Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods.

The global economy has suffered losses as a result of the COVID-19 epidemic. Accurate and effective predictive models are necessary for the governance and readiness of the healthcare system and its resources and, ultimately, for the prevention of the spread of illness. The primary objective of the project is to build a robust, universal method for predicting COVID-19-positive cases. Collaborators will benefit from this while developing and revising their pandemic response plans. For accurate prediction of the spread of COVID-19, the research recommends an adaptive gradient LSTM model (AGLSTM) using multivariate time series data. RNN, LSTM, LASSO regression, Ada-Boost, Light Gradient Boosting and KNN models are also used in the research, which accurately and reliably predict the course of this unpleasant disease. The proposed technique is evaluated under two different experimental conditions. The former uses case studies from India to validate the methodology, while the latter uses data fusion and transfer-learning techniques to reuse data and models to predict the onset of COVID-19. The model extracts important advanced features that influence the COVID-19 cases using a convolutional neural network and predicts the cases using adaptive LSTM after CNN processes the data. The experiment results show that the output of AGLSTM outperforms with an accuracy of 99.81% and requires only a short time for training and prediction.

COVID-19 prediction with Chest X-Ray images using CNN

Coronavirus has outbreak as an epidemic disease, created a pandemic situation for the public health across the Globe. Screening for the large masses is extremely crucial to control disease for the people in a neighborhood. Real-time-PCR[18] is the general diagnostic approach for pathological examination. However, the increasing figure of false results from the test has created a way in choosing alternative procedures. COVID-19 patient's X-rays images of chest has emerged as a significant approach for screening the COVID-19 disease. However, accuracy depends on the knowledge of a radiologist. X-Ray images of lungs may be proper assistive tool for diagnosis in reducing the burden of the doctor. Deep Learning techniques, especially Convolutional Neural Networks (CNN), have been shown to be effective for classification of images in the medical field. Diagnosing the COVID-19 using the four types of Deep-CNN models because they have pre-trained weights. Model needs to pre-trained on the ImageNet database in simplifying the large datasets. CNN-based architectures were found to be ideal in diagnosing the COVID-19 disease. The model having an efficiency of 0.9835 in accuracy, precision of 0.915, sensitivity of 0.963, specificity with 0.972, 0.987 F1 Score and 0.925 ROC AUC. © 2023 IEEE.

Forecasting Emerging Pandemics with Transfer Learning and Location-aware News Analysis

Monitoring and forecasting epidemic diseases are of prime importance to public health organizations and policymakers in taking proper measures and adjusting prevention tactics. Early prediction is especially important to restrict the spread of emerging pandemics such as COVID-19. However, despite increasing research and development for various epidemics, several challenges remain unresolved. On the one hand, early-stage epidemic prediction for emerging new diseases is difficult because of data paucity and lack of experience. On the other hand, many existing studies ignore or fail to leverage the contribution of social factors such as news, geolocations, and climate. Even though some researchers have recognized the profound impact of social features, capturing the dynamic correlation between these features and pandemics requires an extensive understanding of heterogeneous formats of data and mechanisms. In this paper, we design TLSS, a neural transfer learning architecture for learning and transferring general characteristics of existing epidemic diseases to predict a new pandemic. We propose a new feature module to learn the impact of news sentiment and semantic information on epidemic transmission. We then combine this information with historical time-series features to forecast future infection cases in a dynamic propagation process. We compare the proposed model with several state-of-the-art statistics approaches and deep learning methods in epidemic prediction with different lead times of ground truth. We conducted extensive experiments on three stages of COVID-19 development in the United States. Our experiment demonstrates that our approach has strong predictive performance for COVID infection cases, especially with longer lead times. © 2022 IEEE.

How big of an impact do asymptomatic people have on the dynamics of an epidemic?

