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The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%. [ABSTRACT FROM AUTHOR]

The coronavirus (COVID19), also known as the novel coronavirus, first appeared in December 2019 in Wuhan, China. After that, it quickly spread throughout the world and became a disease. It has significantly impacted our everyday lives, the national and international economies, and public health. However, early diagnosis is critical for prompt treatment and reducing trauma in the healthcare system. Clinical radiologists primarily use chest X-rays, and computerized tomography (CT) scans to test for pneumonia infection. We used Chest CT scans to predict COVID19 pneumonia and healthy scans in this study. We proposed a joint framework for prediction based on classical feature fusion and PSO-based optimization. We begin by extracting standard features such as discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and dominant rotated local binary patterns (DRLBP). In addition, we extracted Shanon Entropy and Kurtosis features. In the following step, a Max-Covariance-based maximization approach for feature fusion is proposed. The fused features are optimized in the preliminary phase using Particle Swarm Optimization (PSO) and the ELM fitness function. For final prediction, PSO is used to obtain robust features, which are then implanted in a Support Vector Data Description (SVDD) classifier. The experiment is carried out using available COVID19 Chest CT Scans and scans from healthy patients. These images are from the Radiopaedia website. For the proposed scheme, the fusion and selection process accuracy is 88.6% and 93.1%, respectively. A detailed analysis is conducted, which supports the proposed system efficiency. [ABSTRACT FROM AUTHOR]

Background: Since the outbreak of coronavirus disease (COVID-19) in December 2019 in Wuhan, it has spread rapidly worldwide. We aimed to establish and validate a nomogram that predicts the probability of coronavirus-associated acute respiratory distress syndrome (CARDS). Methods: In this single-centre, retrospective study, 261 patients with COVID-19 were recruited using positive reverse transcription–polymerase chain reaction tests for severe acute respiratory syndrome coronavirus 2 in Tongji Hospital at Huazhong University of Science and Technology (Wuhan, China). These patients were randomly distributed into the training cohort (75%) and the validation cohort (25%). The factors included in the nomogram were determined using univariate and multivariate logistic regression analyses based on the training cohort. The area under the receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency of the nomogram in the training and validation cohorts. Results: Independent predictive factors, including fasting plasma glucose, platelet, D-dimer, and cTnI, were determined using the nomogram. In the training cohort, the AUC and concordance index were 0.93. Similarly, in the validation cohort, the nomogram still showed great distinction (AUC: 0.92) and better calibration. The calibration plot also showed a high degree of agreement between the predicted and actual probabilities of CARDS. In addition, the DCA proved that the nomogram was clinically beneficial. Conclusion: Based on the results of laboratory tests, we established a predictive model for acute respiratory distress syndrome in patients with COVID-19. This model shows good performance and can be used clinically to identify CARDS early. Trial Registration: Ethics committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (No.:(2020) Linlun-34th). [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study. [ABSTRACT FROM AUTHOR]

At the end of 2019, a new coronavirus (COVID-19) epidemic has triggered global public health concern. Here, a model integrating the daily intercity migration network, which constructed from real-world migration records and the Susceptible–Exposed–Infected–Removed model, is utilized to predict the epidemic spreading of the COVID-19 in more than 300 cities in China. However, the model has more than 1800 unknown parameters, which is a challenging task to estimate all unknown parameters from historical data within a reasonable computation time. In this article, we proposed a pseudocoevolutionary simulated annealing (SA) algorithm for identifying these unknown parameters. The large volume of unknown parameters of this model is optimized through three procedures co-adapted SA-based optimization processes, respectively. Our results confirm that the proposed method is both efficient and robust. Then, we use the identified model to predict the trends of the epidemic spreading of the COVID-19 in these cities. We find that the number of infections in most cities in China has reached their peak from February 29, 2020, to March 15, 2020. For most cities outside Hubei province, the total number of infected individuals would be less than 100, while for most cities in Hubei province (exclude Wuhan), the total number of infected individuals would be less than 3000. [ABSTRACT FROM AUTHOR]

