Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.

AI and Dynamic Prediction of Deterioration in Covid-19

During the COVID-19 pandemic it became evident that outcome prediction of patients is crucial for triaging, when resources are limited and enable early start or increase of available therapeutic support. COVID-19 demographic risk factors for severe disease and death were rapidly established, including age and sex. Common Clinical Decision Support Systems (CDSS) and Early Warning Systems (EWS) have been used to triage based on demographics, vital signs and laboratory results. However, all of these have limitations, such as dependency of laboratory investigations or set threshold values, were derived from more or less specific cohort studies. Instead, individual illness dynamics and patterns of recovery might be essential characteristics in understanding the critical course of illness.The pandemic has been a game changer for data, and the concept of real-time massive health data has emerged as one of the important tools in battling the pandemic. We here describe the advantages and limitations of established risk scoring systems and show how artificial intelligence applied on dynamic vital parameter changes, may help to predict critical illness, adverse events and death in patients hospitalized with COVID-19.Machine learning assisted dynamic analysis can improve and give patient-specific prediction in Clinical Decision Support systems that have the potential of reducing both morbidity and mortality. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Person-Environment Fit: Implications for Vocational Students' Career in the Era of Crisis

Person-environment fit theory (PE-fit) theory emphasises a match between a person's attribute (P) and the workplace environment (E). However, a differential predictions hypothesis emphasises the different contributions of personal and environmental inputs to outcomes. Higher education students in Hong Kong (N = 380) completed a survey on their personal interest (P) and the contemporary threatening environment (E) (fear of pandemic, social unrest, international disputes) related to tourism-related outcomes (intent to join tourism, lifelong commitment, leadership, and anxiety) during COVID-19. Structural equation modelling found that P strongly predicted Intent, Lifelong, and Leadership, whereas E strongly predicted Anxiety, supporting the differential predictions hypothesis. PE-fit (P × positive E) predicted Intent in addition to the prediction of P, supporting the PE-fit hypothesis. The findings imply the different merits of PE-fit and differential predictions hypotheses for various vocational outcomes, and the importance of reinforcing students' interest to launch their career in challenging times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Predict Turnaround Time of Hospital Discharge

COVID-19 pandemics lead to further shortages of beds globally. Ningbo No.1 Hospital implemented an integrated digital management system to tackle inefficiency in the discharge process, however, this problem is not fully solved. To help the hospital fully address this problem, this article identifies the problems in the hospital's dataset and proposes a methodology for the machine learning model training in order to predict the patient's leaving time, which provides a space for the hospital to improve the discharge process when procedures simplify, integration and digitalization are done. © 2022 IEEE.

The COVID-19 Pandemic and the International Geographical Union

Having broken out in late 2019, COVID-19 has resulted in a once-in-a-century health emergency that has rapidly evolved into a global socio-economic crisis. As of March 2022, more than 450 million people were infected by the SARS-CoV-2 virus, the cause of COVID-19, resulting in more than six million deaths (WHO, Coronavirus disease (COVID-19) situation dashboard, 2022). The medical systems of many countries have been stretched to the verge of collapse and more than half of the global labor force has stood down. Not only has the pandemic doubled the number of people at risk of starvation to 270 million (Nature, 589:329-330, 2021), but it also pushed 100 million people into poverty in 2020, triggering the worst global recession since World War II (Blake and Wadhwa, 2020 year in review: the impact of COVID-19 in 12 charts, 2020), and increasing the risk of exposure to other pandemics related to ecosystem degradation (IPBES, Workshop report on biodiversity and pandemics of the intergovernmental platform on biodiversity and ecosystem services. Retrieved from Bonn, Germany, 2020;Yin et al., Geogr Sustain 2(1):68-73, 2021). The normal functioning of many organizations has also been hampered by the pandemic and disruptions to the global travel and tourism industry have been unprecedented. By way of an example, travel restrictions led to the postponement of the 2020 34th International Geographical Congress to the following year and, ultimately, the decision was made to transition to an entirely online format for the event. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Determination of Anti-SARS-CoV-2 Activity of Compounds Based on Machine Learning

