DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

Dashboard Framework. A Tool for Threat Monitoring on the Example of Covid-19

The aim of the study is to create a dashboard framework to monitor the spread of the Covid-19 pandemic based on quantitative and qualitative data processing. The theoretical part propounds the basic assumptions underlying the concept of the dashboard framework. The paper presents the most important functions of the dashboard framework and examples of its adoption. The limitations related to the dashboard framework development are also indicated. As part of empirical research, an original model of the Dash-Cov framework was designed, enabling the acquisition and processing of quantitative and qualitative data on the spread of the SARS-CoV-2 virus. The developed model was pre-validated. Over 25,000 records and around 100,000 tweets were analyzed. The adopted research methods included statistical analysis and text analysis methods, in particular the sentiment analysis and the topic modeling. © 2022 IEEE Computer Society. All rights reserved.

The Development of Mobile Application for Assisting COVID-19 Antigen Test Kit Results Reading

The antigen test kits or ATKs have been widely used for screening COVID-19 infections because they can detect and give the results quickly and can be done easily by untrained patients. However, reading ATK test results could be difficult for some people and may lead to misinterpretations of the test results. This paper presents a preliminary study for developing a mobile application for helping in reading the results of the COVID-19 ATKs from an image using algorithms based on the YOLO object detection. The results are classified into 3 classes, negative, positive, and invalid. The negative and the invalid results are further refined by using the distances between the visible line and the letters on the test cassette. Experiments were conducted to test the efficiency and accuracy of the developed model with a mean of average precision or mAP of 0.986 and an F1 score of 0.970. The model was developed and put into a prototype mobile application using tools that support cross-platform technology. © 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).

Light-Weight Face Mask Detector

People's lives have been severely disrupted recently due to the COVID-19 outbreak's fast worldwide proliferation and transmission. An option for controlling the epidemic is to make individuals wear face masks in public. For such regulation, automatic and effective face detection systems are required. A facial mask recognition model for real-time video-recorded streaming is provided in this research, which categorizes the pictures as (with mask) or (without mask). A dataset from Kaggle was used to develop and assess the model. The suggested system is computationally more precise, efficient and lightweight when compared to other systems like VGG-16, DenseNet-121, and Inception-V3 which helped the developed model meet low end PC system requirements. The collected data set contains exactly 12,000 images and has a 98.1% performance training accuracy and a validation accuracy of 98.2%, which is achieved by using MobileNetV2. © 2022 IEEE.

Multiphase Compartment Models of Distribution of New Coronavirus Infections

A fundamentally new multiphase compartmental mathematical model for predicting the spread of several waves of coronavirus infection has been developed. Quality indicators in comparison with existing single-phase models are analyzed. The developed model will allow to model several waves of the process of spreading new coronavirus infections, to predict the process of loading the medical system, as well as the needs for staff, equipment and hospital beds during pandemics. © 2022 IEEE.

DEVELOPING GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODEL FOR SPATIO-TEMPORAL ANALYSIS OF COVID-19 POSITIVE CASES IN KALIMANTAN, INDONESIA

This article describes spatial-temporal analysis using the innovation of developing a Geographically Weighted Panel Regression model with a distance weighting function that includes the interaction between spatial and time aspects (GWPR-st). The method is a local regression technique that provides a parameter model that varies in each location through cross-sectional and time-series data observation units. This study develops a new model in spatial statistics and offers new methodologies in Geographic Models and Geographic Information Systems (GIS). This study aims to determine the factors that influence the increase in positive cases and map the spread of COVID-19 on the Kalimantan Regency/City Scale. The model applied in this study involves geographic weighting functions, including the Gaussian kernel, Bisquare kernel, and tricube kernel, which spatial interactions and time series have modified. This study uses national COVID-19 data from 56 regencies/cities until August 2021. According to the research results, the developed model with the geographic weighting of the Bisquare kernel function was considered the most acceptable method. The developed model, which is deemed capable of information the most substantial influence on the number of COVID-19 cases in Kalimantan, is health services, such as a shortage of doctors, number of hospitals, number of community health centers, and number of tuberculosis cases. The study results provide the local governments with decision-making recommendations for overcoming the COVID-19 problems in their regions. © 2022 Science Press. All rights reserved.

