SARS-CoV-2 in Pregnancy-The First Wave.

Background and Objectives: COVID-19, a disease caused by SARS-CoV-2, is a public health emergency. Data on the effect of the virus on pregnancy are limited. Materials and Methods: We carried out a retrospective descriptive study, in order to evaluate the obstetric results on pregnant women in which SARS-CoV-2 was detected through RT-PCR of the nasopharyngeal swab, at admission to the maternity hospital. Results: From 16 March to 31 July 2020, 12 SARS-CoV-2 positive pregnant women have been hospitalized. Eleven were hospitalized for initiation or induction of labor, corresponding to 0.64% of deliveries in the maternity hospital. One pregnant woman was hospitalized for threatened abortion, culminating in a stillbirth at 20 weeks of gestation. Regarding the severity of the disease, nine women were asymptomatic and three had mild illness (two had associated cough and one headache). Three had relevant environmental exposure and a history of contact with infected persons. None had severe or critical illness due to SARS-CoV-2. There were no maternal deaths. The following gestational complications were observed: one stillbirth, one preterm labor, one preterm prelabor rupture of membranes, and one fetal growth restriction. Four deliveries were eutocic, two vacuum-assisted deliveries and five were cesarean sections. The indications for cesarean section were obstetric. Conclusions: SARS-CoV-2 infection was found in a minority of hospitalized pregnant women in this sample. Most are asymptomatic or have mild illness, from gestational complications to highlight stillbirth and preterm birth. There were no cases of vertical transmission by coronavirus.

Initial Observations of COVID-19 in US Children.

Coronavirus disease (COVID-19) has affected children differently from adults worldwide. Data on the clinical presentation of the infection in children are limited. We present a detailed account of pediatric inpatients infected with severe acute respiratory syndrome coronavirus 2 virus at our institution during widespread local transmission, aiming to understand disease presentation and outcomes. A retrospective chart review was performed of children, ages 0 to 18 years, with a positive polymerase chain reaction test for severe acute respiratory syndrome coronavirus 2 on nasopharyngeal specimens admitted to our hospital over a 4-week period. We present clinical data from 22 patients and highlight the variability of the presentation. In our study, most children presented without respiratory illness or symptoms suggestive of COVID-19; many were identified only because of universal testing. Because children may have variable signs and symptoms of COVID-19 infection, targeted testing may miss some cases.

Confronting COVID-19-associated cough and the post-COVID syndrome: role of viral neurotropism, neuroinflammation, and neuroimmune responses.

Cough is one of the most common presenting symptoms of COVID-19, along with fever and loss of taste and smell. Cough can persist for weeks or months after SARS-CoV-2 infection, often accompanied by chronic fatigue, cognitive impairment, dyspnoea, or pain-a collection of long-term effects referred to as the post-COVID syndrome or long COVID. We hypothesise that the pathways of neurotropism, neuroinflammation, and neuroimmunomodulation through the vagal sensory nerves, which are implicated in SARS-CoV-2 infection, lead to a cough hypersensitivity state. The post-COVID syndrome might also result from neuroinflammatory events in the brain. We highlight gaps in understanding of the mechanisms of acute and chronic COVID-19-associated cough and post-COVID syndrome, consider potential ways to reduce the effect of COVID-19 by controlling cough, and suggest future directions for research and clinical practice. Although neuromodulators such as gabapentin or opioids might be considered for acute and chronic COVID-19 cough, we discuss the possible mechanisms of COVID-19-associated cough and the promise of new anti-inflammatories or neuromodulators that might successfully target both the cough of COVID-19 and the post-COVID syndrome.

Risk Factors Associated With COVID-19 Transmission Among US Air Force Trainees in a Congregant Setting.