In 2020, the outbreak of pneumonia caused by novel coronavirus spread rapidly all over the world. In the absence of a specific drug, novel coronavirus is still pandemic all over the world. In this paper, we proposed an improved molecular activity prediction model by adding feature selection method on the basis of comparing different methods to extract molecular features and machine learning models. We first used the anti-SARS-CoV-2 compounds reported in recent literatures to construct the data set, and then constructed three machine learning models. In addition, we tried to use three methods to extract molecular features in each model. In order to further improve the performance of the model, we add three feature selection methods. Through the comparison of different models, finally, we used FCFP to extract molecular features and added lasso feature selection method to establish the SVM model. Its test set accuracy is 90.0%, and the AUC value is 0.961, which could well predict the anti-SARS-CoV-2 activity of the compound. Our model can be used to speed up the research and discovery of anti-SARS-CoV-2 drugs. © 2022 IEEE.

MACHINE LEARNING-BASED VIRTUAL SCREENING, MOLECULAR DOCKING, DRUG-LIKENESS, PHARMACOKINETICS AND TOXICITY ANALYSES TO IDENTIFY NEW NATURAL INHIBITORS OF THE GLYCOPROTEIN SPIKE (S1) OF SARS-CoV-2

To identify natural bioactive compounds (NBCs) as potential inhibitors of spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski's and Veber's rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed greater affinity with the S1 (affinity energy <-7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15 and NBC27 had better results in ADMET predictions. These had similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.

Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

Physiological signals indicate a person's physical and mental state at any given time. Accordingly, many studies extract physiological signals from the human body with non-contact methods, and most of them require facial feature points. However, under COVID-19, wearing a mask has become a must in many places, so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research. In this study, RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate, blood pressure, respiratory rate, and forehead temperature for people wearing masks due to the pandemic. Using the green (G) minus red (R) signal in the RGB image, the region of interest (ROI) is established in the forehead and nose bridge regions. The photoplethysmography (PPG) waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method, baseline drift calibration, normalization, and bandpass filtering. The relevant parameters in Deep Neural Networks (DNN) for the regression model can correctly predict the heartbeat and blood pressure. In addition, the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths. Meanwhile, the thermal image can be used to read the temperature average of the ROI of the forehead, and the forehead temperature can be obtained smoothly. The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%∼3% and the error of forehead temperature within ±0.5°C. © 2023 Tech Science Press. All rights reserved.

Covid-19 and Diabetes Risk Prediction for Diabetic Patient using Advance Machine Learning Techniques and Fuzzy Inference System

Diabetes has become a common and critical disease which generally occurs due to the presence of high sugar in blood for long time. A diabetic patient has to follow different rules and restrictions where he/she has to be under proper attention by measuring diabetes level frequently to avoid unexpected risk. The risk become more when patient even doesn't know that he/she is already having diabetes and doesn't follow those restrictions. To prevent this risk, everyone should check the diabetes status to be sure. With the same target different system using machine learning techniques have been introduced which can predict the diabetes status of a patient. But the challenging fact is that the performances and accuracy of those models are questionable where there may be a huge risk of patient's life. The conventional systems are not able to show that which level of diabetes a patient can have using the previous records. To solve this issue, through this paper an efficient system has been proposed with which the diabetes status can be predicted correctly. The proposed system can also show the complexity of diabetes as well as the Covid-19 risk percentage that can also be possible to measure. After comparing several machine learning techniques, the suitable model has been selected where high level of accuracy has been ensured in term of predicting the disease. © 2022 IEEE.

Construction and Application of Bid Fraud Prediction Model Based on AdaBoost Algorithm

In recent years, with the development of Internet big data technology and e-commerce platform, many active offline transaction methods have gradually shifted to online. Online auctions have come a long way due to COVID-19, but bidding fraud has seriously disrupted the health of the industry. In this paper, the AdaBoost model is used to build a bidding fraud prediction model, and the prediction performance of the model is verified by data experiments, and it is found that it has a high accuracy for identifying bidding fraud. At present, there are few prediction models for bidding fraud, and it has broad development prospects. © 2022 IEEE.

