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Covid-19 has infringed socio-economic problems on many nations. This article is on the global overview of the disease and modeling procedures to contain the disease. We think of how to exploit mathematical modeling and simulation techniques to study the parthenogenesis, epidemiological characteristics of the disease, and its demographic impact on countries. Moreover, make use of Machine learning interpretable algorithms to analyze covid-19 data and measure the daily basic reproduction numbers of the countries. It is suggestive to study the dynamics of Covid-19 from a sub-molecular level, to understand biotransformation of coronavirus to other variant forms, and to measure drugs and vaccines efficacies. The strategy of the study is to explore new knowledge and simulation tools to understand the dynamics and containment of the disease. The first major challenge is how to boost the economy of nations by providing gainful employment to a large number of people. The second challenge being how to provide effective drugs and vaccines to contain the virus and its variant forms. The third is on how to measure the risk associated with the pandemic and measure the effectiveness of containment strategies.
Scientific Journal of Astana IT University
The COVID-19 epidemic has gone down in history as an emergency of international importance. Currently, the number of people infected with coronavirus around the world continues to grow, and modeling such a complex system as the spread of infection is one of the most pressing problems. Various models are used to understand the progress of the COVID-19 coronavirus epidemic and to plan effective control strategies. Such models require the use of advanced computing, such as artificial intelligence, machine learning, cloud computing, and edge computing. This article uses the SIR mathematical model, which is often used and simple to model the prevalence of COVID-19 infection. The SIR model can provide a theoretical basis for studying the prevalence of the COVID-19 virus in a specific population and an understanding of the temporal evolution of the virus. One of the main advantages of this model is the ease of adjusting the sampling parameters as the study scale increases and the most appr...
Subject Classification 92D30 92D25 92C42 34C60 Keywords: Coronavirus disease (COVID-19) Mathematical modeling Model reduction Sensitivity analysis Computational simulations
2021
For more than a year, the COVID-19 pandemic has been a major public health issue, affecting the lives of most people around the world. With both people’s health and the economy at great risks, governments rushed to control the spread of the virus. Containment measures were heavily enforced worldwide until a vaccine was developed and distributed. Although researchers today know more about the characteristics of the virus, a lot of work still needs to be done in order to completely remove the disease from the population. However, this is true for most of the infectious diseases in existence, including Influenza, Dengue fever, Ebola, Malaria, and Zika virus. Understanding the transmission process of a disease is usually acquired through biological and chemical studies. In addition, mathematical models and computational simulations offer different approaches to predict the number of infectious cases and identify the transmission patterns of a disease. Information obtained helps provide ...
EC Pulmonology and Respiratory Medicine, 2021
models for infectious diseases and their statistical tools have become an integral part of the inputs for planning control and mitigation measures. These models allow us to test different strategies in simulations before applying them to groups of people or individuals. One specific goal of our future modeling is going to be to check the efficiency of testing and make contact with tracing. One must be very cautious regarding model predictions, because different models that lead to similar outcomes in one context may fail to do so in another. In such instances, it is best to conduct further epidemiological and experimental studies in order to discriminate among the different possible mechanisms. Thus, an important role of modelling enterprises is that they can alert us to the deficiencies in our current understanding of the epidemiology of various infectious diseases and suggest crucial questions for investigation and data that need to be collected. Therefore, when models fail to predict, this failure can provide us with important clues for further research.
Journal of Public Affairs, 2020
In this study, we examined various forms of mathematical models that are relevant for the containment, risk analysis, and features of COVID-19. Greater emphasis was laid on the extension of the Susceptible-Infectious-Recovered (SIR) models for policy relevance in the time of COVID-19. These mathematical models play a significant role in the understanding of COVID-19 transmission mechanisms, structures, and features. Considering that the disease has spread sporadically around the world, causing large scale socioeconomic disruption unwitnessed in contemporary ages since World War II, researchers, stakeholders, government, and the society at large are actively engaged in finding ways to reduce the rate of infection until a cure or vaccination procedure is established. We advanced argument for the various forms of the mathematical model of epidemics and highlighted their relevance in the containment of COVID-19 at the present time. Mathematical models address the need for understanding the transmission dynamics and other significant factors of the disease that would aid policymakers to make accurate decisions and reduce the rate of transmission of the disease.
medRxiv, 2020
A novel coronavirus (COVID-19) was identified in Wuhan, China in the end of 2019, it causing an outbreak of viral pneumonia. It caused to the death rate of 4:63% among 571; 678 confirmed cases around the world to the March 28th, 2020. In this brief current study, we will present a simple mathematical model where we show how the probability of successfully getting infected when coming into contact with an infected individual and the per capita contact rate affect the healthy and infected population with time. The proposed model is used to offer predictions about the behavior of COVID-19 for a shorter period of time.
Arabian Journal for Science and Engineering
The entire world has been affected by the outbreak of COVID-19 since early 2020. Human carriers are largely the spreaders of this new disease, and it spreads much faster compared to previously identified coronaviruses and other flu viruses. Although vaccines have been invented and released, it will still be a challenge to overcome this disease. To save lives, it is important to better understand how the virus is transmitted from one host to another and how future areas of infection can be predicted. Recently, the second wave of infection has hit multiple countries, and governments have implemented necessary measures to tackle the spread of the virus. We investigated the three phases of COVID-19 research through a selected list of mathematical modeling articles. To take the necessary measures, it is important to understand the transmission dynamics of the disease, and mathematical modeling has been considered a proven technique in predicting such dynamics. To this end, this paper sum...
International Journal of Scientific and Management Research, 2021
The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics, for the high and low pandemic case, which occurred during the year 2021 at State de Paraná Brazil and these results were compared. For this procedure, the model parameters using officially reported of State de Paraná Brazil were calibrate. As result the S (susceptible population) and E (exposed population) parameters decay as a function of time, being a very drastic drop for S and a slow decrease to E in high pandemic, however, the I (infected population) parameter rises and decays as a function of time, but, in a high pandemic, the tendency is to grow, nevertheless, the R (recovered population) parameter rises as a function of time, in low pandemics this parameter has a much higher growth behavior than in high pandemics, as expected. So, the numerical simulation is consistent with reality. This result is coherent with what is happening in the scenario in the different cities of the countries of the world. Among the advantages of the implemented model, it should be noted that despite the simplicity of the hypotheses, the adjustments obtained were quite accurate and the projections made do not differ much from those other more complex models. Our results could also provide useful suggestions for the prevention and control of the COVID-19 outbreaks in different countries and locations.
How does COVID-19 pandemic affect the India? How many people can be hit in a state and how many of them will succumb to the disease? When is it going to peak? How long should the government continue with the lockdown? What is the damage to the economy and what is its impact on each sector? These are some of the questions that haunt not only the decision makers but every sensible people in India. Mathematical models developed by mathematicians and epidemiologists has come to assist decision makers in evaluating the effects of countermeasures to an epidemic before they actually deploy them. The model could give political and beuricatic person's critical insights into the best steps they could take to counter the spread of disease in the face of pandemics. Mathematicians use modeling to represent, analyze and make predictions or otherwise provide insight into real world phenomena. Real world scenarios can be designed into a mathematical model to bring clarity to big messy questions amid fast changing variables. These models aim to make simplifying assumptions in order to arrive at tractable equations. Dealing with the novel coronavirus is an unprecedented situation which the world could not have foreseen. In order to track the COVID-19 pandemic, make predictions about the disease's progression and take decisions, as of now, the government is solely dependent on data from doctors and health workers.
Paediatric Respiratory Reviews
Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this ongoing pandemic requires extensive collaboration across the scientific community in an attempt to contain its impact and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R 0 (of approximately 2-3); (2) updating these estimates following the implementation of various interventions (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread before significant case numbers had been reported internationally; and (4) quantifying the expected disease severity and burden of COVID-19, indicating that the likely true infection rate is often orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response. yUnless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.