Asymptomatic carriers serve as a potential source of transmission of epidemic diseases. Exposed people who develop symptoms only get tested and remain isolated in their homes or sometimes in hospitals when needed. In contrast, the asymptomatic individuals go untested and spread the disease silently as they roam freely throughout their entire infectious lifetime. The work intends to explore the role of asymptomatic carriers in the transmission of epidemic diseases and investigate suitable optimal control strategies. We propose a SEIAQR compartmental model subdividing the total population into six different compartments. To illustrate the model's implication, we estimate the number of asymptomatic individuals using COVID-19 data during June 9–July 18, 2021 from Bangladesh. We then analyze the model to explore whether the epidemic subsides if the asymptomatic individuals are tested randomly and isolated. Finally, to gain a better understanding of the potential of this unidentified transmission route, we propose an optimal control model considering two different control strategies: personal protective measures and isolation of asymptomatic carriers through random testing. Our results show that simultaneous implementation of both control strategies can reduce the epidemic early. Most importantly, sustained effort in identifying and isolation of asymptotic individuals allows relaxation in personal protective measures. © 2023

Analysis of medication regularities of epidemic disease prescription in Treatise on Febrile Diseases and mechanism of core drugs in treatment of COVID-19

Objective To analyze the medication rules of related epidemic disease prescription in Treatise on Febrile Diseases based on data mining, and the mechanism of "Chaihu (Bupleuri Radix)-Huangqin (Scutellariae Radix)” as the core drugs in the treatment of coronavirus disease 2019 (COVID-19) by network pharmacology, in order to explore the contemporary value of classical prescriptions in the treatment of epidemic diseases. Methods The prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were screened, and the medication rules such as drug frequency, flavor and meridian tropism as well as correlation, apriori algorithm were analyzed by using software such as R language. The mechanism of the core drugs in the medication pattern in the treatment of COVID-19 was explored by the network pharmacology. A "disease-drug-ingredient-target” network was constructed on the selected components and targets with Cytoscape. The key targets were introduced into String database for network analysis of protein-protein interaction (PPI), and gene ontology (GO) functional analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted in R language. Results A total of 61 prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were included, including 52 traditional Chinese medicines (TCMs). In the top 20 high-frequency drugs, warm drugs, spicy drugs and qitonifying drugs were mainly used, mostly in the spleen and lung meridian. Chaihu (Bupleuri Radix) and Huangqin (Scutellariae Radix) herb pair had the strongest correlation. A total of five clusters were excavated: supplemented formula of Xiaochaihu Decoction (小柴胡汤), Sini Decoction (四逆汤), supplemented formule of Maxing Shigan Decoction (麻杏石甘汤), Fuling Baizhu Decoction (茯苓白术汤) and Dachengqi Decoction (大承气汤). A total of 45 active ingredients, 189 action targets of Bupleuri Radix-Scutellariae Radix herb pair, and 543 targets of COVID-19 were obtained from TCMSP and Genecards, and 64 intersection targets were generated. The results of the network analysis showed that the main components of core drugs pair against COVID-19 may be quercetin, wogonin, kaempferol baicalein, acacetin etc., and the core targets may be VEGFA, TNF, IL-6, TP53, AKT1, CASP3, CXCL8, PTGS2, etc. A total of 1871 related entries and 164 pathways were obtained by GO and KEGG enrichment analysis, respectively. Conclusion In Treatise on Febrile Diseases, the treatment of epidemic diseases mainly chose pungent, warm, spleen-invigorating and qi-tonifying herbs, such as Xiaochaihu Decoction, Sini Decoction and Dachengqi Decoction, etc. It was found that Bupleuri Radix-Scutellariae Radix core herb pair prevent and treat COVID-19 through multi-target targets such as PTGS2, IL-6 and TNF. The ancient prescriptions for treating epidemic disease in Treatise on Febrile Diseases may have significant reference value for the prevention and treatment of new epidemic diseases today. © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Diagnosis of Covid-19 Via Patient Breath Data Using Artificial Intelligence

