scholarly journals An Evaluation of COVID-19 in Italy: A data-driven modeling analysis

2020 ◽  
Author(s):  
Yongmei Ding ◽  
Liyuan Gao
Keyword(s):  
Real Data ◽  
Data Driven ◽  
The Novel ◽  
State Variables ◽  
Infected People ◽  
Modeling Analysis ◽  
Travel Restrictions ◽  
Novel Coronavirus ◽  
Data Driven Modeling ◽  
Global Travel

Abstract The novel coronavirus (COVID-19) that has been spreading worldwide since December 2019 has sickened millions of people, shut down major cities and some countries, prompted unprecedented global travel restrictions. Real data-driven modeling is an effort to help evaluate and curb the spread of the novel virus. Lockdowns and the effectiveness of reduction in the contacts in Italy has been measured via our modified model, with the addition of auxiliary and state variables that represent contacts, contacts with infected, conversion rate, latent propagation. Results show the decrease in infected people due to stay-at-home orders and tracing quarantine intervention. The effect of quarantine and centralized medical treatment was also measured through numerical modeling analysis.

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.

The Challenges of Biomedical Waste Management During the Ongoing Coronavirus Disease-19 (COVID-19) Pandemic: The Current Scenario

2021 ◽  
Vol 13 ◽  
Author(s):  
Sabitha Vadakedath ◽  
Venkataramana Kandi ◽  
Tarun Kumar Suvvari ◽  
L V Simhachalam Kutikuppala ◽  
Vikram Godishala ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Complex Nature ◽  
The Novel ◽  
Waste Collection ◽  
Biomedical Waste ◽  
Infected People ◽  
Life Threatening ◽  
Current Scenario ◽  
Constant Observation ◽  
Novel Coronavirus

: The novel Coronavirus (SARS-CoV-2) that has emerged and spread throughout the world causing CoV disease-19 (COVID-19) has since its discovery affected not only humans and animals but also the environment. Because of the highly infectious nature of the virus, and the respiratory aerosol transmission route, face masks and personal protective equipment have become mandatory for public and healthcare workers, respectively. Also, the complex nature of the pathogenicity of the virus, wherein, it has been associated with mild, moderate, and severe life-threatening infections, has warranted increased laboratory testing and placing the infected people in isolation and under constant observation in quarantine centers or at dedicated hospitals. Some infected people, who are generally healthy, and do not show symptoms have been placed in home quarantines. At this juncture, there has been increased amount of biomedical waste (BMW), and infectious general waste along with plastic disposable recyclable and non-recyclable waste. The increased BMW along with the potentially hazardous plastic waste collection, segregation, transport, and disposal has assumed increased significance during the ongoing pandemic. Therefore, this review attempts to investigate the current scenario of BMW management and strategies to minimize BMW and prevent potential environmental pollution.

scholarly journals How to Better Manage Tourism on Pandemic-Times? Moving Forward on a Discussion We Should Have Had Before the 2020 Crisis

2020 ◽  
Author(s):  
Laura Sinay ◽  
Maria Cristina Fogliatti de Sinay
Keyword(s):  
Literature Review ◽  
Research Funding ◽  
Tourism Industry ◽  
The Novel ◽  
Scholarly Community ◽  
Pandemic Virus ◽  
Funding Agencies ◽  
Travel Restrictions ◽  
Novel Coronavirus

Taking advantage of tourists’ intensive flow, the SARS-CoV-2 virus rapidly spread causing thousands of deaths globally. Trying to contain the already pandemic virus, government travel restrictions were suddenly imposed. Consequently, the tourism industry, which at that moment employed one in ten workers globally, suddenly collapsed. Hundreds of thousands of workers immediately lost their income. Flights were cancelled, and thousands of tourists were stuck abroad with no means to return to their home countries. The gravity of the situation raised the question of whether there was scholarly knowledge that could have helped manage tourism during the current pandemic. To answer this question, a methodical literature review was performed, allowing for up to 900 publications to be analysed. Keywords used were pandemic, tourism, tourist and travel. Based on this process, 63 publications were selected for further analysis. Among these, less than 5% were focused on the tourism side of the problem. As such, this research concludes that, by the time the novel coronavirus emerged, there was, virtually, no scholarly knowledge on how to manage tourism during pandemic times so as to avoid chaos, and that the scholarly community studying related issues is very small. Moving forward, this article recommends that research funding agencies and universities encourage the sound development of this area of knowledge. Aspects that should be investigated include when, how and by whom should tourism be halted, as well as the feasibility of a Tourism World Fund for supporting related costs.

