scholarly journals Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)

2021 ◽  
pp. 70-74
Author(s):  
Iulia Clitan ◽  
◽  
Adela Puscasiu ◽  
Vlad Muresan ◽  
Mihaela Ligia Unguresan ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Web Application ◽  
Point Of View ◽  
The Novel ◽  
End User ◽  
First Case ◽  
Novel Coronavirus ◽  
Second Wave ◽  
Over Time

Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.

scholarly journals Fractional-order mathematical model for analysing impact of quarantine on transmission of COVID-19 in India

2021 ◽  
Vol 8 (2) ◽  
pp. 253-266
Author(s):  
D. D. Pawar ◽  
◽  
W. D. Patil ◽  
D. K. Raut ◽  
◽  
...  
Keyword(s):  
Mathematical Model ◽  
Fractional Order ◽  
Local Population ◽  
Euler Method ◽  
Transmission Dynamics ◽  
The Novel ◽  
First Case ◽  
Physical Interactions ◽  
The Government ◽  
Novel Coronavirus

An outbreak of the novel coronavirus disease was first reported in Wuhan, China in December 2019. In India, the first case was reported on January 30, 2020 on a person with a travel history to an affected country. Considering the fact of a heavily populated and diversified country like India, we have proposed a novel fractional-order mathematical model to elicit the transmission dynamics of the coronavirus disease (COVID-19) and the control strategy for India. The classical SEIR model is employed in three compartments, namely: quarantined immigrated population, non-quarantined asymptomatic immigrated population, and local population subjected to lockdown in the containment areas by the government of India to prevent the spread of disease in India. We have also taken into account the physical interactions between them to evaluate the coronavirus transmission dynamics. The basic reproduction number ($R_{0}$) has been derived to determine the communicability of the disease. Numerical simulation is done by using the generalised Euler method. To check the feasibility of our analysis, we have investigated some numerical simulations for various fractional orders by varying values of the parameters with help of MATLAB to fit the realistic pandemic scenario.

scholarly journals Modeling and Forecasting of COVID-19 Spreading by Delayed Stochastic Differential Equations

Axioms ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 18
Author(s):  
Marouane Mahrouf ◽  
Adnane Boukhouima ◽  
Houssine Zine ◽  
El Mehdi Lotfi ◽  
Delfim F. M. Torres ◽  
...  
Keyword(s):  
Real Data ◽  
Economic Damage ◽  
Population Parameter ◽  
The Novel ◽  
First Case ◽  
Epidemiological Trend ◽  
Modeling And Forecasting ◽  
Novel Coronavirus ◽  
Parameter Values ◽  
Stochastic Time

The novel coronavirus disease (COVID-19) pneumonia has posed a great threat to the world recent months by causing many deaths and enormous economic damage worldwide. The first case of COVID-19 in Morocco was reported on 2 March 2020, and the number of reported cases has increased day by day. In this work, we extend the well-known SIR compartmental model to deterministic and stochastic time-delayed models in order to predict the epidemiological trend of COVID-19 in Morocco and to assess the potential role of multiple preventive measures and strategies imposed by Moroccan authorities. The main features of the work include the well-posedness of the models and conditions under which the COVID-19 may become extinct or persist in the population. Parameter values have been estimated from real data and numerical simulations are presented for forecasting the COVID-19 spreading as well as verification of theoretical results.

scholarly journals Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

2021 ◽  
Vol 11 (9) ◽  
pp. 4266
Author(s):  
Md. Shahriare Satu ◽  
Koushik Chandra Howlader ◽  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
Sheikh Mohammad Shariful Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Models ◽  
Short Term ◽  
Infected People ◽  
Infected Case ◽  
First Case ◽  
Novel Coronavirus ◽  
Short Term Prediction ◽  
Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

scholarly journals Epstein-Barr Virus Versus Novel Coronavirus-Induced Hemophagocytic Lymphohistocytosis: The Uncharted Waters