System dynamics model for changing transportation demand during the pandemic in Japan

To realize a sustainable transportation system, it is necessary to estimate the environmental load caused by transportation. Here transportation demand affects carbon dioxide emissions directly. In general, traffic simulations or scenario-based evaluations have been used to predict transportation demand. However, the COVID-19 pandemic that began in late 2019 has changed transportation demand drastically, and such changes have not been considered in conventional simulation models. Therefore, it is important to quantify the impact of the pandemic on transportation demand and its magnitude. In this study, we developed a model focused on describing the changes in transportation demand caused by the COVID-19 pandemic in Japan. We developed a model using system dynamics because this method is effective in describing socio-technical systems such as transportation demand. Based on related studies, we categorized transportation demand by purpose and modeled it based on the cause-and-effect relationship between the amount of transportation and the prevalence of infectious diseases. To verify the developed model, we compared actual data of 2020 in Japan with the output of the model. We set scenarios with varying parameter values that contribute significantly to changes in transportation demand, such as individual awareness of the pandemic. As a result, the developed model was verified at the behavioral level. This model can be used in developing future transportation systems. © 2022 Elsevier B.V.. All rights reserved.

Modeling and simulation of the potential indoor airborne transmission of SARS-CoV-2 virus through respiratory droplets

Respiratory viruses are transported from an infected person to other neighboring people through respiratory droplets. These small droplets are easily advected by air currents in a room and can potentially infect others. In this work, the spread of droplets released during coughing, talking, and normal breathing is numerically analyzed in a typical conference room setting. The room space is occupied by ten people, with eight people sitting around a conference table and two people standing. Four different scenarios are considered, with the air-conditioning turned on/off and people wearing/not-wearing masks, to understand the spread of respiratory droplets inside the room. The flow in the room is simulated using a multiphase mixture model with properties computed for the inhaled and exhaled air using fundamental gas relations. The transport of respiratory droplets is analyzed using the discrete phase model with a range of droplet sizes fitted to data from previous experimental studies. The mask is modeled as porous media with the properties of a woven fabric computed using a newly developed model for multilayered homemade masks. The human inhalation and exhalation are modeled using analytical functions to mimic the biological flow patterns during breathing, coughing, and talking. Important observations about the air flow and dispersion of respiratory droplets in the conference room are presented based on the numerical analysis. Animations of all the results are included to provide insight into flow physics of the various dynamic conditions occurring in the room during an ongoing meeting. Although this study is conducted for a typical conference room, the newly developed models and techniques can be applied to other confined environments. © 2022 Author(s).

Using a Rule-based Model to Detect Arabic Fake News Propagation during Covid-19

Since the emergence of the Covid-19, both factual and false information about the new virus has been disseminated. Fake news harms societies and must be combated. This research aims to identify Arabic fake news tweets and classify them into six categories: entertainment, health, politics, religious, social, and sports. The study also aims to uncover patterns in the spread of Arabic fake news associated with the Covid-19 pandemic. The researchers created an Arabic dictionary and used text classification based on a rule-based system to detect and categorize fake news. A dataset consisting of 5 million tweets was analyzed. The developed model achieves an overall accuracy of 78.1% with 70% precision and 98%recall. The model detected more than 26006 fake news tweets. Interestingly we found an association between the number of fake news tweets and dates. The result demonstrates that as more information and knowledge about Covid-19 become available over time, people's awareness increase, while the number of fake news tweets decreases. The categorization of false news indicates that the social category was highest in all Arab countries except Palestine, Qatar, Yemen, and Algeria. Conversely, fake news related to the entertainment category was the weakest dissemination in most Arab countries © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved