Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model's performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The model's ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.

D-DIMER AND FIBRINOGEN TRENDS IN PATIENTS WITH AUTOIMMUNE RHEUMATIC DISEASES INFECTED WITH SEVERE ACUTE RESPIRATORY SYNDROME CORONAVIRUS 2: A RETROSPECTIVE COMPARATIVE STUDY

BackgroundD-dimer and fibrinogen elevation has been observed in severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection which is associated with higher incidence of venous thromboembolism (VTE) and higher mortality rates. [1-3]. Autoimmune Rheumatic Diseases (ARDs) are associated with higher rates of VTE compared to general population [4]. Whether patients with ARDs infected with SARS-CoV2 have similar D-dimer and fibrinogen trends compared to patients without ARDs is unknown.ObjectivesCompare D-dimer and fibrinogen levels in patients with ARDs infected with SARS-CoV2 to patients without ARDs.MethodsPatients with ARDs infected with SARS-CoV2 were identified retrospectively from the electronic medical records (EMR) of Hamad Medical Corporation and matched (age and sex) to controls (1:3). D-dimer and fibrinogen levels were extracted electronically from EMR and stratified into six-time intervals defined in table 1. Day 0 was defined as the date of positive nasopharyngeal polymerase chain reaction swab test. 2 Independent Samples test (Mann-Whitney U) was used to compare the median (25th - 75th interquartile range [IQR]) level of D-dimer and fibrinogen between both study groups at the defined intervals.ResultsThe study included 203 cases and 551 controls with a mean (SD) age of 45.3 (11.7) and 44 (12.5) years, females were (122 [60.1%] vs. 297 [53.9%], p = 0.129), respectively.Distribution of ARDs was rheumatoid arthritis 86 (42.4%), spondyloarthropathy 33 (16.1%) and systemic lupus erythematosus 31 (15.7%) cases. 67% were on conventional synthetic disease modifying anti-rheumatic drugs (Cs-DMARDs), 15.8% on biological DMARDs and 4.9% on rituximab. About 83% of the ARDs group were in remission or low disease activity and 13% were in moderate or high disease activity.The median (25th - 75th IQR) level of D-dimer and fibrinogen were comparable between study groups in all defined intervals with insignificant p values except at interval 4, fibrinogen was significantly higher in the cases, p 0.006. Table 1ConclusionThere was no significant difference in the trend of D-dimer and fibrinogen levels during SARS-CoV2 infection between patients with ARDs and those without ARDs. Additional studies are needed to quantify the actual risk of VTE in patients with ARDs during SARS-CoV2 in correlation with serum markers of VTE.References[1]Eljilany I, Elzouki AN. D-Dimer, Fibrinogen, and IL-6 in COVID-19 Patients with Suspected Venous Thromboembolism: A Narrative Review. Vasc Health Risk Manag. 2020;16:455-62.[2]Li JY, Wang HF, Yin P, Li D, Wang DL, Peng P, et al. Clinical characteristics and risk factors for symptomatic venous thromboembolism in hospitalized COVID-19 patients: A multicenter retrospective study. J Thromb Haemost. 2021;19(4):1038-48.[3]Zhan H, Chen H, Liu C, Cheng L, Yan S, Li H, et al. Diagnostic Value of D-Dimer in COVID-19: A Meta-Analysis and Meta-Regression. Clin Appl Thromb Hemost. 2021;27:10760296211010976.[4]Lee JJ, Pope JE. A meta-analysis of the risk of venous thromboembolism in inflammatory rheumatic diseases. Arthritis Res Ther. 2014;16(5):435.Table 1.Differences in D-dimer and fibrinogen during SARS-CoV2 infection between patients with ARDs and those without at the defined intervals.Case N = 203Control N = 551P valueMedian (25th - 75th IQR), D-dimer (mg/L)(0 to < 3 days)0.56 (0.34 – 1.31)0.86 (0.54 – 1.41)0.096(≤ 3 to < 6 days)0.67 (0.35 – 2.58)1.11 (0.44 – 1.11)0.340(≤ 6 to < 9 days)0.81 (0.33 – 5.12)1.12 (0.56 – 3.28)0.299(≤ 9 to 12 days)0.94 (0.72 – 5.44)5.20 (1.0 – 15.05)0.058(≤ 12 to < 15 days)2.88 (0.72 – 5.53)4.96 (0.57 – 9.98)0.681(≤ 15 to 18 days)1.81 (0.89 – 2.55)5.56 (2.60 – 15.1)0.086Median (25th – 75th IQR), fibrinogen (mg/L)(0 to < 3 days)6.53 (2.0 - 6.53)5.65 (3.75 – 7.17)1.000(≤ 3 to < 6 days)6.25 (3.72 – 8.3)4.6 (4.1 – 5.6)0.385(≤ 6 to < 9 days)3.53 (3.29 – 4.62)3.4 (3.2 – 3.92)0.328(≤ 9 to 12 days)4.3 (2.82 – 4.78)2.2 (1.65 – 3.05)0.006(≤ 12 to < 15 days)4.4 (2.37 – 5.13)3.1 (1.7 – 4.45)0.170(≤ 15 to 18 days)3.6 ( – 5.7)3.7 (2.0 – 4.88)0.524Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Designing a Pseudo-Haptics Study for Virtual Anesthesia Skills Development

