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The whole world is suffering from a pandemic virus known as COVID-19 nowadays. This virus is also known as novel corona virus. There are various statements coming out regarding its generation but till now one thing is coming is that its origin place seems to be a lab somewhere in Wuhan city of China. It is also said off and on the record that covid-19 virus came into existence near November 2019 but on record, the WHO office (China) reported on 31st December 2019 that an unknown virus is detected in Wuhan city. On 7th January 2020, Chinese authorities announced about the existence of this virus. At the initial stage, it was supposed that this virus might be bat-origin but after that several other statements are coming day by day. Authors are not going to discuss about the causes of its origin. But since this virus has become the pandemic around the world so it is necessary to study this virus and its nature to predict the future outbreaks and also control strategies by using mathematical modeling. In this paper, we are going to study the model by using Atangana-Baleanu fractional operator. Also we calculated the numeric results with graphical representation. [ABSTRACT FROM AUTHOR]

A new mathematical model of Coronavirus (2019-nCov) using piecewise hybrid fractional order derivatives is given in this paper. Moreover, in order to be consistent with the physical model problem, a new parameter μ is presented. The boundedness, existence, and positivity of the solutions for the proposed model are discussed. Two improved numerical methods are presented in this paper. The Caputo proportional constant nonstandard modified Euler–Maruyama method is introduced to study the fractional stochastic model, and the Grünwald–Letnikov nonstandard finite difference method is presented to study the hybrid fractional order deterministic model. Comparative studies with real data from Spain and Wuhan are presented. [ABSTRACT FROM AUTHOR]

In December 2019, human cases of novel coronavirus infection were detected in Wuhan, China which have been named as COVID-19 by the World Health Organization (WHO). Since COVID-19 was first detected in China, the virus has reached more than 120 countries and was declared a global pandemic on March 11, 2020 by the WHO. In this paper, we have highlighted the influence of temperature on the spread of COVID-19. For this, the dynamic transmission of COVID-19 is modeled taking into account the influence of the temperature on the persistence of coronavirus in the environment. We also took into account the impact of proportion of people who respect the barrier measures published by the WHO on the scale of the COVID-19 pandemic. Taking into account the influence of the temperature on the persistence of the virus in the environment, the dynamic transmission has been described by a system of ordinary differential equations (ODEs). First, we analyzed the solutions of system in the case where the impact of the temperature on the virus is neglected. Second, we carried out the mathematical analysis of the solutions of the system in the non-autonomous case. Mathematical analyzes have enabled us to establish that the temperature and proportion of persons who respect the barrier rules can affect the spread of COVID-19. Some numerical simulations have been proposed to illustrate the behavior of the pandemic in some countries. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath. And, the disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. in Radiology 295(1):202–207, 2020). It is an international real-life problem. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people. In this article, we aim to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using three mathematical models. This article deals with a complex real-life problem people face all over the world, an international real-life problem. The main focus is on the USA due to large infection and death due to coronavirus and thereby the life of every individual is uncertain. The death counts of the USA from February 29 to April 22, 2020, are used in this article as a data set. The death counts of the USA are fitted by the solutions of three mathematical models and a solution to an international problem is achieved. Based on the death rate, the lifetime of the coronavirus COVID-19 is predicted as 1464.76 days from February 29, 2020. That is, after March 2024 there will be no death in the USA due to COVID-19 if everyone follows the guidelines of WHO and the advice of healthcare workers. People and government can get prepared for this situation and many lives can be saved. It is the contribution of soft computing. Finally, this article suggests several steps to control the spread and severity of the disease. The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature. [ABSTRACT FROM AUTHOR]

Every day, a large number of people will use a hospital, creating a main air quality problem which may mean the risk of airborne infectious disease contamination in outpatient rooms, and affects human health. TB, COVID-19, MERS, and SARS are a hazardous communicable disease which are spread from person to person through the air or the aerosol in different ways, such as through coughing, spitting, sneezing, speaking, or through wounds. US scientists in the laboratory have shown that the virus can live in an aerosol and remain infectious for at least 3 hours. A new human coronavirus now known as the serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (formerly known as HCoV-19) emerged in late 2019 in Wuhan, China, and is now triggering a pandemic. COVID-19, TB, MERS and SARS - threats and opportunities progress against deadly infection make more people sick in the hospital. Therefore, we should be aware of the care and control of these diseases. Consequently, good air quality management is required to control and reduce possible infected air, such as carbon dioxide (CO2) concentration. In this research, a mathematical model for the risk analysis of airborne infectious disease in an outpatient room is proposed. Not only considering one type of person but also in this research, people are considered according to personal classifications. There are 4 types - patient, relative, worker, and outsider, staying in an outpatient room, which is in accordance with the real world. Air quality control manipulations are simulated using the inlet and outlet ventilation rates adjustment under the condition of a number of surrounding people with a personal classified factor. The fourth-order Runge-Kutta (RK4) is used to approximate the model solution. The proposed numerical model can be used to describe the dynamical dispersion of airborne infectious disease in an outpatient room. The results of the model are satisfactory, and it will be able to control airborne disease in more complicated structures. [ABSTRACT FROM AUTHOR]