Using machine learning algorithms for the rapid diagnosis and detection of the COVID-19 pandemic and isolating the patients from crowded environments are very important to controlling the epidemic. This study aims to develop a point-of-care testing (POCT) system that can detect COVID-19 by detecting volatile organic compounds (VOCs) in a patient's exhaled breath using the Gradient Boosted Trees Learner Algorithm. 294 breath samples were collected from 142 patients at Istanbul Medipol Mega Hospital between December 2020 and March 2021. 84 cases out of 142 resulted in negatives, and 58 cases resulted in positives. All these breath samples have been converted into numeric values through five air sensors. 10% of the data have been used for the validation of the model, while 75% of the test data have been used for training an AI model to predict the coronavirus presence. 25% have been used for testing. The SMOTE oversampling method was used to increase the training set size and reduce the imbalance of negative and positive classes in training and test data. Different machine learning algorithms have also been tried to develop the e-nose model. The test results have suggested that the Gradient Boosting algorithm created the best model. The Gradient Boosting model provides 95% recall when predicting COVID-19 positive patients and 96% accuracy when predicting COVID-19 negative patients. © The Authors.

A discrete-time survival model for porcine epidemic diarrhoea virus.

Since the arrival of porcine epidemic diarrhea virus (PEDV) in the United States in 2013, elimination and control programmes have had partial success. The dynamics of its spread are hard to quantify, though previous work has shown that local transmission and the transfer of pigs within production systems are most associated with the spread of PEDV. Our work relies on the history of PEDV infections in a region of the southeastern United States. This infection data is complemented by farm-level features and extensive industry data on the movement of both pigs and vehicles. We implement a discrete-time survival model and evaluate different approaches to modelling the local-transmission and network effects. We find strong evidence in that the local-transmission and pig-movement effects are associated with the spread of PEDV, even while controlling for seasonality, farm-level features and the possible spread of disease by vehicles. Our fully Bayesian model permits full uncertainty quantification of these effects. Our farm-level out-of-sample predictions have a receiver-operating characteristic area under the curve (AUC) of 0.779 and a precision-recall AUC of 0.097. The quantification of these effects in a comprehensive model allows stakeholders to make more informed decisions about disease prevention efforts.

Analysis of medication regularities of epidemic disease prescription in Treatise on Febrile Diseases and mechanism of core drugs in treatment of COVID-19

Objective To analyze the medication rules of related epidemic disease prescription in Treatise on Febrile Diseases based on data mining, and the mechanism of "Chaihu (Bupleuri Radix)-Huangqin (Scutellariae Radix)" as the core drugs in the treatment of coronavirus disease 2019 (COVID-19) by network pharmacology, in order to explore the contemporary value of classical prescriptions in the treatment of epidemic diseases. Methods The prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were screened, and the medication rules such as drug frequency, flavor and meridian tropism as well as correlation, apriori algorithm were analyzed by using software such as R language. The mechanism of the core drugs in the medication pattern in the treatment of COVID-19 was explored by the network pharmacology. A "disease-drug-ingredient-target" network was constructed on the selected components and targets with Cytoscape. The key targets were introduced into String database for network analysis of protein-protein interaction (PPI), and gene ontology (GO) functional analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted in R language. Results A total of 61 prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were included, including 52 traditional Chinese medicines (TCMs). In the top 20 high-frequency drugs, warm drugs, spicy drugs and qitonifying drugs were mainly used, mostly in the spleen and lung meridian. Chaihu (Bupleuri Radix) and Huangqin (Scutellariae Radix) herb pair had the strongest correlation. A total of five clusters were excavated: supplemented formula of Xiaochaihu Decoction (), Sini Decoction (), supplemented formule of Maxing Shigan Decoction (), Fuling Baizhu Decoction () and Dachengqi Decoction (). A total of 45 active ingredients, 189 action targets of Bupleuri Radix-Scutellariae Radix herb pair, and 543 targets of COVID-19 were obtained from TCMSP and Genecards, and 64 intersection targets were generated. The results of the network analysis showed that the main components of core drugs pair against COVID-19 may be quercetin, wogonin, kaempferol baicalein, acacetin etc., and the core targets may be VEGFA, TNF, IL-6, TP53, AKT1, CASP3, CXCL8, PTGS2, etc. A total of 1871 related entries and 164 pathways were obtained by GO and KEGG enrichment analysis, respectively. Conclusion In Treatise on Febrile Diseases, the treatment of epidemic diseases mainly chose pungent, warm, spleen-invigorating and qi-tonifying herbs, such as Xiaochaihu Decoction, Sini Decoction and Dachengqi Decoction, etc. It was found that Bupleuri Radix-Scutellariae Radix core herb pair prevent and treat COVID-19 through multi-target targets such as PTGS2, IL-6 and TNF. The ancient prescriptions for treating epidemic disease in Treatise on Febrile Diseases may have significant reference value for the prevention and treatment of new epidemic diseases today. Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Analysis of COVID-19 Epidemic Disease Dynamics Using Deep Learning