To predict the mortality of patients with coronavirus disease 2019 (COVID-19). We collected clinical data of COVID-19 patients between January 18 and March 29 2020 in Wuhan, China. Gradient boosting decision tree (GBDT), logistic regression (LR) model, and simplified LR were built to predict the mortality of COVID-19. We also evaluated different models by computing area under curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV) under fivefold cross-validation. A total of 2924 patients were included in our evaluation, with 257 (8.8%) died and 2667 (91.2%) survived during hospitalization. Upon admission, there were 21 (0.7%) mild cases, 2051 (70.1%) moderate case, 779 (26.6%) severe cases, and 73 (2.5%) critically severe cases. The GBDT model exhibited the highest fivefold AUC, which was 0.941, followed by LR (0.928) and LR-5 (0.913). The diagnostic accuracies of GBDT, LR, and LR-5 were 0.889, 0.868, and 0.887, respectively. In particular, the GBDT model demonstrated the highest sensitivity (0.899) and specificity (0.889). The NPV of all three models exceeded 97%, while their PPV values were relatively low, resulting in 0.381 for LR, 0.402 for LR-5, and 0.432 for GBDT. Regarding severe and critically severe cases, the GBDT model also performed the best with a fivefold AUC of 0.918. In the external validation test of the LR-5 model using 72 cases of COVID-19 from Brunei, leukomonocyte (%) turned to show the highest fivefold AUC (0.917), followed by urea (0.867), age (0.826), and SPO2 (0.704). The findings confirm that the mortality prediction performance of the GBDT is better than the LR models in confirmed cases of COVID-19. The performance comparison seems independent of disease severity. [ABSTRACT FROM AUTHOR]

COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that COVID-19 is transmitted among humans by droplet infection or direct contact. COVID-19 pandemic has invaded more than 210 countries around the world and as of February 18th, 2021, just after a year has passed, a total of 110,533,973 confirmed cases of COVID-19 were reported and its death toll reached about 2,443,091. COVID-19 is a new member of the family of corona viruses, its nature, behaviour, transmission, spread, prevention, and treatment are to be investigated. Generally, a huge amount of data is accumulating regarding the COVID-19 pandemic, which makes hot research topics for machine learning researchers. However, the panicked world's population is asking when the COVID-19 will be over? This study considered machine learning approaches to predict the spread of the COVID-19 in many countries. The experimental results of the proposed model showed that the overall R2 is 0.99 from the perspective of confirmed cases. A machine learning model has been developed to predict the estimation of the spread of the COVID-19 infection in many countries and the expected period after which the virus can be stopped. Globally, our results forecasted that the COVID-19 infections will greatly decline during the first week of September 2021 when it will be going to an end shortly afterward. [ABSTRACT FROM AUTHOR]

Background: To explore the relationship between peripheral lymphocyte counts (PLCs) and the mortality risk of coronavirus disease 2019 (COVID-19), as well as the potential of PLC for predicting COVID-19 hospitalized patients death.Methods: Baseline characteristics, laboratory tests, imaging examinations, and outcomes of 134 consecutive COVID-19 hospitalized patients were collected from a tertiary hospital in Wuhan city from January 25 to February 24, 2020. Multiple regression analysis was used to analyze the relationship between the PLC at admission and mortality risk in COVID-19 patients and to establish a model for predicting death in COVID-19 hospitalized patients based on PLC.Results: After adjusting for potential confounding factors, we found a non-linear relationship and threshold saturation effect between PLC and mortality risk in COVID-19 patients (infection point of PLC: 0.95 × 109/L). Multiple regression analysis showed that when PLCs of COVID-19 patients were lower than 0.95 × 109/L, the patients had a significantly higher mortality risk as compared to COVID-19 patient with PLCs > 0.95 × 109/L (OR 7.27; 95% CI 1.10-48.25). The predictive power of PLC for death in COVID-19 patients (presented as area under the curve) was 0.78. The decision curve analysis showed that PLC had clinical utility for the prediction of death in COVID-19 inpatients.Conclusions: PLC had a non-linear relationship with mortality risk in COVID-19 inpatients. Reduced PLCs (< 0.95 × 109/L) were associated with an increased mortality risk in COVID-19 inpatients. PLCs also had a potential predictive value for the death of COVID-19 inpatients. [ABSTRACT FROM AUTHOR]

Virological tests have now shown conclusively that a novel coronavirus is causing the 2019–2020 atypical pneumonia outbreak in Wuhan, China. We demonstrate that non-virological descriptive characteristics could have determined that the outbreak is caused by a novel pathogen in advance of virological testing. Characteristics of the ongoing outbreak were collected in real time from two medical social media sites. These were compared against characteristics of eleven pathogens that have previously caused cases of atypical pneumonia. The probability that the current outbreak is due to "Disease X" (i.e., previously unknown etiology) as opposed to one of the known pathogens was inferred, and this estimate was updated as the outbreak continued. The probability (expressed as a percentage) that Disease X is driving the outbreak was assessed as over 29% on 31 December 2019, one week before virus identification. After some specific pathogens were ruled out by laboratory tests on 5 January 2020, the inferred probability of Disease X was over 49%. We showed quantitatively that the emerging outbreak of atypical pneumonia cases is consistent with causation by a novel pathogen. The proposed approach, which uses only routinely observed non-virological data, can aid ongoing risk assessments in advance of virological test results becoming available. [ABSTRACT FROM AUTHOR]