Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques

Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model

Since the dissemination of information is more rapid and the scale of users on online platforms is enormous, the public opinion risk is more visible and harder to tackle for universities and authorities. Improving the accuracy of predictions regarding online public opinion crises, especially those related to campuses, is crucial for maintaining social stability. This research proposes a public opinion crisis prediction model that applies the Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) and implements it to analyze a trending topic on Sina Weibo to validate its prediction accuracy. A full-chain analytical framework for online public opinion prediction is established in this study, which enables the model to illustrate the level of risk related to public opinion and its variation trend by introducing the public opinion risk index. The prediction accuracy of the model is validated through several evaluation criteria, and a comparison between real and predicted results, and the simulation of the intervention on this incident indicates that the proposed model is competent for both trend prediction and assisting in intervention. The study also demonstrates the importance of immediate response and intervention to public opinion crises.

A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future.

Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain.

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

BACKGROUND

COVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time.

RESULTS

In this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination-Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient.

CONCLUSION

Our proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript's availability of the data and materials under the declaration section for access.

Impact of the Pandemic Time on the Mental Health of People with Dementia and Their Family Caregivers in Brazil and Chile: One-Year Follow-Up

Background

Previous studies reported the negative impact of social isolation on mental health in people with dementia (PwD) and their caregivers, butlongitudinal studies seem scarcer.

Objective

To describe a one-year follow-up impact of the COVID-19 pandemic on PwD and their caregivers in both Brazil and Chile.

Methods

This study analyzed the impact of the pandemic on the psychological and physical health of PwD and their family caregivers after one year of follow-up in three outpatient clinics in Brazil (n = 68) and Chile (n = 61).

Results

In both countries, PwD reduced their functional capacity after one year of follow-up (p = 0.017 and p = 0.009; respectively) and caregivers reported worse physical and mental health (p = 0.028 and p = 0.039). Only in Chile, caregivers reported more sadness associated with care (p = 0.001), and reduced time sleeping (p = 0.07).

Conclusions

In conclusion, the COVID-19 pandemic appears to have had a long-lasting impact on PwD and their caregivers. However, it is essential to acknowledge that the inherent progression of dementia itself may also influence changes observed over a year.

An Expedition on Synthetic Methodology of FDA-approved Anticancer Drugs (2018-2021)

New drugs being established in the market every year produce specified structures for selective biological targeting. With medicinal insights into molecular recognition, these begot molecules open new rooms for designing potential new drug molecules. In this review, we report the compilation and analysis of a total of 56 drugs including 33 organic small molecules (Mobocertinib, Infigratinib, Sotorasib, Trilaciclib, Umbralisib, Tepotinib, Relugolix, Pralsetinib, Decitabine, Ripretinib, Selpercatinib, Capmatinib, Pemigatinib, Tucatinib, Selumetinib, Tazemetostat, Avapritinib, Zanubrutinib, Entrectinib, Pexidartinib, Darolutamide, Selinexor, Alpelisib, Erdafitinib, Gilteritinib, Larotrectinib, Glasdegib, Lorlatinib, Talazoparib, Dacomitinib, Duvelisib, Ivosidenib, Apalutamide), 6 metal complexes (Edotreotide Gallium Ga-68, fluoroestradiol F-18, Cu 64 dotatate, Gallium 68 PSMA-11, Piflufolastat F-18, 177Lu (lutetium)), 16 macromolecules as monoclonal antibody conjugates (Brentuximabvedotin, Amivantamab-vmjw, Loncastuximabtesirine, Dostarlimab, Margetuximab, Naxitamab, Belantamabmafodotin, Tafasitamab, Inebilizumab, SacituzumabGovitecan, Isatuximab, Trastuzumab, Enfortumabvedotin, Polatuzumab, Cemiplimab, Mogamulizumab) and 1 peptide enzyme (Erwiniachrysanthemi-derived asparaginase) approved by the U.S. FDA between 2018 to 2021. These drugs act as anticancer agents against various cancer types, especially non-small cell lung, lymphoma, breast, prostate, multiple myeloma, neuroendocrine tumor, cervical, bladder, cholangiocarcinoma, myeloid leukemia, gastrointestinal, neuroblastoma, thyroid, epithelioid and cutaneous squamous cell carcinoma. The review comprises the key structural features, approval times, target selectivity, mechanisms of action, therapeutic indication, formulations, and possible synthetic approaches of these approved drugs. These crucial details will benefit the scientific community for futuristic new developments in this arena.

FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease

Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.