COVID-19 has induced anxiety, depression, and fear among people around the world with its cases. During this period, people undergo mixed emotion. Social media is a tool that affected human life during this time in a dominant manner. Twitter is a trending social media platform. Analyzing sentiment of tweets related to COVID-19 can help to analyze the sentiments around the world. In this system, we have taken the dataset which contains tweets related to COVID-19 from IEEE dataport. SVM and LSTM models are built which classifies the tweets as positive, negative, and neutral accordingly. The performance of LSTM model is further analyzed by using hyperparameter tuning method. LSTM gave better results than SVM. It gave an accuracy of 94.58%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Modelling the Effect of Vaccination and Human Behaviour on the Spread of Epidemic Diseases on Temporal Networks

Motivated by the increasing number of COVID-19 cases that have been observed in many countries after the vaccination campaign and relaxation of non-pharmaceutical interventions (NPIs), we propose a network model for the spread of recurrent epidemic diseases in a partially vaccinated population. The model encapsulates several realistic features, such as different vaccine efficacy against transmission and development of severe symptoms, testing practices, implementation of NPIs, isolation of detected individuals, and human behaviour. Using a mean-field approach, we analytically derive the epidemic threshold of the model and, if the system is below such a threshold, we compute the epidemic prevalence at the endemic equilibrium. These theoretical results show that precautious human behaviour and effective testing practices are key towards avoiding epidemic outbreaks. Interestingly, we found that, in many realistic scenarios, vaccination is successful in mitigating the outbreak by reducing the prevalence of seriously ill patients, but it could be a double-edged sword, favouring resurgent outbreaks, and it thus calls for higher testing rates, more cautiousness and responsibility among the population, or the reintroduction of NPIs to achieve full eradication. © 2022 EUCA.

Back to the future: Covid-19 and the recurring debate over social determinants of disease, and health.

Since the 1980s, a large literature has developed on the social determinants of health, primarily non-communicable diseases for which mortality and morbidity can be shown to change across a socioeconomic gradient. Primarily regional or national in focus, they are joined, today, with an increasing focus on international health and the effect of inequalities between nations effect disease generation and spread. Similar and earlier literatures first considered socioeconomic factors influencing disease incidence and intensity primarily at local and regional levels. One such literature was primarily "sanitarian," focusing on general infrastructure needs (safe water, for example) to create a beter health environment. A second, primarily nineteenth century literature focused on social inequalities and the epidemic diseases in specific populations. This paper seeks to review these separate foci and then combine them into a more comprehensive understanding of both the general and specific determinants of health and disease at local, national, and international scales of address. It notes that while disease dynamics have been long known that current literatures typically consider socioeconomic determinants at local, national, and global scales as a new phenomenon.

Incorporating mass vaccination into compartment models for infectious diseases.

The standard way of incorporating mass vaccination into a compartment model for an infectious disease is as a spontaneous transition process that applies to the entire susceptible class. The large degree of COVID-19 vaccine refusal, hesitancy, and ineligibility, and initial limitations of supply and distribution require reconsideration of this standard treatment. In this paper, we address these issues for models on endemic and epidemic time scales. On an endemic time scale, we partition the susceptible class into prevaccinated and unprotected subclasses and show that vaccine refusal/hesitancy/ineligibility has a significant impact on endemic behavior, particularly for diseases where immunity is short-lived. On an epidemic time scale, we develop a supply-limited Holling type 3 vaccination model and show that it is an excellent fit to vaccination data. We then extend the Holling model to a COVID-19 scenario in which the population is divided into two risk classes, with the high-risk class being prioritized for vaccination. In both cases, with and without risk stratification, we see significant differences in epidemiological outcomes between the Holling vaccination model and naive models. Finally, we use the new model to explore implications for public health policies in future pandemics.