COVID-19 has been declared as pandemic by WHO. This disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). The spread of this virus from Wuhan, China to the rest of the world has led to increasing concerns as it is considered a threat to mankind. The present work in situation analysis using neutrosophy identifies factors which should be taken care of so that the spread of this virus can be contained. Known factors as put forward by experts together with indeterminate factors which are still not taken into consideration are used to model the situation. This work using neutrosophic cognitive maps proves mathematically how these indeterminate and unknown factors are responsible for the spread of COVID-19 in India. The Neutrosophic Cognitive Map (NCM) is an enhanced version of Fuzzy Cognitive Map (FCM) since it takes into account the concept of indeterminacy and uncertainty, which is not addressed by FCMs. The obtained results not only show the importance of determinate factors (measures taken by the government bodies) in containing the spread of this deadly virus but also show how indeterminate and uncertain factors such as poverty, negligence, ineffectual healthcare, age, immunity, unscientific practices on religious grounds and illiteracy play a vital role in this regard. This model, in general, helps policymakers and government agencies to represent any complicated situation mathematically thereby helping them in responding appropriately and forming new policies for dealing with such pandemics. This work also signifies how neutrosophic theories can be applied in the analysis of the situation for dealing with real life problems. [ABSTRACT FROM AUTHOR]

Mathematical models for transmission dynamics of the novel COVID-2019 coronavirus, an outbreak of which began in December, 2019, in Wuhan are considered. To control the epidemiological situation, it is necessary to develop corresponding mathematical models. Mathematical models of COVID-2019 spread described by systems of nonlinear ordinary differential equations (ODEs) are overviewed. Some of the coefficients and initial data for the ODE systems are unknown or their averaged values are specified. The problem of identifying model parameters is reduced to the minimization of a quadratic objective functional. Since the ODEs are nonlinear, the solution of the inverse epidemiology problems can be nonunique, so approaches for analyzing the identifiability of inverse problems are described. These approaches make it possible to establish which of the unknown parameters (or their combinations) can be uniquely and stably recovered from available additional information. For the minimization problem, methods are presented based on a combination of global techniques (covering methods, nature-like algorithms, multilevel gradient methods) and local techniques (gradient methods and the Nelder–Mead method). [ABSTRACT FROM AUTHOR]

Background: Since the first cluster of cases was identified in Wuhan City, China, in December 2019, coronavirus disease 2019 (COVID-19) rapidly spreads globally. Scientists have made strides in estimating key transmission and epidemiological parameters. In particular, accumulating evidence points to a substantial fraction of asymptomatic or subclinical infections, which influences our understanding of the transmission potential and severity of this emerging disease. In this study, we derive estimates of the transmissibility and virulence of COVID-19 in Wuhan City, China, by reconstructing the underlying transmission dynamics using multiple data sources.Methods: We employ statistical methods and publicly available epidemiological datasets to jointly derive estimates of transmissibility and severity associated with the novel coronavirus. For this purpose, the daily series of laboratory-confirmed COVID-19 cases and deaths in Wuhan City together with epidemiological data of Japanese repatriated from Wuhan City on board government-chartered flights were integrated into our analysis.Results: Our posterior estimates of basic reproduction number (R) in Wuhan City, China, in 2019-2020 reached values at 3.49 (95% CrI 3.39-3.62) with a mean serial interval of 6.0 days, and the enhanced public health intervention after January 23 in 2020 was associated with a significantly reduced R at 0.84 (95% CrI 0.81-0.88), with the total number of infections (i.e., cumulative infections) estimated at 1,906,634 (95% CrI 1,373,500-2,651,124) in Wuhan City, elevating the overall proportion of infected individuals to 19.1% (95% CrI 13.5-26.6%). We also estimated the most recent crude infection fatality ratio (IFR) and time-delay adjusted IFR at 0.04% (95% CrI 0.03-0.06%) and 0.12% (95% CrI 0.08-0.17%), respectively, estimates that are substantially smaller than the crude CFR estimated at 4.06%.Conclusions: We have estimated key epidemiological parameters of the transmissibility and virulence of COVID-19 in Wuhan, China, during January-February 2020 using an ecological modeling approach that is suitable to infer epidemiological parameters with quantified uncertainty from partial observations collected by surveillance systems. Our estimate of time-delay adjusted IFR falls in the range of the median IFR estimates based on multiple serological studies conducted in several areas of the world. [ABSTRACT FROM AUTHOR]

In December 2019, the first case of infection with a new virus COVID-19 (SARS-CoV-2), named coronavirus, was reported in the city of Wuhan, China. At that time, almost nobody paid any attention to it. The new pathogen, however, fast proved to be extremely infectious and dangerous, resulting in about 3–5% mortality. Over the few months that followed, coronavirus has spread over entire world. At the end of March, the total number of infections is fast approaching the psychological threshold of one million, resulting so far in tens of thousands of deaths. Due to the high number of lives already lost and the virus high potential for further spread, and due to its huge overall impact on the economies and societies, it is widely admitted that coronavirus poses the biggest challenge to the humanity after the second World war. The COVID-19 epidemic is provoking numerous questions at all levels. It also shows that modern society is extremely vulnerable and unprepared to such events. A wide scientific and public discussion becomes urgent. Some possible directions of this discussion are suggested in this article. [ABSTRACT FROM AUTHOR]

