, the risk of transmission is very low with Ethiopia, South Africa, Egypt, Mauritius and Brazil showing a similar risk of transmission compared to the risk of any of the countries where at least one case is detected. The risk of transmission on 31 January 2020 was very high in neighbouring Asian countries, followed by Europe (UK, France, Russia and Germany), Oceania (Australia) and North America (USA and Canada). Increased public health response including early case recognition, isolation of identified case, contract tracing and targeted airport screening, public awareness and vigilance of health workers will help mitigate the force of further spread to naïve countries.

Matched MeSH terms: Pneumonia, Viral/diagnosis
Diagnostic performance of COVID-19 serology assays

Zainol Rashid Z, Othman SN, Abdul Samat MN, Ali UK, Wong KK

Malays J Pathol, 2020 Apr;42(1):13-21.
PMID: 32342927

INTRODUCTION: The World Health Organization (WHO) declared COVID-19 outbreak as a world pandemic on 12th March 2020. Diagnosis of suspected cases is confirmed by nucleic acid assays with real-time PCR, using respiratory samples. Serology tests are comparatively easier to perform, but their utility may be limited by the performance and the fact that antibodies appear later during the disease course. We aimed to describe the performance data on serological assays for COVID-19.
MATERIALS AND METHODS: A review of multiple reports and kit inserts on the diagnostic performance of rapid tests from various manufacturers that are commercially available were performed. Only preliminary data are available currently.
RESULTS: From a total of nine rapid detection test (RDT) kits, three kits offer total antibody detection, while six kits offer combination SARS-CoV-2 IgM and IgG detection in two separate test lines. All kits are based on colloidal gold-labeled immunochromatography principle and one-step method with results obtained within 15 minutes, using whole blood, serum or plasma samples. The sensitivity for both IgM and IgG tests ranges between 72.7% and 100%, while specificity ranges between 98.7% to 100%. Two immunochromatography using nasopharyngeal or throat swab for detection of COVID-19 specific antigen are also reviewed.
CONCLUSIONS: There is much to determine regarding the value of serological testing in COVID-19 diagnosis and monitoring. More comprehensive evaluations of their performance are rapidly underway. The use of serology methods requires appropriate interpretations of the results and understanding the strengths and limitations of such tests.

Matched MeSH terms: Pneumonia, Viral/diagnosis*
Fulltext Clinical characteristics of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV2) patients in Hospital Tengku Ampuan Afzan

Soh TV, Dzawani M, Noorlina N, Nik F, Norazmi A

Med J Malaysia, 2020 09;75(5):479-484.
PMID: 32918413

BACKGROUND: The COVID-19 is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aims to describe the clinical characteristics of COVID-19 patients admitted to Hospital Tengku Ampuan Afzan (HTAA), Pahang, Malaysia and to identify the clinical and laboratory markers for severe disease, complications and virologic clearance according to clinical staging.

METHODS: This was a single-centre, retrospective, descriptive study. All COVID-19 patients admitted to HTAA from March 9 to April 15, 2020, were included in the study. Patients were categorised according to clinical staging. Data obtained from the medical report includes baseline characteristics of patients, comorbidities, presenting symptoms, laboratory findings, treatments, complications, and outcomes.

RESULTS: Of the total of 247 patients hospitalised, the majority consisted at clinical-stage 1 (43%) and stage 2 (39%) disease. Older patients, diabetes mellitus, hypertension, cardiovascular diseases, and chronic kidney disease were found more common among patients with severe disease. Fever was uncommon and the majority had normal haemoglobin levels, white cell counts, and platelet counts. C-reactive protein (CRP) was found statistically significant to predict pneumonia or hypoxia at a cut-off value of 14mg/L (sensitivity 73.8%, specificity 91.3%) and 50mg/L (sensitivity 100%, specificity 96.4%) respectively. Pneumonia was mostly diagnosed radiologically using chest radiography, especially among clinical stage 3. Acute kidney injury (AKI) was a significant complication, with 31% of clinical stage 3 and above developed AKI and 44% of them requiring haemodialysis. Median virologic clearance time was 15 days from onset of illness, and asymptomatic patients had longer clearance time.

CONCLUSION: COVID-19 presented with a wide spectrum of clinical patterns. CRP was a valuable predictor of severe disease. In this study risk and severity of acute kidney injury were found to be higher. A longer duration of virologic clearance was observed among the asymptomatic patients.

Matched MeSH terms: Pneumonia, Viral/diagnosis*

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