scholarly journals DESIGN OF A NONLINEAR SITR FRACTAL MODEL BASED ON THE DYNAMICS OF A NOVEL CORONAVIRUS (COVID-19)

Fractals ◽  
2020 ◽  
Vol 28 (08) ◽  
pp. 2040026 ◽  
Author(s):  
YOLANDA GUERRERO SÁNCHEZ ◽  
ZULQURNAIN SABIR ◽  
JUAN L. G. GUIRAO

The aim of the present paper is to state a simplified nonlinear mathematical model to describe the dynamics of the novel coronavirus (COVID-19). The design of the mathematical model is described in terms of four categories susceptible ([Formula: see text], infected ([Formula: see text], treatment ([Formula: see text] and recovered ([Formula: see text], i.e. SITR model with fractals parameters. These days there are big controversy on if is needed to apply confinement measure to the population of the word or if the infection must develop a natural stabilization sharing with it our normal life (like USA or Brazil administrations claim). The aim of our study is to present different scenarios where we draw the evolution of the model in four different cases depending on the contact rate between people. We show that if no confinement rules are applied the stabilization of the infection arrives around 300 days affecting a huge number of population. On the contrary with a contact rate small, due to confinement and social distancing rules, the stabilization of the infection is reached earlier.

2021 ◽  
Author(s):  
Yi Li ◽  
Xianhong Yin ◽  
Meng Liang ◽  
Xiaoyu Liu ◽  
Meng Hao ◽  
...  

Abstract Objective: In December 2019, pneumonia infected with the novel coronavirus burst in Wuhan, China. We aimed to use a mathematical model to predict number of diagnosed patients in future to ease anxiety on the emergent situation. Methods: According to all diagnosis number from WHO website and combining with the transmission mode of infectious diseases, the mathematical model was fitted to predict future trend of outbreak. Our model was based on the epidemic situation in China, which could provide referential significance for disease prediction in other countries, and provide clues for prevention and intervention of relevant health authorities. In this retrospective, all diagnosis number from Jan 21 to Feb 10, 2020 reported from China was included and downloaded from WHO website. We develop a simple but accurate formula to predict the next day diagnosis number: ,where N i is the total diagnosed patient till the i th day, and was estimated as 0.904 at Feb 10. Results: Based on this model, it is predicted that the rate of disease infection will decrease exponentially. The total number of infected people is limited; thus, the disease will have limited impact. However, new diagnosis will last to end of March. Conclusions: Through the establishment of our model, we can better predict the trend of the epidemic in China.


2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Elham Hashemizadeh ◽  
Mohammad Ali Ebadi

Abstract Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. This paper provides a numerical solution for the mathematical model of the novel coronavirus by the application of alternative Legendre polynomials to find the transmissibility of COVID-19. The mathematical model of the present problem is a system of differential equations. The goal is to convert this system to an algebraic system by use of the useful property of alternative Legendre polynomials and collocation method that can be solved easily. We compare the results of this method with those of the Runge–Kutta method to show the efficiency of the proposed method.


Author(s):  
Yi Li ◽  
Xianhong Yin ◽  
Meng Liang ◽  
Xiaoyu Liu ◽  
Meng Hao ◽  
...  

AbstractImportanceTo predict the diagnosed COVID-19 patients and the trend of the epidemic in China. It may give the public some scientific information to ease the fear of the epidemic.ObjectiveIn December 2019, pneumonia infected with the novel coronavirus burst in Wuhan, China. We aimed to use a mathematical model to predict number of diagnosed patients in future to ease anxiety on the emergent situation.DesignAccording to all diagnosis number from WHO website and combining with the transmission mode of infectious diseases, the mathematical model was fitted to predict future trend of outbreak.SettingOur model was based on the epidemic situation in China, which could provide referential significance for disease prediction in other countries, and provide clues for prevention and intervention of relevant health authorities.ParticipantsIn this retrospective, all diagnosis number from Jan 21 to Feb 10, 2020 reported from China was included and downloaded from WHO website.Main Outcome(s) and Measure(s)We develop a simple but accurate formula to predict the next day diagnosis number:,where Ni is the total diagnosed patient till the ith day, and α was estimated as 0.904 at Feb 10.ResultsBased on this model, it is predicted that the rate of disease infection will decrease exponentially. The total number of infected people is limited; thus, the disease will have limited impact. However, new diagnosis will last to March.Conclusions and RelevanceThrough the establishment of our model, we can better predict the trend of the epidemic in China.