2020 ◽  
Vol 8 ◽  
pp. 232470962095010 ◽  
Author(s):  
Rawan Amir ◽  
Asim Kichloo ◽  
Jagmeet Singh ◽  
Ravinder Bhanot ◽  
Michael Aljadah ◽  
...  
Keyword(s):  
Bacterial Infections ◽  
Epstein Barr Virus ◽  
Genetic Mutations ◽  
High Grade ◽  
The Novel ◽  
Barr Virus ◽  
First Case ◽  
Epstein Barr ◽  
Virus Versus ◽  
Novel Coronavirus

Hemophagocytic lymphohistocytosis (HLH) is a hyperinflammatory syndrome characterized by fever, hepatosplenomegaly, and pancytopenia. It may be associated with genetic mutations or viral/bacterial infections, most commonly Epstein-Barr virus (EBV) and cytomegalovirus. As for the novel coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), also known as COVID-19 (coronavirus disease-2019), the cytokine storm it triggers can theoretically lead to syndromes similar to HLH. In this article, we report a case of a 28-year-old female who presented with high-grade fevers, found to have both SARS-CoV-2 and EBV infections, and eventually began to show signs of early HLH. To our knowledge, this is the first case reported in literature that raises the possibility of SARS-CoV-2–related HLH development.

scholarly journals Exploring the role and mechanism of COVID-19 apps in fighting the current pandemic

2020 ◽  
Vol 0 ◽  
pp. 1-6
Author(s):  
Karthikeyan P. Iyengar ◽  
Rachit Jain ◽  
David Ananth Samy ◽  
Vijay Kumar Jain ◽  
Raju Vaishya ◽  
...  
Keyword(s):  
Data Privacy ◽  
Viral Transmission ◽  
Contact Tracing ◽  
The Novel ◽  
Test Track ◽  
The World ◽  
Novel Coronavirus ◽  
And Control ◽  
Second Wave ◽  
Transmission Test

As COVID-19 pandemic spread worldwide, policies have been developed to contain the disease and prevent viral transmission. One of the key strategies has been the principle of “‘test, track, and trace” to minimize spread of the virus. Numerous COVID-19 contact tracing applications have been rolled around the world to monitor and control the spread of the disease. We explore the characteristics of various COVID-19 applications and especially the Aarogya Setu COVID-19 app from India in its role in fighting the current pandemic. We assessed the current literature available to us using conventional search engines, including but not limited to PubMed, Google Scholar, and Research Gate in May 2020 till the time of submission of this article. The search criteria used MeSH keywords such as “COVID-19,” “pandemics,” “contact tracing,” and “mobile applications.” A variable uptake of different COVID-19 applications has been noted with increasing enrolment around the world. Security concerns about data privacy remain. The various COVID-19 applications will complement manual contact tracing system to assess and prevent viral transmission. Test, track, trace, and support policy will play a key role in avoidance of a “second wave” of the novel coronavirus severe acute respiratory syndrome coronavirus 2 outbreak.

scholarly journals COVID-19 ResNet: Residual neural network for COVID-19 classification with bayesian data augmentation

2021 ◽  
Vol 13 (2) ◽  
pp. 19
Author(s):  
Maria Baldeon calisto ◽  
Javier Sebastián Balseca Zurita ◽  
Martin Alejandro Cruz Patiño
Keyword(s):  
Neural Network ◽  
Medical Image ◽  
Data Augmentation ◽  
Optimization Approach ◽  
Hyperparameter Optimization ◽  
First Case ◽  
Augmentation Strategy ◽  
Medical Image Classification ◽  
Novel Coronavirus ◽  
Insight Into

COVID-19 is an infectious disease caused by a novel coronavirus called SARS-CoV-2. The first case appeared in December 2019, and until now it still represents a significant challenge to many countries in the world. Accurately detecting positive COVID-19 patients is a crucial step to reduce the spread of the disease, which is characterize by a strong transmission capacity. In this work we implement a Residual Convolutional Neural Network (ResNet) for an automated COVID-19 diagnosis. The implemented ResNet can classify a patient´s Chest-Xray image into COVID-19 positive, pneumonia caused from another virus or bacteria, and healthy. Moreover, to increase the accuracy of the model and overcome the data scarcity of COVID-19 images, a personalized data augmentation strategy using a three-step Bayesian hyperparameter optimization approach is applied to enrich the dataset during the training process. The proposed COVID-19 ResNet achieves a 94% accuracy, 95% recall, and 95% F1-score in test set. Furthermore, we also provide an insight into which data augmentation operations are successful in increasing a CNNs performance when doing medical image classification with COVID-19 CXR.

