International Journal of Modeling, Simulation, and Scientific Computing

A mechanism-oriented model is proposed here as a speculative but robust attempt to understand whether there might be any increased risk of electrostatically induced contamination, with relevant consequences from the epidemiological viewpoint. This could also be the case for the COVID-19 spreading because an amount of micro-sized droplet nuclei, often carrying net electric charge, are expected to be electro-dynamically involved in a physical process originated by the natural and unperceivable static electrification of human beings. The effects of the triboelectric charging have long been successfully tested because the phenomenon under examination is also implied in the genesis of the electrostatic discharge (ESD), a demanding key objective in the special context of electromagnetic compatibility (EMC). Therefore, the ultimate purpose of this technical paper is to provide valuable insights into infection control, building on what is already being done for maintaining static-safe environments. The stature of the applied model can be further appreciated because some currently observed climate-dependent and sex-linked different vulnerabilities to COVID-19 are critically examined by unique sound arguments. These ultimately focus attention on ambient relative humidity and worn shoes, the latter differing for typology, size and material, for their integrated control of the inadvertent human aptitude to buildup tribocharges. These would appear as a dreadful prerequisite for charge bearing droplets in the airborne state to be efficiently attracted/repelled according to the described electrostatic mechanism.

The total number of infections (epidemic size), and the time needed for the infection to die out (epidemic duration), represent two of the main indicators for the measuring gravity of infectious disease epidemics in humans. A few attempts have been made to address the problem of controlling both the epidemic size and duration simultaneously from a theoretical point of view, by primarily using the Optimal control theory. In this study, a multi-objective optimal control problem has been simulated to gauge the success of the lockdowns in India. To accomplish this objective, a system dynamics modeling was used to simulate the Susceptible–Infected–Quarantined–Removed epidemic model. A set of sensitivity experiments for different scenarios allows illustrating the model’s behavior and its value for decision-makers regarding the lockdown intensity. The simulation of the model presents various scenarios, wherein the cost–benefit analysis of lockdown was done. Notably, the lockdown success intensity was defined, post which, the findings indicate that the states of Uttar Pradesh, Kerala, Panjab, Jammu, Kashmir, Haryana, and Bihar have achieved more than 90% lockdown success intensity. Further, it was observed that these states could effectively implement lockdowns by strictly enforcing social distancing measures during the early stages of the virus spread, which in turn resulted in the high success rate of lockdowns.

The world wide spread of COVID-19 epidemic has instigated an unprecedented demand and supply of research related to this epidemic. Together with medical research for treatment and cure of the pandemic, efficient mathematical modeling of epidemics is the need of hour. First, this paper depicts a detailed and comprehensive compendium of various numerical methods used for infectious diseases modeling during last 14 years including the very recent work done for COVID-19 models. This gives researchers a good insight about past work done, present efforts and future scope of numerical analysis methods for epidemic modeling. Second, this paper also proposes its numerical analysis approach based on Adomian decomposition method (ADM) for generalized SEIR model of COVID-19. The proposed method is shown to give very accurate numerical results.

The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic–stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR [Susceptible–Infected–Recovered–Protected–Isolated (SIRCX)] model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model. The estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be $4.78$ and $3.25$, respectively. ISA model-based results reveal that the number of cases in Italy and Spain between 11 May, 2020–9 June, 2020 will be 10,982 (6383–15,582) and 13,731 (3395–29,013), respectively. Additionally, the expected number of daily cases on 9 July, 2020 for Italy and Spain is estimated to be 30 (0–183) and 92 (0–602), respectively.

In this paper, we have proposed a nonlinear mathematical model of different classes of individuals for coronavirus (COVID-19). The model incorporates the effect of transmission and treatment on the occurrence of new infections. For the model, the basic reproduction number $({{ℛ}}_0)$ has been computed. Corresponding to the threshold quantity $({{ℛ}}_0)$, the stability of endemic and disease-free equilibrium (DFE) points are determined. For ${{ℛ}}_0>1$, if the endemic equilibrium point exists, then it is locally asymptotically stable, whereas the DFE point is globally asymptotically stable for ${{ℛ}}_0<1$ which implies the eradication of the disease. The effects of various parameters on the spread of COVID-19 are discussed in the segment of sensitivity analysis. The model is numerically simulated to understand the effect of reproduction number on the transmission dynamics of the disease COVID-19. From the numerical simulations, it is concluded that if the reproduction number for the coronavirus disease is reduced below unity by decreasing the transmission rate and detecting more number of infectives, then the epidemic can be eradicated from the population.

The dynamics of COVID-19 contagion investigated in São Luís-MA, Brazil, is based on the SIR model. São Luís was the first city in Brazil to implement the lockdown to control the disease. The SIR model’s definition and properties are presented in addition to the ways of estimating its parameters. The estimation of their values is due to a database released on the Maranhão State Health Department website during the period March 20 to July 2, 2020. The analysis of the proliferation of the disease shows the evolution of the COVID-19 in the city. The graphs illustrate that result.