Pseudo-haptics refers to the simulation of haptic sensations without the use of haptic interfaces, using, for example, audiovisual feedback and kinesthetic cues. Given the COVID-19 pandemic and the shift to online learning, there has been a recent interest in pseudo-haptics as it can help facilitate psychomotor skills development away from simulation centers and laboratories. Here we present work-in-progress that describes the study design of a pseudo-haptics for virtual anesthesia skills development. We anticipate this work will provide greater insight to pseudo-haptics and its application to anesthesia-based training. © 2022 IEEE.

ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination

The emergence of the COVID-19 pandemic and the first global infodemic have changed our lives in many different ways. We relied on social media to get the latest information about COVID-19 pandemic and at the same time to disseminate information. The content in social media consisted not only health related advise, plans, and informative news from policymakers, but also contains conspiracies and rumors. It became important to identify such information as soon as they are posted to make an actionable decision (e.g., debunking rumors, or taking certain measures for traveling). To address this challenge, we developed and publicly released the first largest manually annotated Arabic tweet dataset, ArCovidVac, for the COVID-19 vaccination campaign, covering many countries in the Arab region. The dataset is enriched with different layers of annotation, including, (i) Informativeness (more vs. less important tweets);(ii) fine-grained tweet content types (e.g., advice, rumors, restriction, authenticate news/information);and (iii) stance towards vaccination (pro-vaccination, neutral, anti-vaccination). Further, we performed in-depth analysis of the data, exploring the popularity of different vaccines, trending hashtags, topics and presence of offensiveness in the tweets. We studied the data for individual types of tweets and temporal changes in stance towards vaccine. We benchmarked the ArCovidVac dataset using transformer models for informativeness, content types, and stance detection. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

Impact of Sustainable Development Goals on Economic Growth in Saudi Arabia: Role of Education and Training

Sustainable development goals (SDGs) are intended to be attained as a balanced whole. However, significant interactions (the synergies and trade-offs) between the SDGs have caused the need, especially in developing economies, to identify and pursue them in line with their particular developmental needs. The research intends to empirically investigate the relationship between selected UN SDGs and GDP growth rate as a proxy for economic well-being in Saudi Arabia. We also investigate the role of education and training in achieving SDGs in accordance with the Saudi Vision 2030, which places emphasis on the knowledge economy. This research employs multiple regression analysis to explore the relationship between the SDG variables and the GDP. The results show that education and training, gender equity/women's empowerment, greenhouse gas emissions, and decent employment are positively and significantly related to the GDP growth, whereas poverty, hunger, and health appear to be negatively related. The research indicates that education and training can promote economic, socioeconomic, and health goals without compromising environmental goals. Consequently, the Saudi government should invest more in education and training to maximize synergies and minimize tradeoffs between the SDGs. This will help to promote sustainable employment generation, build human capital, improve socioeconomic empowerment through technology, and boost economic growth.

Overview of the CLEF-2022 CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection

We describe the fifth edition of the CheckThat! lab, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting tasks related to factuality in multiple languages: Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Task 1 asks to identify relevant claims in tweets in terms of check-worthiness, verifiability, harmfullness, and attention-worthiness. Task 2 asks to detect previously fact-checked claims that could be relevant to fact-check a new claim. It targets both tweets and political debates/speeches. Task 3 asks to predict the veracity of the main claim in a news article. CheckThat! was the most popular lab at CLEF-2022 in terms of team registrations: 137 teams. More than one-third (37%) of them actually participated: 18, 7, and 26 teams submitted 210, 37, and 126 official runs for tasks 1, 2, and 3, respectively.

Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection

We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that tweet contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking;also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A total of ten teams submitted systems for task 1, and one team participated in task 2;nine teams also submitted a system description paper. Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used. Most submissions achieved sizable improvements over several baselines, and the best systems used pre-trained Transformers and ensembles. The data, the scorers and the leader-boards for the tasks are available at http://gitlab.com/NLP4IF/nlp4if-2021. © 2021 Association for Computational Linguistics.

Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations

Harmful or abusive online content has been increasing over time, raising concerns for social media platforms, government agencies, and policymakers. Such harmful or abusive content can have major negative impact on society, e.g., cyberbullying can lead to suicides, rumors about COVID-19 can cause vaccine hesitance, promotion of fake cures for COVID-19 can cause health harms and deaths. The content that is posted and shared online can be textual, visual, or a combination of both, e.g., in a meme. Here, we describe our experiments in detecting the roles of the entities (hero, villain, victim) in harmful memes, which is part of the CONSTRAINT-2022 shared task, as well as our system for the task. We further provide a comparative analysis of different experimental settings (i e , unimodal, multimodal, attention, and augmentation). For reproducibility, we make our experimental code publicly available.(1)

Overview of the CLEF-2022 CheckThat! Lab Task 2 on Detecting Previously Fact-Checked Claims

We describe the fourth edition of the CheckThat! Lab, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting three tasks related to factuality, and it covers seven languages such as Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Here, we present the task 2, which asks to detect previously fact-checked claims (in two languages). A total of six teams participated in this task, submitted a total of 37 runs, and most submissions managed to achieve sizable improvements over the baselines using transformer based models such as BERT, RoBERTa. In this paper, we describe the process of data collection and the task setup, including the evaluation measures, and we give a brief overview of the participating systems. Last but not least, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in detecting previously fact-checked claims. © 2022 Copyright for this paper by its authors.

Overview of the CLEF-2022 CheckThat! Lab Task 1 on Identifying Relevant Claims in Tweets

We present an overview of CheckThat! lab 2022 Task 1, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). Task 1 asked to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics in six languages: Arabic, Bulgarian, Dutch, English, Spanish, and Turkish. A total of 19 teams participated and most submissions managed to achieve sizable improvements over the baselines using Transformer-based models such as BERT and GPT-3. Across the four subtasks, approaches that targetted multiple languages (be it individually or in conjunction, in general obtained the best performance. We describe the dataset and the task setup, including the evaluation settings, and we give a brief overview of the participating systems. As usual in the CheckThat! lab, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research on finding relevant tweets that can help different stakeholders such as fact-checkers, journalists, and policymakers. © 2022 Copyright for this paper by its authors.

SARS-COV-2 VIRAL LOAD in PATIENTS with AUTOIMMUNE RHEUMATIC DISEASE, A RETROSPECTIVE COMPARATIVE STUDY

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load and its impact on disease outcome in patients with autoimmune rheumatic disease (ARD) are lacking. Also, whether patients with ARD receiving immunomodulators have different viral loads compared to the general population is unknown. Objectives: To compare the viral load of SARS-CoV-2 and its trending between patients without and with ARD. Methods: Retrospectively, patients with ARD infected with SARS-CoV-2 were matched by age and sex at a ratio of 1:2 to patients without ARD and not receiving immunosuppression or immunomodulator drugs. Viral load was determined by the cycle threshold (CT) value measured by a number of platforms: (a) Automated Platforms-the Roche Cobas 6800 system using the Cobas SARS-CoV-2 Test targeting the E and orf1a/b genes (Roche, Switzerland) and the Xpert Xpress SARS-CoV-2 targeting the E and N genes (Cepheid, USA);(b) Manual platforms-EZ1 (QIAGEN, USA), QIAsymphony (QIAGEN, USA), and Bioneer ExiPrepTM 96 Virus DNA/RNA kits Catalogue No K4614 (Bioneer, South Korea) extraction with thermal cycling using TaqPath™ PCR COVID-19 Combo Kit targeting the N, S and orf1a/b genes (Thermo Fisher Scientific, USA) on ABI 7500 thermal cyclers. Independent samples t-test was used to compare the mean CT values of the study groups at baseline and at 5 subsequent intervals (1-5.9, 6-11.9, 12-17.9, 18-23.9 and 24-30 days). Results: Mean age (SD) of 197 cases and 420 controls were 45.2 (11.8) and 44.1 (12.3) years, respectively. Females were predominant in both groups 60% vs. 52%, P=0.053. The most common ARD was rheumatoid arthritis in 82 cases (41.6%), followed by spondyloarthropathy in 33 (16.8%) and systemic lupus ery-thematosus in 31 (15.7%). Of the cases, 67% were on conventional synthetic disease modifying anti-rheumatic drugs (DMARDs), 15.2% on biological DMARDs and 4.6% patients were on rituximab. The mean CT values was signifcantly lower in the ARD group at baseline and persisted till day 24. Conclusion: Compared to patients without ARD, the viral load of SARS-CoV-2 in patients with ARD is signifcantly higher at baseline testing and persists till day 24. This fnding may indicate that patients with ARD are at higher risk of severe SARS-CoV-2 infection and prolonged potential transmission. Clinical outcome correlation is needed.