scholarly journals Formulation and Solution of an Inverse Reliability Problem to Simulate the Dynamic Behavior of COVID-19 Pandemic

2021 ◽  
Vol 22 (1) ◽  
pp. 91-107
Author(s):  
F. S. Lobato ◽  
G. M. Platt ◽  
G. B. Libotte ◽  
A. J. Silva Neto
Keyword(s):  
Dynamic Behavior ◽  
Differential Evolution Algorithm ◽  
Iteration Process ◽  
Real Data ◽  
The Novel ◽  
Design Variables ◽  
Different Types ◽  
Evolution Algorithm ◽  
Novel Coronavirus ◽  
Optimization Loop

Different types of mathematical models have been used to predict the dynamic behavior of the novel coronavirus (COVID-19). Many of them involve the formulation and solution of inverse problems. This kind of problem is generally carried out by considering the model, the vector of design variables, and system parameters as deterministic values. In this contribution, a methodology based on a double loop iteration process and devoted to evaluate the influence of uncertainties on inverse problem is evaluated. The inner optimization loop is used to find the solution associated with the highest probability value, and the outer loop is the regular optimization loop used to determine the vector of design variables. For this task, we use an inverse reliability approach and Differential Evolution algorithm. For illustration purposes, the proposed methodology is applied to estimate the parameters of SIRD (Susceptible-Infectious-Recovery-Dead) model associated with dynamic behavior of COVID-19 pandemic considering real data from China's epidemic and uncertainties in the basic reproduction number (R0). The obtained results demonstrate, as expected, that the increase of reliability implies the increase of the objective function value.

scholarly journals SARS2020: an integrated platform for identification of novel coronavirus by a consensus sequence-function model

2020 ◽  
Author(s):  
Dachuan Zhang ◽  
Tong Zhang ◽  
Sheng Liu ◽  
Dandan Sun ◽  
Shaozhen Ding ◽  
...  
Keyword(s):  
Biological Function ◽  
Consensus Sequence ◽  
Rapid Identification ◽  
Data Driven ◽  
Supplementary Information ◽  
Respiratory Syndrome Virus ◽  
The Novel ◽  
Function Model ◽  
Catalytic Function ◽  
Novel Coronavirus

Abstract Motivation The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been conducted to identify viruses by the enzymes that they encode, and which are key to pathogen propagation. Results We built a comprehensive scientific resource, SARS2020, which integrates coronavirus-related research, genomic sequences and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the severe acute respiratory syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics. Availabilityand implementation SARS2020 is available at http://design.rxnfinder.org/sars2020/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Fran Sérgio Lobato ◽  
Gustavo Barbosa Libotte ◽  
Gustavo Mendes Platt
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Real Data ◽  
The Novel ◽  
Estimation Process ◽  
Design Variables ◽  
Novel Coronavirus ◽  
Stochastic Fractal Search Algorithm ◽  
Stochastic Fractal Search

Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.

scholarly journals Modeling the Novel Coronavirus (SARS-CoV-2) Outbreak in Sicily, Italy

2020 ◽  
Vol 17 (14) ◽  
pp. 4964 ◽  
Author(s):  
Andrea Maugeri ◽  
Martina Barchitta ◽  
Sebastiano Battiato ◽  
Antonella Agodi
Keyword(s):  
Transmission Rate ◽  
Control Measures ◽  
Transmission Dynamics ◽  
The Novel ◽  
Epidemic Spread ◽  
Transmission Rates ◽  
Contact Precautions ◽  
Travel Restrictions ◽  
Novel Coronavirus ◽  
Reported Data

Italy was the first country in Europe which imposed control measures of travel restrictions, quarantine and contact precautions to tackle the epidemic spread of the novel coronavirus (SARS-CoV-2) in all its regions. While such efforts are still ongoing, uncertainties regarding SARS-CoV-2 transmissibility and ascertainment of cases make it difficult to evaluate the effectiveness of restrictions. Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to assess SARS-CoV-2 transmission dynamics, working on the number of reported patients in intensive care unit (ICU) and deaths in Sicily (Italy), from 24 February to 13 April. Overall, we obtained a good fit between estimated and reported data, with a fraction of unreported SARS-CoV-2 cases (18.4%; 95%CI = 0–34.0%) before 10 March lockdown. Interestingly, we estimated that transmission rate in the community was reduced by 32% (95%CI = 23–42%) after the first set of restrictions, and by 80% (95%CI = 70–89%) after those adopted on 23 March. Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Moreover, our findings suggested that transmission rates were reduced after the adoption of control measures. However, we cannot evaluate whether part of this reduction might be attributable to other unmeasured factors, and hence further research and more accurate data are needed to understand the extent to which restrictions contributed to the epidemic control.