scholarly journals Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling

2021 ◽  
Author(s):  
Miguel López ◽  
Alberto Peinado ◽  
Andrés Ortiz
Keyword(s):  
Growth Model ◽  
Logistic Growth ◽  
Data Series ◽  
The Novel ◽  
Short Term ◽  
Growth Trend ◽  
Global Pandemic ◽  
First Case ◽  
Logistic Growth Model ◽  
Novel Coronavirus

AbstractSince the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that a global pandemic is possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and even construct short-term forecast in the near time horizon.

scholarly journals Controlling the cascade of corona

2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Muneeba Azmat
Keyword(s):  
Public Health ◽  
The Novel ◽  
Limited Data ◽  
Press Releases ◽  
The Public ◽  
Health Measures ◽  
First Case ◽  
Pandemic Response ◽  
Potential Impact ◽  
Novel Coronavirus

The pandemic of the 2019 novel Coronavirus has seen unprecedented exponential growth. Within three months, 192 countries have been affected, crossing more than 1 million confirmed cases and over 60 thousand deaths until the first week of April. Decision making in such a pandemic becomes difficult due to limited data on the nature of the disease and its propagation, course, prevention, and treatment. The pandemic response has varied from country to country and has resulted in a heterogeneous timeline for novel Coronavirus propagation. We compared the public health measures taken by various countries and the potential impact on the spread. We studied 6 countries including China, Italy, South Korea, Singapore, United Kingdom(UK), United States(US), and the special administrative region of Hong Kong. All articles, press releases, and websites of government entities published over a five-month period were included. A comparison of the date of the first diagnosed case, the spread of disease, and time since the first case and major public health policy implemented for prevention and containment and current cases was done. An emphasis on early and aggressive border restriction and surveillance of travelers from infected areas, use of information technology, and social distancing is necessary for control of the novel pandemic. Moving forwards, improvement in infrastructure, and adequate preparedness for pandemics is required.

scholarly journals Conservation analysis and screening of Ayurvedic compounds against SARS-CoV-2 Spike protein

2020 ◽  
Author(s):  
Zarrin Basharat ◽  
Muhammad Jahanzaib ◽  
Noor Rahman ◽  
Ishtiaq Ahmad Khan ◽  
Azra Yasmin
Keyword(s):  
Caspase 3 ◽  
Spike Protein ◽  
The Novel ◽  
Post Translational Modification ◽  
S Protein ◽  
Conservation Analysis ◽  
The Past ◽  
Online Tools ◽  
Novel Coronavirus ◽  
Over Time

Abstract Recent infections caused by the novel coronavirus (SARS-CoV-2) have led to global panic and mortality. Here, we analyzed the spike (S) protein of this virus using bioinformatics tools. We aimed to determine relative changes among different coronavirus species over the past two decades and to understand the conservation of the S-protein. Representative sequences of coronaviruses were collected from humans and other animals between 2000 and 2020. Evolutionary analyses found that the S-protein did not evolve overnight, but rather continuously over time. Post-translational modification (PTM) analysis using online tools and virtual screening of S-protein against a phytochemical database of Ayurvedic medicinal compounds (n = 2103) identified the S-protein inhibitors. Among these, top ranked were Gingerol (IUPAC name: 4'-Me ether, 3,5-di-Ac 3,5-di-Gingerdiols), 1-(5-Butyltetrahydro-2-furanyl)-2-hexacosanone and Ginsenoyne N ginseng that stimulates Caspase-3, Caspase-8, and the immune system. Gingerol is found in the fresh ginger and has reputation of being a potent antiviral. These compounds might prove useful to design drugs against COVID-19.

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