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Sir - Springer Nature

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Last Updated: 20 September 2022

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Simulating and Modeling the Vaccination of Covid-19 Pandemic Using SIR Model - SVIRD

The COVID-19 epidemic is not only affecting the health sector but also the economy in these days. Results: We used a series of simulations for the four parameters in the SVIRD model, and the result of the simulation indicates that: The more connected a population is, the higher vaccination rates must be to adequately protect the population. Also, the relationship between vaccination and infection rates seems to be more like an exponential decay, and suicide rates for very low and high numbers of connections seem to be increasing linearly with increased and higher numbers of connections.

Source link: https://doi.org/10.1007/978-3-031-15191-0_40


SIR-M Epidemic Model: A SARS-CoV-2 Perspective

Specifically in India, the second wave of the mutant SARS-CoV-2 epidemic has statistically demonstrated an exponential increase in mortality rate. In addition, the SIER model did not expose the virus, which caused the infection. The World Health Organization's daily reports also reveal unavoidable estimates that represent the number of deaths. As of May 30, 2021, India has confirmed 27 million total infections as of May 30, 2021, and 32 thousand deaths have been reported. This alerts us that pandemic epidemic spread cannot be represented as a simple SIR diagram. The significant difference between the infected and the mortals causes the SIR model to be redesigned as a SIR-M model. This paper inserts the death count into the model and updates the SIR model as a SIR-M model. We have investigated the causes of death in COVID-19 patients and have validated our model with the real-time COVID dataset.

Source link: https://doi.org/10.1007/978-981-19-3391-2_38


Optimal control for a multi-group reaction–diffusion SIR model with heterogeneous incidence rates

This paper is dedicated to the investigation of an optimal control problem for a generalized multi-group reactionu2013diffusion SIR epidemic model, with heterogeneous nonlinear incidence rates. The proposed model incorporates a large number of spatiotemporal epidemic models. In order to capture all the possible routes of infection, it is necessary to divide the vulnerable, infected, and recovered populations into several subpopulations. The issue of finding the appropriate control tactics and the organization of the available control resources is also complicated. In addition, using the adjoint state method, first-order optimality conditions for the newly introduced optimal control issue are established for the first-order. Lastly, numerical simulations are conducted for a two-group epidemic model with particular prevalence rates and accounting for three cases in the maximum control capacity allowed for each subpopulation.

Source link: https://doi.org/10.1007/s40435-022-01030-3


Analysing the Effect of Test-and-Trace Strategy in an SIR Epidemic Model

Consider a Markovian SIR epidemic model in a homogeneous group. We'll see how many people are tested, and if an infectious individual tests positive it is isolated, we'll measure it independently with some absolute certainty. If such a traced individual tests positive, it is isolated, the contact tracing is done. The reproduction number for the branching process of components is not monotonically decreasing in the tracing likelihood, but the individual reproduction number is expected to be monotonic as expected. Further, in the situation where individuals also self-report for testing, the tracing probability is more relevant than the screening rate.

Source link: https://doi.org/10.1007/s11538-022-01065-9

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions