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Coronavirus disease (COVID-19) is a viral pneumonia that originated in China and has rapidly spread around the world. Early diagnosis is important to provide effective and timely treatment. Thus, many studies have attempted to solve the COVID-19 classification problems of workload classification, disease detection, and differentiation from other types of pneumonia and healthy lungs using different radiological imaging modalities. To date, several researchers have investigated the problem of using deep learning methods to detect COVID-19, but there are still unsolved challenges in this field, which this review aims to identify. The existing research on the COVID-19 classification problem suffers from limitations due to the use of the binary or flat multiclass classification, and building classifiers based on only a few classes. Moreover, most prior studies have focused on a single feature modality and evaluated their systems using a small public dataset. These studies also show a reliance on diagnostic processes based on CT as the main imaging modality, ignoring chest X-rays, as explained below. Accordingly, the aim of this review is to examine existing methods and frameworks in the literature that have been used to detect and classify COVID-19, as well as to identify research gaps and highlight the limitations from a critical perspective. The paper concludes with a list of recommendations, which are expected to assist future researchers in improving the diagnostic process for COVID-19 in particular. This should help to develop effective radiological diagnostic data for clinical applications and to open future directions in this area in general. [ABSTRACT FROM AUTHOR]

The global spread of COVID-19 has caused a substantial societal burden and become a major global public health issue. The COVID-19 elderly population with hypertension, diabetes, cardiovascular, and cerebrovascular diseases are at risk. Mortality rates are highest in these individuals if infected with COVID-19. Although the lungs are the main organs involved in acute respiratory distress syndrome caused by COVID-19 infection, COVID-19 triggers inflammatory and immune mechanisms, inducing a "cytokine storm" that aggravates disease progression and may lead to death. Presently, effective drugs are lacking, although current studies have confirmed that drugs with therapeutic potential include redaciclovir, lopinavir/ritonavir combined with interferon-β, convalescent plasma, and monoclonal antibodies. Currently, the most reasonable and effective way to prevent COVID-19 is to control the source of infection, terminate routes of transmission, and protect susceptible populations. With the rise of COVID-19 in China and worldwide, further prevention, diagnosis, and treatment measures are a critical unmet need. Cerebrovascular disease has high incidence, disability rate, and fatality rate. COVID-19 patient outcomes may also be complicated with acute stroke. This paper summarizes the influence of COVID-19 on cerebrovascular disease and discusses possible pathophysiological mechanisms to provide new angles for the prevention and diagnosis of this disease. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and availability of expert radiologists, as well as limitations of the reverse-transcription polymerase chain reaction (RT-PCR) method. Deep learning, a sophisticated machine learning technique, leverages radiological imaging modalities for disease diagnosis and image classification tasks. Previous research on COVID-19 classification has encountered several limitations, including binary classification methods, single-feature modalities, small public datasets, and reliance on CT diagnostic processes. Additionally, studies have often utilized a flat structure, disregarding the hierarchical structure of pneumonia classification. This study aims to overcome these limitations by identifying pneumonia caused by COVID-19, distinguishing it from other types of pneumonia and healthy lungs using chest X-ray (CXR) images and related tabular medical data, and demonstrate the value of incorporating tabular medical data in achieving more accurate diagnoses. Resnet-based and VGG-based pre-trained convolutional neural network (CNN) models were employed to extract features, which were then combined using early fusion for the classification of eight distinct classes. We leveraged the hierarchal structure of pneumonia classification within our approach to achieve improved classification outcomes. Since an imbalanced dataset is common in this field, a variety of versions of generative adversarial networks (GANs) were used to generate synthetic data. The proposed approach tested in our private datasets of 4523 patients achieved a macro-avg F1-score of 95.9% and an F1-score of 87.5% for COVID-19 identification using a Resnet-based structure. In conclusion, in this study, we were able to create an accurate deep learning multi-modal to diagnose COVID-19 and differentiate it from other kinds of pneumonia and normal lungs, which will enhance the radiological diagnostic process. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