Importance: Owing to concerns of coronavirus disease 2019 (COVID-19) outbreaks, many congregant settings are forced to close when cases are detected because there are few data on the risk of different markers of transmission within groups. Objective: To determine whether symptoms and laboratory results on the first day of COVID-19 diagnosis are associated with development of a case cluster in a congregant setting. Design, Setting, and Participants: This cohort study of trainees with COVID-19 from May 11 through August 24, 2020, was conducted at Joint Base San Antonio-Lackland, the primary site of entry for enlistment in the US Air Force. Symptoms and duration, known contacts, and cycle threshold for trainees diagnosed by reverse transcription-polymerase chain reaction were collected. A cycle threshold value represents the number of nucleic acid amplification cycles that occur before a specimen containing the target material generates a signal greater than the predetermined threshold that defines positivity. Cohorts with 5 or more individuals with COVID-19 infection were defined as clusters. Participants included 10 613 trainees divided into 263 parallel cohorts of 30 to 50 people arriving weekly for 7 weeks of training. Exposures: All trainees were quarantined for 14 days on arrival. Testing was performed on arrival, on day 14, and anytime during training when indicated. Protective measures included universal masking, physical distancing, and rapid isolation of trainees with COVID-19. Main Outcomes and Measures: Association between days of symptoms, specific symptoms, number of symptoms, or cycle threshold values of individuals diagnosed with COVID-19 via reverse transcription-polymerase chain reaction and subsequent transmission within cohorts. Results: In this cohort study of 10 613 US Air Force basic trainees in 263 cohorts, 403 trainees (3%) received a diagnosis of COVID-19 in 129 cohorts (49%). Among trainees with COVID-19 infection, 318 (79%) were men, and the median (interquartile range [IQR]) age was 20 (19-23) years; 204 (51%) were symptomatic, and 199 (49%) were asymptomatic. Median (IQR) cycle threshold values were lower in symptomatic trainees compared with asymptomatic trainees (21.2 [18.4-27.60] vs 34.8 [29.3-37.4]; P < .001). Cohorts with clusters of individuals with COVID-19 infection were predominantly men (204 cohorts [89%] vs 114 cohorts [64%]; P < .001), had more symptomatic trainees (146 cohorts [64%] vs 53 cohorts [30%]; P < .001), and had more median (IQR) symptoms per patient (3 [2-5] vs 1 [1-2]; P < .001) compared with cohorts without clusters. Within cohorts, subsequent development of clusters of 5 or more individuals with COVID-19 infection compared with those that did not develop clusters was associated with cohorts that had more symptomatic trainees (31 of 58 trainees [53%] vs 43 of 151 trainees [28%]; P = .001) and lower median (IQR) cycle threshold values (22.3 [18.4-27.3] vs 35.3 [26.5-37.8]; P < .001). Conclusions and Relevance: In this cohort study of US Air Force trainees living in a congregant setting during the COVID-19 pandemic, higher numbers of symptoms and lower cycle threshold values were associated with subsequent development of clusters of individuals with COVID-19 infection. These values may be useful if validated in future studies.

Description of Epidemiological Features, Symptoms and Mortality of the Patients with COVID-19 in Some Provinces of Iran.

BACKGROUND: Clinical manifestations of COVID-19 are different. There are some risk factors for COVID-19. This study aimed to describe the epidemiological features, symptoms and mortality of the patients with COVID-19 in Iran. METHODS: This were a cohort study performed on 103,179 patients with COVID-19. The demographic and clinical data were collected in selected provinces. The required data of all patients was extracted from the COVID registry system and analyzed using STATA version 14 and Excel 2016. RESULTS: The mean age was 52.40 years for men and 52.41 years for women. About 55.2% of the study population were male and 44.8% were female. Totally, 60.9% (5085) of deaths happened in men and 39.1% (3263) in women. The mean time from onset of symptoms to hospitalization in men and women were 3.47 and 3.48 days, respectively. The mean time from onset of symptoms to isolation was 2.81 days in men and was 2.87 days in women, from onset of symptoms to death was 9.29 and 9.54 days, respectively, from onset of symptoms to discharge was 7.47 and 7.39 days, and from hospitalization to death was 6.76 and 7.05 days. Cough and shortness of breath were the most common symptoms in the patients. CONCLUSION: According to the results, the overall mortality rate was higher in men than women. Women with cardiovascular disease and diabetes were more likely to die. The mean time from onset of symptoms to hospitalization, isolation, and discharge was similar in men and women.

Identifying individuals with recent COVID-19 through voice classification using deep learning.

Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an 'ah' sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic 'ah' sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.

Other Primary Headache Disorders.

PURPOSE: This article provides an overview of a diverse group of primary headache disorders that are categorized in the International Classification of Headache Disorders, 3rd Edition (ICHD-3), as "other primary headache disorders." This article provides clinicians with a distilled understanding of the diagnoses and their epidemiology, pathophysiology, and management. RECENT FINDINGS: Cough-induced headache requires neuroimaging to exclude posterior fossa pathology and recently has been reported as a common symptom in patients with CSF-venous fistula. Clinical overlap is observed between patients with primary exercise headache and primary headache associated with sexual activity. Patients with recurrent thunderclap headache associated with sexual activity should be presumed to have reversible cerebral vasoconstriction syndrome until proven otherwise. De novo external-pressure headache is a common sequela among health care workers using personal protective equipment during the COVID-19 pandemic. New daily persistent headache is an important mimicker of chronic migraine or chronic tension-type headache and is distinguished by a daily-from-onset progression of persistent headache; a treatment-refractory course is often observed, and early involvement of a multidisciplinary team, including a psychotherapist, is advised. SUMMARY: Patients with primary headache disorders that are classified as "other primary headache disorders" have presentations with unique diagnostic and management considerations. The disorders are highly recognizable, and an appreciation of the diagnoses will aid clinicians in providing safe and effective care for patients presenting with headache.

Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China.

Over the past few months, the spread of the current COVID-19 epidemic has caused tremendous damage worldwide, and unstable many countries economically. Detailed scientific analysis of this event is currently underway to come. However, it is very important to have the right facts and figures to take all possible actions that are needed to avoid COVID-19. In the practice and application of big data sciences, it is always of interest to provide the best description of the data under consideration. The recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in medical science. In this article, we continue to carry this area of research, and introduce a new statistical model called the arcsine modified Weibull distribution. The proposed model is introduced using the modified Weibull distribution with the arcsine-X approach which is based on the trigonometric strategy. The maximum likelihood estimators of the parameters of the new model are obtained and the performance these estimators are assessed by conducting a Monte Carlo simulation study. Finally, the effectiveness and utility of the arcsine modified Weibull distribution are demonstrated by modeling COVID-19 patients data. The data set represents the survival times of fifty-three patients taken from a hospital in China. The practical application shows that the proposed model out-classed the competitive models and can be chosen as a good candidate distribution for modeling COVID-19, and other related data sets.

Clinical relevance of SARS-CoV-2 infection in late pregnancy.

BACKGROUND: Evidence on the outcome of SARS-CoV-2 infection in pregnancy is generally reassuring but yet not definitive. METHODS: To specifically assess the impact of SARS-CoV-2 infection in late pregnancy, we prospectively recruited 315 consecutive women delivering in a referral hospital located in Lombardy, Italy in the early phase of the epidemic. Restriction of the recruitment to this peculiar historical time period allowed to exclude infections occurring early in pregnancy and to limit the recall bias. All recruited subjects underwent a nasopharyngeal swab to assess the presence of Sars-Cov-2 using Real-time PCR. In addition, two different types of antibodies for the virus were evaluated in peripheral blood, those against the spike proteins S1 and S2 of the envelope and those against the nucleoprotein of the nucleocapsid. Women were considered to have had SARS-CoV-2 infection in pregnancy if at least one of the three assessments was positive. RESULTS: Overall, 28 women had a diagnosis of SARS-CoV-2 infection in pregnancy (8.9%). Women diagnosed with the infection were more likely to report one or more episodes of symptoms suggestive for Covid-19 (n = 11, 39.3%) compared to unaffected women (n = 39, 13.6%). The corresponding OR was 4.11 (95%CI: 1.79-9.44). Symptoms significantly associated with Covid-19 in pregnancy included fever, cough, dyspnea and anosmia. Only one woman necessitated intensive care. Pregnancy outcome in women with and without SARS-CoV-2 infection did not also differ. CONCLUSIONS: SARS-CoV-2 infection is asymptomatic in three out of five women in late pregnancy and is rarely severe. In addition, pregnancy outcome may not be markedly affected.