An Investigation into Crime Forecast Using Auto ARIMA and Stacked LSTM

The severity of criminal activities which cause both physical and psychological damage has been increasing at an alarming rate across the globe. Realizing the significance of this problem, law enforcement agencies have developed several strategies to prevent crimes. Being slow-paced and ineffective in most cases, these prevention strategies are not robust enough to contribute in predicting crime trends for an early prevention. In this paper, we propose a regression-based model that incorporates temporal, statistical relationships and other relevant information about the data to forecast crime trends. Since, seasonal information is a powerful inclusion in an application of time series pattern, we use two popular regression methods, including an extended Autoregressive Integrated Moving Average (Auto ARIMA) and stacked Long Short-Term Memory (LSTM) to analyze crime patterns, specifically during the Covid-19 pandemic lockdown, and generate forecasts. We experimented our methods on London Crime Dataset and obtained some interesting results which can not only be useful to take necessary precautions, but also analyze crime patterns during the period of pandemic lockdowns for generating useful guidelines regarding citizens' life styles and hence, contribute to reducing the crime rates accordingly. © 2022 IEEE.

The CLEF-2022 CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection

The fifth edition of the CheckThat! Lab is held as part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting various factuality tasks in seven languages: Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Task 1 focuses on disinformation related to the ongoing COVID-19 infodemic and politics, and asks to predict whether a tweet is worth fact-checking, contains a verifiable factual claim, is harmful to the society, or is of interest to policy makers and why. Task 2 asks to retrieve claims that have been previously fact-checked and that could be useful to verify the claim in a tweet. Task 3 is to predict the veracity of a news article. Tasks 1 and 3 are classification problems, while Task 2 is a ranking one.

Fact-checking, fake news, propaganda, media bias, and the covid-19 infodemic

Social media have democratized content creation and have made it easy for anybody to spread information online. However, stripping traditional media from their gate-keeping role has left the public unprotected against biased, deceptive and disinformative content, which could now travel online at breaking-news speed and influence major public events. For example, during the COVID-19 pandemic, a new blending of medical and political disinformation has given rise to the first global infodemic. We offer an overview of the emerging and inter-connected research areas of fact-checking, disinformation, "fake news'', propaganda, and media bias detection. We explore the general fact-checking pipeline and important elements thereof such as check-worthiness estimation, spotting previously fact-checked claims, stance detection, source reliability estimation, detection of persuasion techniques, and detecting malicious users in social media. We also cover large-scale pre-trained language models, and the challenges and opportunities they offer for generating and for defending against neural fake news. Finally, we discuss the ongoing COVID-19 infodemic. © 2022 ACM.

Impact of COVID-19 on Online Education in Developing Countries - An Overview

The COVID-19 lockdown since March 2020 necessitates higher education institutions to deliver education online. Although education institutions in high and higher middle-income countries could relatively easily transition face to face education to online delivery, most higher education institutions in low-income and lower middle-income countries were unable to do it. World-wide, more than half of the world's 1.5 billion students is out of online education activities especially in developing and emerging nations. Hence, the primary objective is to examine the difficulties and challenges experienced by some of those countries in their higher education institutions' transition to online education. The study focuses on internet infrastructure, accessibility, affordability, digital learning management system, academics and students' perspectives and digital knowledge gap related to online education. The study finds that poor or no internet infrastructures/connections, streaming devices, learning management system, inexperience in online education, and socioeconomic conditions are the main impedances for slow or no transition to online education in most emerging and developing countries. Some action plans (recommendations) to overcome these challenges are also compiled.

Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News

We describe the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting tasks related to factuality, and covers Arabic, Bulgarian, English, Spanish, and Turkish. Task 1 asks to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics (in all five languages). Task 2 asks to determine whether a claim in a tweet can be verified using a set of previously fact-checked claims (in Arabic and English). Task 3 asks to predict the veracity of a news article and its topical domain (in English). The evaluation is based on mean average precision or precision at rank k for the ranking tasks, and macro-F1 for the classification tasks. This was the most popular CLEF-2021 lab in terms of team registrations: 132 teams. Nearly one-third of them participated: 15, 5, and 25 teams submitted official runs for tasks 1, 2, and 3, respectively. © 2021, Springer Nature Switzerland AG.