Background: The current outbreak of COVID-19 infection, which started in Wuhan, Hubei province, China, in December 2019, is an ongoing challenge and a significant threat to public health requiring surveillance, prompt diagnosis, and research efforts to understand a new, emergent, and unknown pathogen and to develop effective therapies. Despite the increasing number of published studies on COVID-19, in all the examined studies the lack of a well-defined pathophysiology of death among patients who died following COVID-19 infection is evident. Autopsy should be considered mandatory to define the exact cause of death, thus providing useful clinical and epidemiologic information as well as pathophysiological insights to further provide therapeutic tools. Methods: A literature review was performed on PubMed database, using the key terms: "COVID-19", "nCov 19", and "Sars Cov 2". 9709 articles were retrieved; by excluding all duplicated articles, additional criteria were then applied: articles or abstracts in English and articles containing one of the following words: "death", "died", "comorbidity", "cause of death", "biopsy", "autopsy", or "pathological". Results: A total of 50 articles met the inclusion criteria. However, only 7 of these studies reported autopsy-based data. Discussion: The analysis of the main data from the selected studies concerns the complete analysis of 12,954 patients, of whom 2269 died (with a mortality rate of 17.52%). Laboratory confirmation of COVID-19 infection was obtained in all cases and comorbidities were fully reported in 46 studies. The most common comorbidities were: cardiovascular diseases (hypertension and coronary artery disease), metabolic disorders (diabetes, overweight, or obesity), respiratory disorders (chronic obstructive pulmonary disease), and cancer. The most common reported complications were: acute respiratory distress syndrome (ARDS), acute kidney injury, cardiac injury, liver insufficiency, and septic shock. Only 7 papers reported histological investigations. Nevertheless, only two complete autopsies are described and the cause of death was listed as COVID-19 in only one of them. The lack of postmortem investigation did not allow a definition of the exact cause of death to determine the pathways of this infection. Based on the few histopathological findings reported in the analyzed studies, it seems to be a clear alteration of the coagulation system: frequently prothrombotic activity with consequent thromboembolism was described in COVID-19 patients. As a scientific community, we are called on to face this global threat, and to defeat it with all the available tools necessary. Despite the improvement and reinforcement of any method of study in every field of medicine and science, encouraging the autopsy practice as a tool of investigation could also therefore, help physicians to define an effective treatment to reduce mortality. [ABSTRACT FROM AUTHOR]

Background: Since December 2019, SARS-CoV-2 has been causing cases of severe pneumonia in China and has spread all over the world, putting great pressure on health systems. Nasopharyngeal swab (NPS) sensitivity is suboptimal. When the SARS-CoV-2 infection is suspected despite negative NPSs, other tests may help to rule out the infection. Objectives: To evaluate the yield of the lower respiratory tract (LRT) isolation of SARS-CoV-2. To evaluate the correlations between SARS-CoV-2 detection and clinical symptoms, and laboratory values and RSNA CT review scores in suspect patients after two negative NPSs. To assess the safety of bronchoscopy in this scenario. Method: A retrospective analysis of data from LRT sampling (blind nasotracheal aspiration or bronchial washing) for suspected COVID-19 after two negative NPS. Chest CT scans were reviewed by two radiologists using the RSNA imaging classification. Results: SARS-CoV-2 was detected in 14/99 patients (14.1%). A correlation was found between SARS-CoV2 detection on the LRT and the presence of a cough as well as with typical CT features. Typical CT resulted in 57.1% sensitivity, 80.8% accuracy and 92.3% NPV. Neither severe complications nor infections in the personnel were reported. Conclusions: In suspect cases after two negative swabs, CT scan revision can help to rule out COVID-19. In selected cases, with consistent CT features above all, LRT sampling can be of help in confirming COVID-19. [ABSTRACT FROM AUTHOR]