A Fully-Automated Method to Evaluate Coronavirus Disease Progression with COVID-19 Cough Sounds Using Minimal Phase Information.

This paper focuses on an important issue of disease progression of COVID-19 (coronavirus disease 2019) through processing COVID-19 cough sounds by proposing a fully-automated method. The new method is based on time-domain exploiting only phase 1 data which is always available for any cough events. The proposed approach generates plausible click sequences consist of clicks for various cough samples from covid-19 patients. The click sequence, which is extracted from the phase slope function of an input signal, is used to calculate inter-click intervals (ICIs), and thereby a scoring index (SI) is derived based on coefficient of variation(CV) of the extracted ICIs. Moreover, probability density function (pdf) of the output click sequence is obtained. The method does not need to adjust any parameters. The experimental results achieved from real-recorded COVID-19 cough data using the medically annotated Novel Coronavirus Cough Database (NoCoCoDa) reveal that the proposed time-domain method can be a very useful tool for automatic cough sound processing to determine the disease progression of coronavirus patients.

Characteristics and mortality determinants of COVID-19 patients undergoing hemodialysis

Background/aim: The COVID-19 infection, which started in Wuhan City, China, in December 2019, turned into a pandemic in a very short time, affecting mainly the elderly and those with serious chronic illnesses. COVID-19 infections have been observed to have a high mortality rate, especially in patients undergoing maintenance hemodialysis. Materials and methods: Forty-two patients over 18 years of age who underwent a maintenance hemodialysis program at our unit, who tested positive for COVID-19 by PCR from nasopharyngeal swabs, and/or who were observed to have disease-related signs in their CTs were included in the study. Results: In this study, 23 of 42 patients receiving hemodialysis support in our clinic were included. The median age was 67 years old (min: 35; max: 91 years), and all of our patients had primary hypertension and other comorbidities. Their clinical evaluation showed that dry cough (47.8%) and shortness of breath (47.8%) were the most common symptoms. Fever was less pronounced (30.4%). The median time from the onset of symptoms to hospitalization was 1 day (min: 0; max:), and the time from hospitalization to death was 18 days (min: 1; max: 22). Transfer from the inpatient ward to the ICU took a median of 7 days (min: 1; max: 13). Among the 23 patients, 3 died during follow-up, and 20 were discharged with full recovery. Baseline ferritin, procalcitonin levels, and CRP/albumin rates were higher, and neutrophil/lymphocyte levels were lower in patients who eventually died. In these patients, despite being nonsignificant, there were more diabetic patients, and the D-dimer levels were higher than 1000 ugFEU/L. Conclusion: The COVID-19 infection is associated with increased mortality in chronic kidney diseases patients. Despite being nonsignificant, there was a trend towards increased mortality in patient with diabetes, D-dimer levels >1000 ugFEU/L, higher ferritin and prokalsitonin levels, an increased CRP/albumin ratio, and a lower neutrophil/lymphocyte ratio.

Aerosol-generating behaviours in speech pathology clinical practice: A systematic literature review.