A ripple effect of covid-19 pandemic on shortage of medicinal products and its impact on patient care

Objective: Shortages of medicinal products are complex global problems. Drug shortages remain a significant public health issue. Global shortages of medical products have a potential effect on patient health and total healthcare costs. Countries worldwide, especially those affected by Coronavirus disease 2019 (COVID-19), is experiencing a rapid increase in drug shortage, which causes several complications for physicians, health care provider, patients, health institutes and health regulatory bodies. Methods: To carry out the study of shortages, several efforts have been taken by the regulators and industries. Prominent amongst these include FDA's research the needs and the reforms made in the regulations about shortages. We also searched for electronic databases (PubMed, Science direct, Web of Science) using the terms (COVID-19 and shortage) or (medicine and COVID-19) for articles in periods of 2019 to 2021. Results: On assessment based on the report, the number of shortage drugs in 2020 is 835;Anesthesia drugs are highest during the COVID-19 outbreak data indicate the number of shortages is 143 in USA. It was found that generic products were mostly in short supply, with antimicrobial agents (63%) topping the list of therapeutic categories of medicines with interrupted supply, followed by oncology medicines (47%) and then anesthetic agents (38%) during COVID-19 pandemic. Conclusion: Many steps have been taken to reduce the impact of a shortage of health care. Agencies like the United States Food and Drug Administration (US FDA) and European Medicines Agency (EMA) has established guidelines and works with manufacturers and other partners to help prevent shortages. This article aims to the analysis the root cause of medicinal product shortages, their effects on the patient outcome, medication error, which occurs due to the substitution safe and effective therapies with alternative treatments, identify possible solutions and policies established to manage medicinal product shortages. © 2021 The Authors. Published by Innovare Academic Sciences Pvt Ltd.

Overview of the CLEF-2021 CheckThat! Lab Task 1 on check-worthiness estimation in tweets and political debates

We present an overview of Task 1 of the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The task asks to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics in five languages: Arabic, Bulgarian, English, Spanish, and Turkish. A total of 15 teams participated in this task and most submissions managed to achieve sizable improvements over the baselines using Transformer-based models such as BERT and RoBERTa. Here, we describe the process of data collection and the task setup, including the evaluation measures, and we give a brief overview of the participating systems. We release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in check-worthiness estimation for tweets and political debates. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Overview of the CLEF-2021 CheckThat! Lab Task 2 on detecting previously fact-checked claims in tweets and political debates

We describe the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting three tasks related to factuality, and it covers Arabic, Bulgarian, English, Spanish, and Turkish. Here, we present the task 2, which asks to detect previously fact-checked claims (in two languages). A total of four teams participated in this task, submitted a total of sixteen runs, and most submissions managed to achieve sizable improvements over the baselines using transformer based models such as BERT, RoBERTa. In this paper, we describe the process of data collection and the task setup, including the evaluation measures used, and we give a brief overview of the participating systems. Last but not least, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in detecting previously fact-checked claims. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

EXPLORATION OF THE NOVEL CORONA VIRUS TRANSITION GRAPHS WITH PETRINET MODELING

Corona virus (CoV) is a group of viruses with non-bifurcated, single-stranded, and positive-sense RNA genomes. Apart from infecting several economically significant vertebrates (such as pigs and chickens), it is reported in the recent literature that six main types of CoVs infect the human hosts and cause lung infections. In animals, CoVs cause several diseases, including pneumonia, gastrointestinal tract, and central nervous system diseases. In humans, the CoVs work as respiratory tract diseases, and the new CoVs can penetrate the barrier between other species and humans and can cause a high mortality rate. In the course of this study, a novel approach to networking, based on the density-dependent differential equations, is adopted for the precise explanation of the propagation of the virus and the effect of quarantine on it. An infectious disease model with a time delay is suggested based on the conventional infectious disease model. To describe the viral infection period and treatment time, the time differential is used. Using the epidemic data released in real-time, the minimum error is obtained firstly through the inversion of the numerical simulation parameter;then we simulate the development pattern of the epidemic according to the dynamics system;finally, the effectiveness of quarantine steps is compared and analyzed. With the help of a discrete model, the transformations are documented in detail that is difficult to evaluate numerically. The provided numerical results are in close agreement with the experimental findings. The modeling of Petri nets (PNs) used has proven to be a successful method. The current research strategy can help the public to gain awareness of the disease spread, which is highly desired.