The YOPP Final Summit: Assessing Past and Forecasting Future Polar Prediction Research

The successes of YOPP from the presentations and keynote presentations included * a better understanding of the impact of key polar measurements (radiosondes and space-based instruments such as microwave radiometers), and recent advancements in the current NWP observing system, achieved through coordinated OSEs in both polar regions (e.g., Sandu et al. 2021);* enhanced understanding of the linkages between Arctic and midlatitude weather (e.g., Day et al. 2019);* advancements in the atmosphere–ocean–sea ice and atmosphere–land–cryosphere coupling in NWP, and in assessing and recognizing the added value of coupling in Earth system models (e.g., Bauer et al. 2016);* deployment of tailored polar observation campaigns to address yet-unresolved polar processes (e.g., Renfrew et al. 2019);* progress in verification and forecasting techniques for sea ice, including a novel headline score (e.g., Goessling and Jung 2018);* advances in process understanding and process-based evaluation with the establishment of the YOPPsiteMIP framework and tools (Svensson 2020);* better understanding of emerging societal and stakeholder needs in the Arctic and Antarctic (e.g., Dawson et al. 2017);and * innovative transdisciplinary methodologies for coproducing salient information services for various user groups (Jeuring and Lamers 2021). The YOPP Final Summit identified a number of areas worthy of prioritized research in the area of environmental prediction and services for the polar regions: * coupled atmosphere, sea ice, and ocean models with an emphasis on advanced parameterizations and enhanced resolution at which critical phenomena start to be resolved (e.g., ocean eddies);* improved definition and representation of stable boundary layer processes, including mixed-phase clouds and aerosols;incorporation of wave–ice–ocean interactions;* radiance assimilation over sea ice, land ice, and ice sheets;understanding of linkages between polar regions and lower latitudes from a prediction perspective;* exploring the limits of predictability of the atmosphere–cryosphere–ocean system;* an examination of the observational representativeness over land, sea ice, and ocean;better representation of the hydrological cycle;and * transdisciplinary work with the social science community around the use of forecasting services and operational decision-making to name but a few. The presentations and discussions at the YOPP Final Summit identified the major legacy elements of YOPP: the YOPPsiteMIP approach to enable easy comparison of collocated multivariate model and observational outputs with the aim of enhancing process understanding, the development of an international and multi-institutional community across many disciplines investigating aspects of polar prediction and services, the YOPP Data Portal3 (https://yopp.met.no/), and the education and training delivered to early-career polar researchers. Next steps Logistical issues, the COVID-19 pandemic, but also new scientific questions (e.g., the value of targeted observations in the Southern Hemisphere), as well as technical issues emerging toward the end of the YOPP Consolidation Phase, resulted in the decision to continue the following three YOPP activities to the end of 2023: (i) YOPP Southern Hemisphere (YOPP-SH);(ii) Model Intercomparison and Improvement Project (MIIP);of which YOPPSiteMIP is a critical element;and (iii) the Societal, Economics and Research Applications (PPP-SERA) Task Team.

Covid Wave Prediction using SARIMA Machine Learning Algorithm

A speedy and accurate diagnosis of COVID-19 is made possible by effective SARS-Co V -2 screening, which can also lessen the strain on health care systems. There have been built prediction models that assess the likelihood of infection by combining a number of parameters. These are intended to help medical professionals worldwide prioritize patients, particularly when there are few healthcare resources available. From a dataset of 51,831 tested people, out of which 4,769 were confirmed to have COVID-19 virus, a machine learning method was developed and trained. Records of the following week with 47,401 tested people, of which 3,624 were tested positive was also considered. Our method accurately predicted the COVID-19 test results using eight binary characteristics, including gender, age 60, known contact with an infected person, and the presence of five early clinical signs. © 2023 IEEE.

Healthcare Diagnostics Service Using Federated Learning

Healthcare systems around the world rely on powerful computational prediction tools in order to make accurate diagnostics with regard to the human body. In order to estimate the severity of lung damage post-COVID infection, healthcare providers rely on AI prediction tools to perform diagnosis. While such tools exist at a rudimentary level, there is a growing demand for more reliable and democratised systems that train models over a diverse data-set. To that end, the focus of this research paper turns to federated learning, a distributed machine learning paradigm. The system proposed consists of a central server that pools features and weights across various nodes, thereby cutting bias in the prediction models. This also achieves data decentralisation which ensures patient privacy. An end-to-end application is realised that facilitates distributed training of batch data that is visualised in real-time with the help of sockets. The application also features an inference service, classifying chest x-rays based on whether the image displays damage in case of Pneumonia. © 2023 IEEE.

Prediction and Analysis of COVID-19 Epidemic Based on Improved GEP Algorithm to Optimize SEIR Mode

In 2019, COVID-19 (CoronaVirus Disease 2019) broke out all over the world. COVID-19 is an infectious disease, which has a huge impact on the global economy. It is very difficult to prevent and control the epidemic situation of this infectious disease. At present, many SEIR(Susceptible Exposed Infected Recovered)models are used to predict the number of infectious diseases, which has the shortcomings of low prediction accuracy and inaccurate inflection point prediction. Therefore, this paper proposes that the prediction and analysis of COVID-19 based on improved GEP algorithm and optimized SEIR model can improve the prediction accuracy and inflection point prediction accuracy, and provide a theoretical basis for epidemic prevention of large-scale infectious diseases in the future. The algorithm. First, establish SEIR (Susceptible Exposed Infected Recovered) model to analyze the epidemic trend, and then use improved GEP (Gene Expression Programming) algorithm to analyze the infection coefficient of SEIR model beta And coefficient of restitution y, perform parameter estimation to optimize the initial value I and recovery coefficient of the infected population y and so on to improve the accuracy of model prediction. The experimental data take the number of COVID-19 infected people in the United States, China, the United Kingdom and Italy as examples. The results show that the SEIR model optimized based on the improved GEP algorithm conforms to the inflection point of the actual data, and the average error value is 1.32%. The algorithm provides a theoretical basis for the future epidemic prevention. © 2022 IEEE.

Pandemic Boosts Prospects for Recovery of Rural Tourism in Serbia

Rural tourism in Serbia had its chance to shine with the advent of the COVID-19 pandemic. The aim of this study was to determine to what extent the quality of rural service can contribute to improving rural tourism, and predictions for the continuation of the trend in terms of increasing the number of overnight stays in rural households. The obtained results show a small number of services in the sector could be improved, but that all elements except price value can influence the future development of rural tourism and that the number of overnight stays is expected to continue to grow. The importance and innovativeness of the research is reflected in the specific methodology that was applied, and the results complement those of previous research. It has been shown that villages in Serbia can create a barrier against COVID-19 through tourism. © 2023 by the authors.

Computational predictors of the predominant protein function: SARS-CoV-2 case

In this chapter, we describe the main molecular features of SARS-CoV-2 that cause COVID-19 disease, as well as a high-efficiency computational prediction called Polarity Index Method®. We also introduce a molecular classification of the RNA virus and DNA virus families and two main classifications: supervised and non-supervised algorithms of the predictions of the predominant function of proteins. Finally, some results obtained by the proposed non-supervised method are given, as well as some particularities found about the linear representation of proteins. © 2022 Scrivener Publishing LLC.

Assessment of Automated Parking Garage Services as a Means to Sustainable Traffic Development in a Mid-Sized City

The research in this article deals with verifying the deficit of parking spaces from model examples in the city of Ostrava, Czech Republic. Specifically, it deals with the possibilities of solving these deficits using automated parking systems. The main data collection took place between 2010 and 2019;later, supplemental lockdown data (up until May 2022) were obtained. The main objective of this article was to use data to determine the profitability and functionality of automated parking systems in mid-sized cities such as Ostrava. The RING system was chosen as a suitable model for the automated parking system. The data (using a least-squares approximation) were used via statistical methods to make predictions for future years, including the construction of confidence limits for a given significance level. Based on data from 2011–2019, we found that the RING system would be profitable with a probability of 92.45% in the following years. We compared these predictions with the actual data and made a new prediction.