The coming Omicron waves and factors affecting its spread after China reopening borders

The Chinese government relaxed the Zero-COVID policy on Dec 15, 2022, and reopened the border on Jan 8, 2023. Therefore, COVID prevention in China is facing new challenges. Though there are plenty of prior studies on COVID, none is regarding the predictions on daily confirmed cases, and medical resources needs after China reopens its borders. To fill this gap, this study innovates a combination of the Erdos Renyl network, modified computational model [Formula: see text], and python code instead of only mathematical formulas or computer simulations in the previous studies. The research background in this study is Shanghai, a representative city in China. Therefore, the results in this study also demonstrate the situation in other regions of China. According to the population distribution and migration characteristics, we divided Shanghai into six epidemic research areas. We built a COVID spread model of the Erodos Renyl network. And then, we use python code to simulate COVID spread based on modified [Formula: see text] model. The results demonstrate that the second and third waves will occur in July-September and Oct-Dec, respectively. At the peak of the epidemic in 2023, the daily confirmed cases will be 340,000, and the cumulative death will be about 31,500. Moreover, 74,000 hospital beds and 3,700 Intensive Care Unit (ICU) beds will be occupied in Shanghai. Therefore, Shanghai faces a shortage of medical resources. In this simulation, daily confirmed cases predictions significantly rely on transmission, migration, and waning immunity rate. The study builds a mixed-effect model to verify further the three parameters' effect on the new confirmed cases. The results demonstrate that migration and waning immunity rates are two significant parameters in COVID spread and daily confirmed cases. This study offers theoretical evidence for the government to prevent COVID after China opened its borders.

Automatic COVID-19 prediction using explainable machine learning techniques

The coronavirus is considered this century's most disruptive catastrophe and global concern. This disease has prompted extreme social, psychological and economic impacts affecting millions of people around the globe. COVID-19 is transmitted from one infected person's body to another through respiratory droplets. This virus proliferates when people breathe in air-contaminated space with droplets and microscopic airborne particles. This research aims to analyze automatic COVID-19 detection using machine learning techniques to build an intelligent web application. The dataset has been preprocessed by dropping null values, feature engineering, and synthetic oversampling (SMOTE) techniques. Next, we trained and evaluated different classifiers, i.e., logistic regression, random forest, decision tree, k-nearest neighbor, support vector machine (SVM), ensemble models (adaptive boosting and extreme gradient boosting) and deep learning (artificial neural network, convolutional neural network and long short-term memory) techniques. Explainable AI with the LIME framework has been applied to interpret the prediction results. The hybrid CNN-LSTM algorithm with the SMOTE approach performed better than the other models on the employed open-source dataset obtained from the Israeli Ministry of Health website, with 96.34% accuracy and a 0.98 F1 score. Finally, this model was chosen to deploy the proposed prediction system to a website, where users may acquire an instantaneous COVID-19 prognosis based on their symptoms.

Effect of a health education program about COVID-19 on the knowledge, attitude, and practices of paramedical students in Egypt: an interventional study

Background

In December 2019, an outbreak of novel coronavirus disease 2019, caused by severe acute respiratory syndrome coronavirus 2, has been reported in China.

Objective

This study aims to assess the knowledge, attitude, and practices (KAP) of Benha Health Technical Institute (BHTI) students and to evaluate the impact of a health education program about COVID-19 on their KAP.

Methods

This is an interventional study that recruited 398 students from BHTI and was conducted in 3 phases. Firstly, an assessment of students' KAP was done using a structured questionnaire concerning COVID-19. Secondly, an education program about COVID-19 was conducted. Lastly, the reassessment of KAP was carried out using the same questionnaire after one month.

Results

The median knowledge, attitude, and practice scores among the studied students in the pre-interventional stage were 12, 15, and 26 which had significantly increased to 15, 16, and 28 respectively after the intervention. The knowledge score of the participants was significantly affected by students' age and grade of education; the attitude score was affected by age, gender, and grade of education, while the practice score was only affected by participants' age.

Conclusion

The educational program significantly increased the KAP of BHTI students.

An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM

The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two single composing models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE, outperforming the composing AR (5.629%) and LSTM (4.934%) alone on average. In country-level datasets, our hybrid model outperforms the widely-used predictive models such as AR, LSTM, Support Vector Machines, Gradient Boosting, and Random Forest, in predicting the COVID-19 cases in Japan, Canada, Brazil, Argentina, Singapore, Italy, and the United Kingdom. In addition to the predictive performance, we illustrate the interpretability of our proposed hybrid model using the estimated AR component, which is a key feature that is not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models for COVID-19 cases, which could have significant implications for public health policy making and control of the current COVID-19 and potential future pandemics.

Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

Coronavirus disease 2019 (COVID-19) has produced a global pandemic, which has devastating effects on health, economy and social interactions. Despite the less contraction and spread of COVID-19 in Africa compared to some other continents in the world, Africa remains amongst the most vulnerable regions due to less technology and unequipped or poor health system. Recent happenings showed that COVID-19 may stay for years owing to the discoveries of new variants (such as Omicron) and new wave of infections in several countries. Therefore, accurate prediction of new cases is vital to make informed decisions and in evaluating the measures that should be implemented. Studies on COVID-19 prediction are limited in Africa despite the risks and dangers that the virus possessed. Hence, this study was performed to predict daily COVID-19 cases in 10 African countries spread across the north, south, east, west and central Africa considering countries with few and large number of daily COVID-19 cases. Machine learning (ML) models due to their nonlinearity and accurate prediction capabilities were employed for this purpose, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and conventional multiple linear regression (MLR) models. As any other natural process, the COVID-19 pandemic may contain both linear and nonlinear aspects. In such circumstances, neither nonlinear (ML) nor linear (MLR) models could be sufficient; hence, combining both ML and MLR models may produce better accuracy. Consequently, to improve the prediction efficiency of the ML models, novel ensemble approaches including ANN-E and SVM-E were employed. The advantage of using ensemble approaches is that they provide collective benefits of all the standalone models, thereby reducing their weaknesses and enhancing their prediction capabilities. The obtained results showed that ANFIS led to better prediction performance with MAD = 0.0106, MSE = 0.0003, RMSE = 0.0185 and R2 = 0.9059 in the validation step. The results of the proposed ensemble approaches demonstrated very high improvements in predicting the COVID-19 pandemic in Africa with MAD = 0.0073, MSE = 0.0002, RMSE = 0.0155 and R2 = 0.9616. The ANN-E improved the standalone models performance in the validation step up to 10%, 14%, 42%, 6%, 83%, 11%, 7%, 5%, 7% and 31% for Morocco, Sudan, Namibia, South Africa, Uganda, Rwanda, Nigeria, Senegal, Gabon and Cameroon, respectively. This study results offer a solid foundation in the application of ensemble approaches for predicting COVID-19 pandemic across all regions and countries in the world.

Prediction and forecasting of worldwide corona virus (COVID-19) outbreak using time series and machine learning

How will the newly discovered coronavirus (COVID-19) affect the world and what will be its global impact? For answering this question, we will require a prediction of overall recoveries and fatalities, as well as a reliable prognosis of coronavirus cases. Predicting, however, requires an ample total of past data related to it. On any particular day, the prediction is unclear since events in the future rarely repeat themselves the way that they did in the past. Furthermore, forecasts and predictions are determined by the absolute interests, accuracy of the data, and prophesied variables. In addition, psychological factors play an enormous role in how people perceive and react to the danger from the disease and therefore the fear that it is going to affect them personally. This research paper advances an unbiased method for predicting the increase of the COVID-19 employing a simple, but powerful method to do so. Assumed that the data are accurate and reliable which the longer term will still follow an equivalent disease pattern, our projections intimate with a large association. Within the COVID-19 cases were documented, in contingency, there is a steady increase. The hazards are far away from symmetric, as underestimating a pandemic's spread and failing to do enough to prevent it is far a lot worse than overspending and being too cautious when it will not be needed. This paper illustrates the timeline of a live forecasting study with huge implied implications for devising and decision-making and gives unbiased predictions on COVID-19 confirmed cases, recovered cases, deaths, and ongoing cases are shown on a continental map using data science and machine learning (ML) approaches. Utilizing these ML-based techniques, the proposed system predicts the accurate COVID-19 cases and gives better performance.

[Usage of Artificial Intelligence in the Combat against the COVID-19 Pandemic]

The COVID-19 pandemic is a global health emergency of historic dimension. In this situation, researchers worldwide wanted to help manage the pandemic by using artificial intelligence (AI). This narrative review aims to describe the usage of AI in the combat against COVID-19. The addressed aspects encompass AI algorithms for analysis of thoracic X-rays or CTs, prediction models for severity and outcome of the disease, AI applications in development of new drugs and vaccines as well as forecasting models for spread of the virus. The review shows, which approaches were pursued, and which were successful.