Biology ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 107 ◽  
Author(s):  
Wei Gao ◽  
P. Veeresha ◽  
D. G. Prakasha ◽  
Haci Mehmet Baskonus

In this study, we investigate the infection system of the novel coronavirus (2019-nCoV) with a nonlocal operator defined in the Caputo sense. With the help of the fractional natural decomposition method (FNDM), which is based on the Adomian decomposition and natural transform methods, numerical results were obtained to better understand the dynamical structures of the physical behavior of 2019-nCoV. Such behaviors observe the general properties of the mathematical model of 2019-nCoV. This mathematical model is composed of data reported from the city of Wuhan, China.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
G. B. Almeida ◽  
T. N. Vilches ◽  
C. P. Ferreira ◽  
C. M. C. B. Fortaleza

AbstractIn 2020, the world experienced its very first pandemic of the globalized era. A novel coronavirus, SARS-CoV-2, is the causative agent of severe pneumonia and has rapidly spread through many nations, crashing health systems and leading a large number of people to death. In Brazil, the emergence of local epidemics in major metropolitan areas has always been a concern. In a vast and heterogeneous country, with regional disparities and climate diversity, several factors can modulate the dynamics of COVID-19. What should be the scenario for inner Brazil, and what can we do to control infection transmission in each of these locations? Here, a mathematical model is proposed to simulate disease transmission among individuals in several scenarios, differing by abiotic factors, social-economic factors, and effectiveness of mitigation strategies. The disease control relies on keeping all individuals’ social distancing and detecting, followed by isolating, infected ones. The model reinforces social distancing as the most efficient method to control disease transmission. Moreover, it also shows that improving the detection and isolation of infected individuals can loosen this mitigation strategy. Finally, the effectiveness of control may be different across the country, and understanding it can help set up public health strategies.


2021 ◽  
pp. 0272989X2110030
Author(s):  
Serin Lee ◽  
Zelda B. Zabinsky ◽  
Judith N. Wasserheit ◽  
Stephen M. Kofsky ◽  
Shan Liu

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.


2020 ◽  
Vol 50 (6-7) ◽  
pp. 614-620 ◽  
Author(s):  
William Hatcher

President Trump’s communications during the novel coronavirus (COVID-19) pandemic violate principles of public health, such as practicing transparency and deferring to medical experts. Moreover, the president’s communications are dangerous and misleading, and his lack of leadership during the crisis limits the nation’s response to the problem, increases political polarization around public health issues of social distancing, and spreads incorrect information about health-related policies and medical procedures. To correct the dangerous path that the nation is on, the administration needs to adopt a more expert-centered approach to the crisis, and President Trump needs to practice compassion, empathy, and transparency in his communications.


Author(s):  
Iulia Clitan ◽  
◽  
Adela Puscasiu ◽  
Vlad Muresan ◽  
Mihaela Ligia Unguresan ◽  
...  

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.


PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260399
Author(s):  
Perla Werner ◽  
Aviad Tur-Sinai

Efforts to control the spread of the novel Coronavirus (COVID-19) pandemic include drastic measures such as isolation, social distancing, and lockdown. These restrictions are accompanied by serious adverse consequences such as forgoing of healthcare. The study aimed to assess the prevalence and correlates of forgone care for a variety of healthcare services during a two-month COVID-19 lockdown, using Andersen’s Behavioral Model of Healthcare Utilization. A cross-sectional study using computerized phone interviews was conducted with 302 Israeli Jewish participants aged 40 and above. Almost half of the participants (49%) reported a delay in seeking help for at least one needed healthcare service during the COVID-19 lockdown period. Among the predisposing factors, we found that participants aged 60+, being more religious, and reporting higher levels of COVID-19 fear were more likely to report forgone care than younger, less religious and less concerned participants. Among need factors, a statistically significant association was found with a reported diagnosis of diabetes, with participants with the disease having a considerably higher likelihood of forgone care. The findings stress the importance of developing interventions aimed at mitigating the phenomenon of forgoing care while creating nonconventional ways of consuming healthcare services. In the short term, healthcare services need to adapt to the social distancing and isolation measures required to stanch the epidemic. In the long term, policymakers should consider alternative ways of delivering healthcare services to the public regularly and during crisis without losing sight of their budgetary consequences. They must recognize the possibility of having to align medical staff to the changing demand for healthcare services under conditions of health uncertainty.


Author(s):  
Adeyemi Olukayode Binuyo

In this paper, the eigenvalue elasticity and sensitivity values of the mathematical model of transmission dynamics of corruption were obtained and presented using the eigenvalue elasticity and sensitivity analysis methods. The parameter with the greatest impact on the mathematical model was determined using the methods. This parameter will assist the government on how to reduce and provide measures to eradicate corrupt practices among the populace. From the mathematical model of corruption presented, it was obtained that the effective contact rate of corruption has the highest value when using the eigenvalue elasticity and sensitivity analysis.


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