Geographic Information Systems in Health and Human Services

The chapter begins with a general overview of how GIS has evolved in the health and human services over the last several decades and provides readers with important definitions and descriptions (Sects. 29.1 and 29.2). Sections 29.3 and 29.4 uncover how GIS became an important tool for epidemiologists in the work of tracking infectious diseases and perfecting the study of population health. Readers will also learn that GIS adoption by hospital marketers and planners in the United States accelerated rapidly after 1970, when US Census data became relatively freely available in digital form. The importance of the legendary work of the Dartmouth Health Care Atlas Project and its founder Jack Wennberg is also introduced. In areas where high GIS adoption rates occurred, such as in public health, we feature key applications such as immunization management, disease tracking, outbreak analysis, disease surveillance, syndromic surveillance, emergency preparedness and response, community health assessment, environmental health, chronic disease prevention, and animal and veterinary health. Section 29.5 describes how GIS education has expanded across the academic fields of public health, healthcare administration, and social services. It is pointed out that the material presented in this chapter is not intended to be an exhaustive examination of the history of GIS but, rather, a brief introduction and overview that will generate further interest and self-discovery. Section 29.6.6 considers the role of GIS in response to the COVID-19 pandemic. © 2022, Springer Nature Switzerland AG.

Using Wi-Fi Position System for Developing a Privacy-Preserving Contact Tracing System in University Campuses

One of the crucial tools to control and fight epidemic disease outbreaks (such as the fast-growing COVID-19 pandemic) is contact tracing in public buildings (such as universities). A contact tracing system can identify and alarm potential individuals who had close contacts with confirmed cases so that they can voluntarily self-quarantine. The current automated contact tracing systems, which mainly use smartphone sensors (e.g., GPS, Bluetooth), have two main challenges: (1) protecting the privacy of the users and (2) relying on GPS sensor, which does not work well indoors and in many urban settings. On the other hand, Wi-Fi positioning systems have been considered one of the most used technologies for creating a real-time indoor positioning system (IPS), especially in university campuses where the required infrastructure usually exists. This study aims to study the feasibility of using Wi-Fi location tracking technology to develop a conceptual privacy-preserving contact tracing system in university campuses. Such a contact tracing system relies on smartphones connected to the central Wi-Fi system's access points and the connected devices Mac addresses to inform at-risk users. This study performs a comprehensive literature review to study the applicability, current limitations, and future research directions of such technologies for contact tracing. Such technology could enhance the current automated contact tracing system in universities by illuminating the need to use cellphones' applications while protecting users' privacy. © 2021 Computing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021. All rights reserved.

The Brusselator Model of Epidemic Diseases Expansion and COVID-19 Statistics

In this work we examined the mathematical model of Brusselator previously proved for simulation the epidemic spread by the statistics data for COVID-19 expansion in many countries. Our estimations are in good agreement with statistics for two periods, which are 6 weeks one after another. Comparing changes in the fitting parameters during the 6 weeks for different countries we mentioned that in majority they are very similar, what means the adequacy of the model to the spread of the disease. Besides, carefully testing of the parameters change show the peculiarity of the development of diseases in every country. © 2022 IEEE.

Analysis of the effect on US manufacture industry during Covid-19 based on Fama-French five-factor model

It is crucial to find the fair value of financial assets for asset pricing model in relevant research and practice. With continuous improvement of the model, researchers are expected to improve the applicability and interpretation ability. Covid-19 is a rare global epidemic disease in human history, causing large-scale negative impact on the financial market. Therefore, it is necessary to study the practicability of asset pricing model, facing major and unpredictable factors. Based on the Fama French five-factor model, this paper compares the model factors of American manufacturing stocks from July 2019 to February 2020, and March 2020 to October 2020. The data mentioned in this paper are all selected from the database of Kenneth R. French's web. French, using the relevant information of American stock market to get various data. Multiple linear regression method and t-test are applied to analyze. The results showed that intercept investment. The asset pricing model changed from non-significant before the epidemic to significant;SMB coefficient is significant both before and after the epidemic, while coefficient increases after the epidemic;RMW is significant before the epidemic and is non-significant after the epidemic;MKT coefficient is significant both before and after the epidemic;CMA is non-significant both before and after the epidemic. Investors are advised to focus more on the explanatory power of MKT, SMB and HML factors on asset pricing when investing on US manufacturing stocks during the epidemic. © 2021 IEEE.