Background: As reported by the World Health Organization, a novel coronavirus (2019-nCoV) was identified as the causative virus of Wuhan pneumonia of unknown etiology by Chinese authorities on 7 January, 2020. The virus was named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by International Committee on Taxonomy of Viruses on 11 February, 2020. This study aimed to develop a mathematical model for calculating the transmissibility of the virus. Methods: In this study, we developed a Bats-Hosts-Reservoir-People transmission network model for simulating the potential transmission from the infection source (probably be bats) to the human infection. Since the Bats-Hosts-Reservoir network was hard to explore clearly and public concerns were focusing on the transmission from Huanan Seafood Wholesale Market (reservoir) to people, we simplified the model as Reservoir-People (RP) transmission network model. The next generation matrix approach was adopted to calculate the basic reproduction number (R0) from the RP model to assess the transmissibility of the SARS-CoV-2. Results: The value of R0 was estimated of 2.30 from reservoir to person and 3.58 from person to person which means that the expected number of secondary infections that result from introducing a single infected individual into an otherwise susceptible population was 3.58. Conclusions: Our model showed that the transmissibility of SARS-CoV-2 was higher than the Middle East respiratory syndrome in the Middle East countries, similar to severe acute respiratory syndrome, but lower than MERS in the Republic of Korea. [ABSTRACT FROM AUTHOR]

• We measure infection of human (A549-ACE2) and animal (Vero-E6) cells by SARS-CoV-2. • We use mathematical modelling to estimate SARS-CoV-2 infection parameters. • SARS-CoV-2 infection rate of A549-ACE2 cells is lower than that of Vero E6 cells. • Latent phase of infected A549-ACE2 cells is longer than that of Vero E6 cells. • We conclude that Vero-E6 cells are not suitable for long-term drug screening assays. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV -2), a causative agent of COVID-19 disease, poses a significant threat to public health. Since its outbreak in December 2019, Wuhan, China, extensive collection of diverse data from cell culture and animal infections as well as population level data from an ongoing pandemic, has been vital in assessing strategies to battle its spread. Mathematical modelling plays a key role in quantifying determinants that drive virus infection dynamics, especially those relevant for epidemiological investigations and predictions as well as for proposing efficient mitigation strategies. We utilized a simple mathematical model to describe and explain experimental results on viral replication cycle kinetics during SARS-CoV-2 infection of animal and human derived cell lines, green monkey kidney cells, Vero-E6, and human lung epithelium cells, A549-ACE2, respectively. We conducted cell infections using two distinct initial viral concentrations and quantified viral loads over time. We then fitted the model to our experimental data and quantified the viral parameters. We showed that such cellular tropism generates significant differences in the infection rates and incubation times of SARS-CoV-2, that is, the times to the first release of newly synthesised viral progeny by SARS-CoV-2-infected cells. Specifically, the rate at which A549-ACE2 cells were infected by SARS-CoV-2 was 15 times lower than that in the case of Vero-E6 cell infection and the duration of latent phase of A549-ACE2 cells was 1.6 times longer than that of Vero-E6 cells. On the other hand, we found no statistically significant differences in other viral parameters, such as viral production rate or infected cell death rate. Since in vitro infection assays represent the first stage in the development of antiviral treatments against SARS-CoV-2, discrepancies in the viral parameter values across different cell hosts have to be identified and quantified to better target vaccine and antiviral research. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spreads rapidly and has attracted worldwide attention.Methods: To improve the forecast accuracy and investigate the spread of SARS-CoV-2, we constructed four mathematical models to numerically estimate the spread of SARS-CoV-2 and the efficacy of eradication strategies.Results: Using the Susceptible-Exposed-Infected-Removed (SEIR) model, and including measures such as city closures and extended leave policies implemented by the Chinese government that effectively reduced the β value, we estimated that the β value and basic transmission number, R0, of SARS-CoV-2 was 0.476/6.66 in Wuhan, 0.359/5.03 in Korea, and 0.400/5.60 in Italy. Considering medicine and vaccines, an advanced model demonstrated that the emergence of vaccines would greatly slow the spread of the virus. Our model predicted that 100,000 people would become infected assuming that the isolation rate α in Wuhan was 0.30. If quarantine measures were taken from March 10, 2020, and the quarantine rate of α was also 0.3, then the final number of infected people was predicted to be 11,426 in South Korea and 147,142 in Italy.Conclusions: Our mathematical models indicate that SARS-CoV-2 eradication depends on systematic planning, effective hospital isolation, and SARS-CoV-2 vaccination, and some measures including city closures and leave policies should be implemented to ensure SARS-CoV-2 eradication. [ABSTRACT FROM AUTHOR]