Background: Due to more attentions paid to melanized fungi over the past few decades and under the background of the global coronavirus disease 2019 pandemic (COVID-19) the fact that the virus itself and the immunosuppressive agents such as glucocorticoids can further increase the risk of infections of deep mycoses, the number of patients with phaeohyphomycosis (PHM) has a substantial increase. Their spectrum is broad and the early diagnosis and treatments are extremely sticky. This study aims to more comprehensively understand the clinical features of phaeohyphomycosis in China over 35 years and to establish a more applicable systematical classification and severity grades of lesions to guide treatments and prognosis. Methods: We reviewed 174 cases of proven phaeohyphomycosis reported in Chinese and English language literature from 1987 to 2021 and we also made the accurate classification definitions and detailed information about the epidemiology, species of clinical dematiaceous fungi, minimum inhibitory concentration values, clinical features, treatments, and prognosis. Results: The mortality of cerebral, disseminated and pulmonary phaeohyphomycosis are 55%, 36%, and 25%. Nearly 19% of patients had poor quality of life caused by the complications such as disability, disfigurements, and blindness. The overall misdiagnosis rate of phaeohyphomycosis was 74%. Moderate to severe rashes are accounting for 82% of subcutaneous phaeohyphomycosis. The areas of the head and face are mostly affected accounting for 16% of severe rashes. Nearly 30% of invasive infections of phaeohyphomycosis are triggered by recurrent lesions. Voriconazole, itraconazole, amphotericin B deoxycholate (AmB-DOC), and terbinafine were most commonly used but diagnosis and treatments of phaeohyphomycosis remain challenging in reality. Conclusions: Our classifications are likely to be more practical and easier to popularize, and there are still also plenty of characteristics in these non-specific lesions. There're no significant variations in cure rates, or death rates between three grades of lesions. But patients with severe rashes have longer courses and lower effective rates. [ABSTRACT FROM AUTHOR]

Diagnosis of significant liver fibrosis is essential to facilitate the optimal treatment decisions and improve prognosis in patients with chronic hepatitis B (CHB). We aimed to evaluate the value of inflammatory indicators and construct a nomogram that effectively predicts significant liver fibrosis among CHB patients. 563 CHB patients from two centers in China from 2014 to 2019 were divided into three cohorts (development, internal validation, and independent validation cohorts), assigned into cases with significant fibrosis (liver fibrosis stages ≥2) and those without. Multiple biochemical and serological inflammatory indicators were investigated. Inflammatory indicators, Alanine aminotransferase (ALT) and aspartate aminotransferase (AST), were significantly associated with significant liver fibrosis in CHB patients but limited predictive performance, and then we combined them with prothrombin time activity percentage (PTA) and liver stiffness measurement (LSM) were identified by multivariate logistic regression analysis. Based on these factors, we constructed the nomogram with excellent performance. The area under the receiver operating characteristic curve (AUROC) for the nomogram in the development, internal validation, and independent validation cohorts were 0.860, 0.877, and 0.811, respectively. Our nomogram based on ALT and AST that had excellent performance in predicting significant fibrosis of CHB patients were constructed. [ABSTRACT FROM AUTHOR]

As countries are lifting restrictions and resuming international travels, the rising risk of COVID-19 importation remains concerning, given that the SARS-CoV-2 virus could be transmitted unintentionally through the global transportation network. To explore and assess the effective strategies for curtailing the epidemic risk from international importation nationwide, we evaluated "the joint prevention and control" mechanism, which made up of 19 containment policies, on how it impacted the change of medical observation and detection time from border arrival to laboratory confirmation of COVID-19 in its burst in China. Based on 1,314 epidemiological-survey cases from February 29 to May 25, 2020, we found that the synchronized approach of implementing multi-dimensional interventional policies, such as a centralized quarantine and nucleic acid testing (NAT), flight service adjustment and border closure, effectively facilitate early identification of infected case. Specifically, the implementation of the international flight service reduction was found to be associated with a reduction of the mean intervals of diagnosis from arrival to lab-confirmation by 0.44 days maximally, and the border closure was associated with a reduction of the diagnosis interval of imported cases by 0.69 days, from arrival to laboratory confirmation. The study suggests that a timely and synchronized implementation of multi-dimensional policies is compelling in preventing domestic spreading from importation. [ABSTRACT FROM AUTHOR]