OBJECTIVE: To evaluate the evidence of aerosol generation across tasks involved in voice and speech assessment and intervention, to inform better management and to reduce transmission risk of such diseases as COVID-19 in healthcare settings and the wider community. DESIGN: Systematic literature review. DATA SOURCES AND ELIGIBILITY: Medline, Embase, Scopus, Web of Science, CINAHL, PubMed Central and grey literature through ProQuest, The Centre for Evidence-Based Medicine, COVID-Evidence and speech pathology national bodies were searched up until August 13th, 2020 for articles examining the aerosol-generating activities in clinical voice and speech assessment and intervention within speech pathology. RESULTS: Of the 8288 results found, 39 studies were included for data extraction and analysis. Included articles were classified into one of three categories: research studies, review articles or clinical guidelines. Data extraction followed appropriate protocols depending on the classification of each article (e.g. PRISMA for review articles). Articles were assessed for risk of bias and certainty of evidence using the GRADE system. Six behaviours were identified as aerosol generating. These were classified into three categories: vegetative acts (coughing, breathing), verbal communication activities of daily living (speaking, loud voicing), and performance-based tasks (singing, sustained phonation). Certainty of evidence ranged from very low to moderate with variation in research design and variables. CONCLUSIONS: This body of literature helped to both identify and categorise the aerosol-generating behaviours involved in speech pathology clinical practice and confirm the low level of evidence throughout the speech pathology literature pertaining to aerosol generation. As many aerosol-generating behaviours are common human behaviours, these findings can be applied across healthcare and community settings. SYSTEMATIC REVIEW REGISTRATION: Registration number CRD42020186902 with PROSPERO International Prospective Register for Systematic Reviews.

SARS-COV-2 infection in pregnant women and newborns in a Spanish cohort (GESNEO-COVID) during the first wave.

BACKGROUND: Knowledge about SARS-CoV-2 infection in pregnancy and newborns is scarce. The objective of this study is to analyse clinical and epidemiological characteristics of a cohort of women infected with SARS-CoV-2 during pregnancy and their newborns exposed to SARS-CoV-2 during gestation. METHODS: Multicentric observational study of Spanish hospitals from the GESNEO-COVD cohort, participants in RECLIP (Spanish Network of Paediatric Clinical Assays). Women with confirmed SARS-CoV-2 infection by PCR and/or serology during pregnancy, diagnosed and delivering during the period 15/03/2020-31/07/2020 were included. Epidemiological, clinical, and analytical data was collected. RESULTS: A total of 105 pregnant women with a median of 34.1 years old (IQR: 28.8-37.1) and 107 newborns were included. Globally, almost 65% of pregnant women had some COVID-19 symptoms and more than 43% were treated for SARS-COV-2. Overall, 30.8% of pregnant women had pneumonia and 5 (4.8%) women were admitted to the intensive care unit needing invasive mechanical ventilation. There was a rate of 36.2% of caesarean sections, which was associated with pneumonia during pregnancy (OR: 4.203, CI 95%: 1.473-11.995) and lower gestational age at delivery (OR: 0.724, CI 95%: 0.578-0.906). The prevalence of preterm birth was 20.6% and prematurity was associated with pneumonia during gestation (OR: 6.970, CI95%: 2.340-22.750) and having a positive SARS-CoV-2 PCR at delivery (OR: 6.520, CI95%: 1.840-31.790). All nasopharyngeal PCR in newborns were negative at birth and one positivized at 15 days of life. Two newborns died, one due to causes related to prematurity and another of unexpected sudden death during early skin-to-skin contact after delivery. CONCLUSIONS: Although vertical transmission has not been reported in this cohort, the prognosis of newborns could be worsened by SARS-CoV-2 infection during pregnancy as COVID-19 pneumonia increased the risk of caesarean section deliveries and preterm births.

Identifying COVID-19 cases in outpatient settings.

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

Clinical characteristics and outcomes of children with COVID-19 in Saudi Arabia.