scholarly journals A Real Data-Driven Analytical Model for Testing for the Novel Coronavirus Disease, COVID-19

2021 ◽  
Vol 5 (8) ◽  
Author(s):  
Chris P. Tsokos ◽  
Lohuwa Mamudu
Keyword(s):  
Risk Factors ◽  
Analytical Model ◽  
Nasal Congestion ◽  
Real Data ◽  
The United States ◽  
Data Driven ◽  
World Health ◽  
Medical Evaluation ◽  
Control And Prevention ◽  
Novel Coronavirus

To address the testing of the horrific pandemic disease that has terrified our global society, COVID-19, we have developed an analytical model that an individual can easily apply to determine if he or she tested positive or negative with a very high degree of accuracy. Our analytical model is real data-driven utilizing data obtained from the World Health Organization, WHO, and the United States Center for Disease Control and Prevention, CDC guidelines. Both WHO and CDC have identified several symptoms or risk factors from individuals diagnosed with the disease, COVID-19. They have identified and published nine symptoms that are associated with the disease, COVID-19. However, our structured analytical model identified only seven of the nine symptoms to statistically significantly contribute to the subject disease. They are fever, tiredness, dry cough, difficulty in breathing, sore throat, pain, and nasal congestion. Each of the symptoms shows highly likelihood of having COVID-19. Our analytical model was carefully developed, very well-validated, and statistically tested to achieve a 93% accuracy in the testing result. If a person is tested positive, we recommend that he/she seek medical evaluation and treatment. That is, once we receive the categorical data from a given individual, and we input into the proposed model, the output result will be the individual is tested positive or negative for COVID-19. The developed model identifies (estimated) the different weights of each of the seven symptoms or risk factors that play a major role in the decision process of the testing results. Our findings seek to enhance testing efficiency, treatment, control, and prevention strategy for the COVID-19 disease.

scholarly journals Hybrid Method for Simulation of a Fractional COVID-19 Model with Real Case Application

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 290
Author(s):  
Anwarud Din ◽  
Amir Khan ◽  
Anwar Zeb ◽  
Moulay Rchid Sidi Ammi ◽  
Mouhcine Tilioua ◽  
...  
Keyword(s):  
Hybrid Method ◽  
Reproduction Number ◽  
Real Data ◽  
The Novel ◽  
Order Analysis ◽  
The Laplace Transform ◽  
Case Application ◽  
Novel Coronavirus ◽  
Available Information ◽  
Fractional Orders

In this research, we provide a mathematical analysis for the novel coronavirus responsible for COVID-19, which continues to be a big source of threat for humanity. Our fractional-order analysis is carried out using a non-singular kernel type operator known as the Atangana-Baleanu-Caputo (ABC) derivative. We parametrize the model adopting available information of the disease from Pakistan in the period 9 April to 2 June 2020. We obtain the required solution with the help of a hybrid method, which is a combination of the decomposition method and the Laplace transform. Furthermore, a sensitivity analysis is carried out to evaluate the parameters that are more sensitive to the basic reproduction number of the model. Our results are compared with the real data of Pakistan and numerical plots are presented at various fractional orders.

scholarly journals Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis

2020 ◽  
Vol 14 (03) ◽  
pp. 246-253 ◽  
Author(s):  
Rui Huang ◽  
Miao Liu ◽  
Yongmei Ding
Keyword(s):  
Logistic Model ◽  
Temporal Distribution ◽  
Hubei Province ◽  
Central China ◽  
Data Driven ◽  
Statistical Tool ◽  
Spatial Panel ◽  
Modeling Analysis ◽  
The Difference ◽  
Data Driven Modeling

Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spread from Hubei Province in Central China to neighbouring areas. Logistic model was employed according to the trend of available data, which shows the difference between Hubei Province and outside of it. We also calculated the reproduction number R0 for the range of [2.23, 2.51] via SEIR model. The measures to reduce or prevent the virus spread should be implemented, and we expect our data-driven modeling analysis providing some insights to identify and prepare for the future virus control.

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