Background: Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19.Objective: This study aims to visualize and measure patients' heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future.Methods: A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables' coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes.Results: A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis "accuracy" attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes.Conclusions: Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People's preferences for the "accuracy" and "diagnostic expenses" attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration. [ABSTRACT FROM AUTHOR]

Background: COVID-19 has posed an enormous threat to public health around the world. Some severe and critical cases have bad prognoses and high case fatality rates, unraveling risk factors for severe COVID-19 are of significance for predicting and preventing illness progression, and reducing case fatality rates. Our study focused on analyzing characteristics of COVID-19 cases and exploring risk factors for developing severe COVID-19. Methods: The data for this study was disease surveillance data on symptomatic cases of COVID-19 reported from 30 provinces in China between January 19 and March 9, 2020, which included demographics, dates of symptom onset, clinical manifestations at the time of diagnosis, laboratory findings, radiographic findings, underlying disease history, and exposure history. We grouped mild and moderate cases together as non-severe cases and categorized severe and critical cases together as severe cases. We compared characteristics of severe cases and non-severe cases of COVID-19 and explored risk factors for severity. Results: The total number of cases were 12 647 with age from less than 1 year old to 99 years old. The severe cases were 1662 (13.1%), the median age of severe cases was 57 years [Inter-quartile range(IQR): 46–68] and the median age of non-severe cases was 43 years (IQR: 32–54). The risk factors for severe COVID-19 were being male [adjusted odds ratio (aOR) = 1.3, 95% CI: 1.2–1.5]; fever (aOR = 2.3, 95% CI: 2.0–2.7), cough (aOR = 1.4, 95% CI: 1.2–1.6), fatigue (aOR = 1.3, 95% CI: 1.2–1.5), and chronic kidney disease (aOR = 2.5, 95% CI: 1.4–4.6), hypertension (aOR = 1.5, 95% CI: 1.2–1.8) and diabetes (aOR = 1.96, 95% CI: 1.6–2.4). With the increase of age, risk for the severity was gradually higher [20–39 years (aOR = 3.9, 95% CI: 1.8–8.4), 40–59 years (aOR = 7.6, 95% CI: 3.6–16.3), ≥ 60 years (aOR = 20.4, 95% CI: 9.5–43.7)], and longer time from symtem onset to diagnosis [3–5 days (aOR = 1.4, 95% CI: 1.2–1.7), 6–8 days (aOR = 1.8, 95% CI: 1.5–2.1), ≥ 9 days(aOR = 1.9, 95% CI: 1.6–2.3)]. Conclusions: Our study showed the risk factors for developing severe COVID-19 with large sample size, which included being male, older age, fever, cough, fatigue, delayed diagnosis, hypertension, diabetes, chronic kidney diasease, early case identification and prompt medical care. Based on these factors, the severity of COVID-19 cases can be predicted. So cases with these risk factors should be paid more attention to prevent severity. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) has showed high transmission across the continents due to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) with total infected cases of around ~ 44 million people. This communicable virus that initiated from the Wuhan city of China in the month of December 2020 has now spread to 189 different countries with 1.1 million fatalities worldwide (till 28 Oct, 2020). The World Health Organization (WHO) declared this outbreak as Public Health Emergency of International Concern in January, 2020. The infection spreads mainly due to contact with infected droplets or fomites, highlighting flu like symptoms initially, which may further progress into severe pneumonia and respiratory failure, often observed in elderly patients with prehistory of other diseases. The diagnosis is based on detection of viral antigen, human antibody and viral gene (RT-PCR). Further, various other diagnostic tools including X-ray, CT-scan are used for imaging purpose, recently artificial intelligence based imaging (contactless scanning) gained popularity. Generally testing of existing drugs (repurposing) and development of new molecules are the main strategies adopted by researchers. However, as per initial findings, various drugs, monoclonal antibody and plasma therapy were found to show effectiveness against COVID-19. Further, many vaccine candidates have entered or will soon enter phase III clinical testing. This disease has further challenged the global economy. Thus, this review uniquely compares the strategies adopted by developed and developing countries worldwide including protective measures like lockdown, continuous testing, utilizing latest tools (artificial intelligence) in curbing this infection spread. [ABSTRACT FROM AUTHOR]