The perishable nature of tourism products and services makes forecasting an important tool for tourism planning, especially in the current COVID-19 pandemic time. The forecast assists tourism organizations in decision-making regarding resource allocations to avoid shortcomings. This study is motivated by the need to model periodic time series with linear and nonlinear trends. A hybrid Polynomial-Fourier series model that uses the combination of polynomial and Fourier fittings to capture and forecast time series data was proposed. The proposed model is applied to monthly foreign visitors to Turkey from January 2014 to August 2020 dataset and diagnostic checks show that the proposed model produces a statistically good fit. To improve the model forecast, a Monte Carlo simulation scheme with 100 simulation paths is applied to the model residue. The mean of the 100 simulation paths within $\pm 2\sigma$ bounds from the model curve was taken and found to give statistically acceptable results.

In this paper, we will discuss prediction methods to restrict the spread of the disease by tracking contact individuals via mobile application to individuals infected with the COVID-19 virus. We will track individuals using bluetooth technology and then we will save information in the central database when they are in touch. Monitoring cases and avoiding the infected person help with social distance. We also propose that sensors used by people to obtain blood oxygen saturation level and their body temperature will be used besides bluetooth monitoring. The estimation of the frequency of the disease is based on the data entered by the patient and also on the data gathered from the users who entered the program on the state of the disease. In this study, we will also propose the best way to restrict the spread of COVID-19 by using methods of artificial intelligence to predict the disease in Jordan using Tensorflow.

This paper proposes spread prediction of novel corona virus outbreak using different compartmental models and artificial intelligence (AI) methods. Real data for several months is collected from the Ministry of Health (MOH) website, Kingdom of Saudi Arabia and two compartmental models, namely SIR (susceptible, infectious, recovered) and SEIRD (susceptible, exposed, infectious, recovered, dead) are utilized to best fit the data. AI methods are well suited for short- and long-term stochastic forecasts. Keeping in view the inherent advantages of AI methods, adaptive neuro-fuzzy inference system (ANFIS) models are trained using the collected data to replicate the dynamic behavior of the COVID-19 spread in Kingdom of Saudi Arabia. The prediction comparison for COVID-19 spread is made between the compartmental and ANFIS models for both short- and long-term forecasts of the experimental data. From the presented results, ANFIS-based models show superior performance as compared to compartmental models.

The fuzzy variable fractional differential equations (FVFDEs) play a very important role in mathematical modeling of COVID-19. The scientists are studying and developing several aspects of these COVID-19 models. The existence and uniqueness of the solution, stability analysis are the most common and important study aspects. There is no study in the literature to establish the existence, uniqueness, and UH stability for fuzzy variable fractional (FVF) order COVID-19 models. Due to high demand of this study, we investigate results for the existence, uniqueness, and UH stability for the considered COVID-19 model based on FVFDEs using a fixed point theory approach with the singular operator. Additionally, discuss the maximal/minimal solution for the FVFDE of the COVID-19 model.

Nowadays, coronavirus disease 2019 (COVID-19) poses a major threat to humanity which is caused by a novel coronavirus in Wuhan, China, at the end of December 2019. This novel coronavirus named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) according to the International Committee on Taxonomy of Viruses (ICTV). It spreads directly through close contact with an asymptomatic (carrier) or symptomatic person who is coughing, sneezing, talking, singing and kissing. It also spreads indirectly through contact with an environment contaminated by viral particles of SARS-COV-2. In the absence of effective treatment against COVID-19, awareness can play an important role in reducing cases of COVID-19. In this study, we develop a mathematical model to better describe the dynamics of COVID-19 by taking into account the above modes of transmission and the prosocial awareness. The existence, uniqueness, positivity and boundedness of solutions of the model are discussed. All possible equilibrium points are determined. The local asymptotic stability of these equilibrium points is studied. Suitable Lyapunov functionals are constructed to investigate the global dynamics of the model.

This paper analyzes the spread of the COVID-19 epidemic in China, establishes a ${\mathrm{\text{SEIR}}}^{+\mathrm{\text{CAQ}}}$ epidemiologic model that classifies the diagnosed population into asymptomatic, mild, and severe, and considers the condition of an isolated population. First of all, we theoretically analyzed the key problems of the disease-free equilibrium, stability, and basic reproduction number of the model. Then, combined with the actual data, the development trend of the epidemic situation is simulated and compared, and the influence of some important parameters is discussed in the model evaluation part. Finally, based on the theoretical analysis and numerical simulation results, we forecast the future development of the COVID-19 epidemic and put forward some reasonable scientific suggestions for the government to formulate a future epidemic prevention policy.