Predicting Order Processing Times in E-Pharmacy Supply Chains During COVID Pandemic Using Machine learning—A Real-World Study

The purpose of this paper is to solve the problem of processing time prediction for orders for medical supplies placed through a large real-world e-Pharmacy—in a post-COVID-lockdown world—using artificial intelligence (AI) and machine learning (ML) techniques. We use an ensemble of ML regressors to predict the processing times of orders for medical supplies and an ensemble of ML classifiers to predict the shipment times of deliverables. We use intelligent model stacking methods to obtain performance improvements for our models. On exact match performance measurement scheme, our solution produces 548.49%, and on a 3-day range performance measurement scheme, our solution produces 25% improvement over the existing statistical solution implemented at the said e-Pharmacy. This is an important problem because when an e-Pharmacy can predict in advance the time elapsed between medical order placement and the time the order gets shipped out, the said e-Pharmacy can implement measures and controls to optimize the speed of fulfillment. We are one of the first to study real-world e-Pharmacy supply chain from the perspective of order processing time prediction under post-COVID-19-lockdown conditions and come up with a novel ML ensemble stacking approach to make predictions. The value this work provides is that we have shown that the adoption of AI and ML techniques in e-pharmacy supply chains would result in infusing certainty in the supply of therapeutics in these uncertain COVID lockdown times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Classification And Errors Analysis of COVID-19 Using Supervised Machine Learning Algorithm and Visualisation

During pandemic many people died as a result of the covid-19 sickness, which appeared in 2019 and spread over the world. The objective of research work is to wards the occurrence of COVID to improve classification accuracy and threshold curve predictions on real-life dataset for Receiver Operator Characteristics (ROC) value. This paper goals the real-life COVID patients from the five countries to test the experiment. The proposed methodology involves of two steps;used Weka for calculating the accuracy by applying Decision Table machine learning classifier and compare the results of all the five countries, secondly, the improvement in ROC value in terms of initial care predictions by area under ROC analysis. For our COVID dataset has 209 instances and 16 attributes, Weka has performed on the number of training instances are 184, number of Rules applied is 20, search direction has been applied in forward direction, total number of subsets evaluated is 96, merit of best subset found is 82.609 and time taken to build model is 0. 06 seconds. One advantage of our suggested mode list hat it keeps the original data intact, ensuring experiment quality. A further advantage is that the model can be used with additional data sets to produce the highest accuracy and ROC analysis out comes.Copyright © 2022, Anka Publishers. All rights reserved.

Policy Driven Epidemiological (PDE) Model for Prediction of COVID-19 in India

The fast spread of COVID-19 has made it a global issue. Despite various efforts, proper forecasting of COVID-19 spread is still in question. Government lockdown policies play a critical role in controlling the spread of coronavirus. However, existing prediction models have ignored lockdown policies and only focused on other features such as age, sex ratio, travel history, daily cases etc. This work proposes a Policy Driven Epidemiological (PDE) Model with Temporal, Structural, Profile, Policy and Interaction Features to forecast COVID-19 in India and its 6 states. PDE model integrates two models: Susceptible-Infected-Recovered-Deceased (SIRD) and Topical affinity propagation (TAP) model to predict the infection spread within a network for a given set of infected users. The performance of PDE model is assessed with respect to linear regression model, three epidemiological models (Susceptible-Infectious-Recovered-Model (SIR), Susceptible-Exposed-Infectious-Recovered-Model (SEIR) and SIRD) and two diffusion models (Time Constant Cascade Model and Time Decay Feature Cascade Model). Experimental evaluation for India and six Indian states with respect to different government policies from 15th June to 30th June, i.e., Maharashtra, Gujarat, Tamil Nadu, Delhi, Rajasthan and Uttar Pradesh divulge that prediction accuracy of PDE model is in close proximity with the real time for the considered time frame. Results illustrate that PDE model predicted the COVID-19 cases up to 94% accuracy and reduced the Normalize Mean Squared Error (NMSE) up to 50%, 35% and 42% with respect to linear regression, epidemiological models and diffusion models, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.