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A highly contagious viral infection emerged in Wuhan city; China had increased mortality with uncertain pathogenesis spreads throughout the world to become a pandemic. It is reported to be caused by a member of β coronaviruses and named it as COVID-19 by the World Health Organization (WHO). The disease is caused by a mutant strain of coronavirus SARS-COV-2 that affects the respiratory tract causing mild to severe respiratory tract illness. The clinical manifestation ranges from mild, moderate, severe and very severe signs and symptoms result in death due to severe hypoxia or multi-organ dysfunction. Also, the affected persons were capable of infecting others through various modes of transmission through respiratory droplets (aerosol spread). A definite investigation protocol has followed to diagnose COVID 19 disease but mainly confirmed with reverse transcription polymerase chain reaction. Computerized tomography scan plays a significant role in the diagnosis and prognosis of affected individuals. The major problem with COVID-19 is due to its novelty and lack of vaccination or treatment. This review focuses on the mutation, pathogenesis, various diagnostic tests adopted and autopsy findings in patients affected with COVID-19. [ABSTRACT FROM AUTHOR]

Objective: Diagnostic/therapeutic accidents Background: At the end of 2019, coronavirus (SARS-CoV-2) was recognized as the cause of a cluster of pneumonia cases in Wuhan, a city in China. There are numerous complications associated with COVID-19 infection, such as acute respiratory distress syndrome, renal failure, circulatory shock, and multi-organ failure. Spontaneous pneumothorax following COVID-19 pneumonia is an extremely rare complication. Case Report: We report the case of a 49-year-old man with a past medical history of type 2 diabetes mellitus with an initial presentation of cough, shortness of breath, and fever. He was diagnosed with COVID-19 pneumonia and rapidly deteriorated on the day of admission, requiring initiation of mechanical ventilation. The patient recovered clinically and was discharged home. He returned 21 days after discharge with a spontaneous pneumothorax. Conclusions: Spontaneous pneumothorax is a rare complication after apparent recovery from COVID-19 pneumonia. It is imperative that treating physicians are aware of this complication in order to recognize it early and treat it promptly. [ABSTRACT FROM AUTHOR]

A new type of coronavirus-induced pneumonia eventually termed "coronavirus disease 2019" (COVID-19) was diagnosed in patients in Wuhan (Hubei Province, China) in December 2019, and soon spread worldwide. To improve the detection rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we analyzed the results of viral nucleic acid and serum-specific antibody tests on clinical samples from 20 patients with SARS-CoV-2 infection diagnosed at the First Affiliated Hospital of Guangzhou Medical University in China. By comparing various sample types collected from COVID-19 patients, we revealed multiple pathways for SARS-CoV-2 shedding, and a prolonged detectable period for viral nucleic acid test in sputum specimens, demonstrating that the timeline of the viral shedding is of great value in determining the time of release from quarantine or discharge from hospital. We also recommend for the application of serological test to assist in confirming SARS-CoV-2 infection judged by viral nucleic acid test, especially when COVID-19-related symptoms have appeared and the viral nucleic acid test was negative. Our findings are critical for the diagnosis of SARS-CoV-2 infection and for determining deadline of restriction measures to prevent transmission caused by convalescent patients with COVID-19. [ABSTRACT FROM AUTHOR]