A Simulation Study of COVID-19 Out Break as an Epidemic Disease

The COVID-19 Pandemic is regarded as the worst public health disaster in the history of the world. As of September 2021, around 219 million cases of the coronavirus have been confirmed globally. The pandemic caused by the disease is considered a global threat. It has caused 4.5 million deaths globally. The coronavirus pandemic caused by the COVID-19 disease is disrupting various sectors of the economy. It has caused havoc in the aviation, retail, and financial markets. This study aims to investigate the effects of the Coronavirus to answer concerns about the number of people affected during influenza outbreaks. we propose to use the MATLAB tool to study the spread of infectious diseases across a population. This paper presents a spatially explicit simulation model of infectious illness to study the spatial dispersion of diseases in humans. © 2022 IEEE.

Immunization and Quarantine in the Light of the Prophetic Traditions with Reference to the UAE Quarantine Policies for Fighting COVID-19

Immunization is considered one of the most important means of protecting human beings against infectious disease. Allah, in His mercy, created means of immunization for the people. Immunization is more effective than any other means of controlling contagious diseases. Quarantine is also one of the most important means of curbing the spread of epidemic diseases in the contemporary era. The Prophet (PBUH) in several hadiths unequivocally explained the principles of immunization and quarantine. He prohibited people from entering the place affected by plague and prohibited the people of the place from leaving it. The aim of this paper is to study immunization and quarantine in the light of the Prophetic traditions with reference to the UAE's COVID19 infection control. The relationship between the two principles and the Prophetic traditions can thus be made clear. The research employs the analytical qualitative method using library-based research, examination of both primary sources of Islamic legislation (the Quran and the Sunna) and secondary sources (classical and modern literature of Muslim scholars). The research finds that there are a number of Prophetic traditions that mention medical immunization and quarantine. If put into practice, they would have a great impact in curbing the surge of any communicable disease and providing cure for other diseases.

Course Scheduling to Minimize Student Wait Times For University Buildings During Epidemics

Epidemic diseases bring many challenges to universities. In the case of airborne contagious diseases like COVID-19, health agencies' guidelines recommend that people maintain a physical distance of about 2 meters from each other. Enforcing such physical distancing on a university campus means that it will potentially take longer for students to get into and out of classrooms and buildings on campus. We use real course registration data from a large US university to study wait times students would encounter to enter and exit campus buildings while keeping the recommended 2 meter physical distance, and show that peak wait times can be longer than 20 minutes. We propose LBCS, a load-balanced course scheduling algorithm that intelligently reduces the peak wait time while ensuring that conflicting classes are scheduled at different times. Through simulations we show that LBCS can reduce the peak wait time by a factor of 3×, better than naive alternatives such as shifting some classes to the weekend or randomly perturbing class start times. © 2021 IEEE.

A new pulmonary disease diagnosis system based on EfficientNet and transfer learning: Pulmonary disease diagnosis based on EfficientNet and TL

Pulmonary epidemic diseases are one of the main causes of human death. Pulmonary epidemic diseases are usually highly contagious because they can be transmitted by droplets. In this study, we mainly focus on two types of common pulmonary epidemic diseases: COVID-19 and tuberculosis. COVID-19 has spread all around the globe since December 2019. The widespread COVID-19 caused the lockdown of the cities and economic losses. On the other hand, tuberculosis is among the ten highest human killers. Accurate and rapid diagnosis of pulmonary epidemic diseases is the primary step in clinical treatment. Therefore, we propose to leverage deep learning models to identify pulmonary epidemic diseases based on chest computed tomography (CT) images. We select the EfficientNet as the backbone model and employ a transfer learning method to train the model on our chest CT dataset. Experimental results reveal that our method can achieve promising classification performance, which is comparable to state-of-the-art approaches. © 2021 ACM.