OBJECTIVES: To determine the demographic and clinical characteristics, underlying comorbidities, and outcomes of children with coronavirus disease 2019 (COVID-19) infection. METHODS: In this retrospective study, we reported 62 pediatric patients (age <14 years) with confirmed COVID-19 between March 2 and July 1, 2020, at King Abdulaziz University Hospital, Jeddah, Saudi Arabia. RESULTS: Comorbid conditions, including cardiac, neurological, respiratory, and malignant disorders, were reported in 9 patients (14.5%). The most prominent presenting complaints were fever (80.6%) and cough (48.4%). Most of our patients (80.6%) had mild disease, 11.3% had moderate disease, and 8.1% exhibited severe and critical illness. Twenty-one patients (33.9%) were hospitalized, with 4 patients (6.5%) admitted to the pediatric intensive care unit, and 3 (4.8%) patients died. CONCLUSION: All pediatric age groups are susceptible to COVID-19, with no gender difference. COVID-19 infection may result in critical illness and even mortality in subsets of pediatric patients.

Coronavirus disease 2019 and dengue: two case reports.

BACKGROUND: The pandemic of this century has overwhelmed the healthcare systems of affected countries, and all resources have been diverted to coronavirus disease 2019. At the onset, coronavirus disease 2019 can present as any other acute febrile undifferentiated illness. In tropical regions, clinicians are increasingly challenged to differentiate these febrile illnesses without the use of diagnostics. With this pandemic, many of these tropical diseases are neglected and go underreported. Dengue is holoendemic in the Maldives, and dengue viruses circulate throughout the year. Reports about coinfections with dengue virus and severe acute respiratory syndrome coronavirus 2 are scarce, and the outcome and the dynamics of the disease may be altered in the presence of coinfection. We have described the clinical manifestation and serial laboratory profile, and highlighted the atypical findings uncommon in dengue infection. CASE PRESENTATION: Case 1 was a 39-year old Asian male, presented on day 6 of dengue infection with warning signs. Reverse transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 that was done as per hospital protocol was found to be positive. Case 2 was a 38-year old Asian male, was admitted on day 5 of illness with symptoms of acute respiratory infection with positive reverse transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2. Evaluation of progressive leukopenia and thrombocytopenia showed positive dengue serology. CONCLUSION: Clinicians must be conscientious when working on the differential diagnosis of possible tropical diseases in cases of coronavirus disease 2019, specifically, when patients develop hemoconcentration, thrombocytopenia, and transaminitis with elevated expression of aspartate higher than alanine transaminase, which is frequently observed in dengue infection. Caution must be taken during the administration of intravenous fluids when treating patients with coronavirus disease 2019 and dengue coinfection, as coronavirus disease 2019 patients are more prone to develop pulmonary edema. Timely diagnosis and appropriate management are essential to avoid the devastating complications of severe forms of dengue infection. It is important to repeat and reconfirm the dengue serology in coronavirus disease 2019 patients to avoid false positivity. Diligence and care must be taken not to neglect other endemic tropical diseases in the region during the present pandemic.

Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires.

The COVID-19 outbreak was announced as a global pandemic by the World Health Organization in March 2020 and has affected a growing number of people in the past few months. In this context, advanced artificial intelligence techniques are brought to the forefront as a response to the ongoing fight toward reducing the impact of this global health crisis. In this study, potential use-cases of intelligent speech analysis for COVID-19 identification are being developed. By analyzing speech recordings from COVID-19 positive and negative patients, we constructed audio- and symptomatic-based models to automatically categorize the health state of patients, whether they are COVID-19 positive or not. For this purpose, many acoustic features were established, and various machine learning algorithms are being utilized. Experiments show that an average accuracy of 80% was obtained estimating COVID-19 positive or negative, derived from multiple cough and vowel /a/ recordings, and an average accuracy of 83% was obtained estimating COVID-19 positive or negative patients by evaluating six symptomatic questions. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease.