Forecast of the outbreak of COVID-19 using artificial neural network: Case study Qatar, Spain, and Italy

A besyian regularisation neural network approach for hepatitis B virus spread prediction and immune system therapy model

The primary aim of the article is to analyze the response of the human immune system when it encounters the hepatitis B virus. This is done using a mathematical system of differential equations. The differential equation system has six components, likely representing various aspects of the immune response or virus dynamics. A Bayesian regularization neural network has been presented in the process of training. These networks are employed to find solutions for different categories or scenarios related to hepatitis B infection. The Adams method is used to generate reference data sets. The back-propagated artificial neural network, based on Bayesian regularization, is trained and validated using the generated data. The data is divided into three sets: 90% for training and 5% each for testing and validation. The correctness and effectiveness of the proposed neural network model have been assessed using various evaluation metrics. The metrics have been used in this study are Mean Square Error (MSE), histogram errors, and regression plots. These measures provide support to the neural network to approximate the immune response to the hepatitis B virus.

Modelling the Unidentified Abortion Burden from Four Infectious Pathogenic Microorganisms (Leptospira interrogans, Brucella abortus, Brucella ovis, and Chlamydia abortus) in Ewes Based on Artificial Neural Networks Approach: The Epidemiological Basis for a Control Policy

Unidentified abortion, of which leptospirosis, brucellosis, and ovine enzootic abortion are important factors, is the main cause of disease spread between animals and humans in all agricultural systems in most developing countries. Although there are well-defined risk factors for these diseases, these characteristics do not represent the prevalence of the disease in different regions. This study predicts the unidentified abortion burden from multi-microorganisms in ewes based on an artificial neural networks approach and the GLM.

METHODS

A two-stage cluster survey design was conducted to estimate the seroprevalence of abortifacient microorganisms and to identify putative factors of infectious abortion.

RESULTS

The overall seroprevalence of Brucella was 70.7%, while Leptospira spp. was 55.2%, C. abortus was 21.9%, and B. ovis was 7.4%. Serological detection with four abortion-causing microorganisms was determined only in 0.87% of sheep sampled. The best GLM is integrated via serological detection of serovar Hardjo and Brucella ovis in animals of the slopes with elevation between 2600 and 2800 meters above sea level from the municipality of Xalatlaco. Other covariates included in the GLM, such as the sheep pen built with materials of metal grids and untreated wood, dirt and concrete floors, bed of straw, and the well water supply were also remained independently associated with infectious abortion. Approximately 80% of those respondents did not wear gloves or masks to prevent the transmission of the abortifacient zoonotic microorganisms.

CONCLUSIONS

Sensitizing stakeholders on good agricultural practices could improve public health surveillance. Further studies on the effect of animal-human transmission in such a setting is worthwhile to further support the One Health initiative.

A critical assessment of SARS-CoV-2 in aqueous environment: Existence, detection, survival, wastewater-based surveillance, inactivation methods, and effective management of COVID-19

In early January 2020, the causal agent of unspecified pneumonia cases detected in China and elsewhere was identified as a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and was the major cause of the COVID-19 outbreak. Later, the World Health Organization (WHO) proclaimed the COVID-19 pandemic a worldwide public health emergency on January 30, 2020. Since then, many studies have been published on this topic. In the present study, bibliometric analysis has been performed to analyze the research hotspots of the coronavirus. Coronavirus transmission, detection methods, potential risks of infection, and effective management practices have been discussed in the present review. Identification and quantification of SARS-CoV-2 viral loads in various water matrices have been reviewed. It was observed that the viral shedding through urine and feces of COVID-19-infected patients might be a primary mode of SARS-CoV-2 transmission in water and wastewater. In this context, the present review highlights wastewater-based epidemiology (WBE)/sewage surveillance, which can be utilized as an effective tool for tracking the transmission of COVID-19. This review also emphasizes the role of different disinfection techniques, such as chlorination, ultraviolet irradiation, and ozonation, for the inactivation of coronavirus. In addition, the application of computational modeling methods has been discussed for the effective management of COVID-19.

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus's transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination.

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology

As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model's accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

A predictive analytics model for COVID-19 pandemic using artificial neural networks

The COVID-19 pandemic spread rapidly around the world and is currently one of the most leading causes of death and heath disaster in the world. Turkey, like most of the countries, has been negatively affected by COVID-19. The aim of this study is to design a predictive model based on artificial neural network (ANN) model to predict the future number of daily cases and deaths caused by COVID-19 in a generalized way to fit different countries’ spreads. In this study, we used a dataset between 11 March 2020 and 23 January 2021 for different countries. This study provides an ANN model to assist the government to take preventive action for hospitals and medical facilities. The results show that there is an 86% overall accuracy in predicting the mortality rate and 87% in predicting the number of cases.