An outbreak of 2019 novel coronavirus disease (COVID-19) began in China during December 2019 which unexpectedly spread to other countries and caused high mortality all over the world. COVID-19 disease primarily manifests as a respiratory tract infection. However, emerging data indicate that it should be regarded as a systemic disease for affecting multiple systems such as cardiovascular, respiratory, gastrointestinal and immune system. There is an accelerated need for detecting the laboratory tests that can aid in identifying infected people and asymptomatic carriers to control the virus transmission process. Although the clinical manifestation of COVID-19 has been widely defined, an overview of the most significant laboratory findings in patients with COVID-19 infection is still limited. Elevation was the predominate result among most of the laboratory parameters while a few decreased in value. Laboratory data have shown that most patients had a decrease in lymphocyte count, Eosinophils count and albumin level. Also, laboratory data recorded an elevation in Leukocyte, ESR, PT, D-dimer, PCT, CRP, ALT, AST, Bilirubin, Creatinine, CK, LDH, Ferritin, Troponin, Myoglobin, IL-6, IL10 and TNF. In general, the parameters had more prominent laboratory abnormalities in severe cases than with non-severe cases. It is well known that laboratory tests results play an important role and can support the early diagnosis of many diseases. This study was carried out to review the abnormalities among the laboratory tests and track the parameters that showed a frequently significant result supporting the primary detection of SARS-COV-2 infection. [ABSTRACT FROM AUTHOR]

The new coronavirus that was first identified in December 2019 in Wuhan China, now called SARS-CoV-2, which causes the disease called COVID-19, has spread from China to the entire world in a few months. Due to its contagious potential (R0: 5.7) and because there is still no effective treatment to stop the infection, and a vaccine for prevention it is not yet available to the general population, COVID-19 is currently considered a global health problem. The need to implement sensitive methods for the identification of individuals with COVID-19 has led to the development of different molecular and immunological tests. The importance of a timely and accurate diagnosis is essential to determine the course of the pandemic. The interpretation of the results obtained by each test as well as the factors that affect these results have not been fully described. In this review, we describe and analyze the different SARS-CoV-2 detection methods that have been performed in Mexico and are available worldwide, outlining their strengths and weaknesses. Further, a broader perspective of the correct use and interpretation of the results obtained with these diagnostic tools is proposed to improve the containment strategy and identify the true impact of the pandemic. [ABSTRACT FROM AUTHOR]

A pneumonia outbreak with unknown etiology was reported in Wuhan, Hubei province, China, in December 2019, associated with the Huanan Seafood Wholesale Market. The causative agent of the outbreak was identified by the WHO as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), producing the disease named coronavirus disease-2019 (COVID-19). The virus is closely related (96.3%) to bat coronavirus RaTG13, based on phylogenetic analysis. Human-to-human transmission has been confirmed even from asymptomatic carriers. The virus has spread to at least 200 countries, and more than 1,700,000 confirmed cases and 111,600 deaths have been recorded, with massive global increases in the number of cases daily. Therefore, the WHO has declared COVID-19 a pandemic. The disease is characterized by fever, dry cough, and chest pain with pneumonia in severe cases. In the beginning, the world public health authorities tried to eradicate the disease in China through quarantine but are now transitioning to prevention strategies worldwide to delay its spread. To date, there are no available vaccines or specific therapeutic drugs to treat the virus. There are many knowledge gaps about the newly emerged SARS-CoV-2, leading to misinformation. Therefore, in this review, we provide recent information about the COVID-19 pandemic. This review also provides insights for the control of pathogenic infections in humans such as SARS-CoV-2 infection and future spillovers. [ABSTRACT FROM AUTHOR]