STOCHASTIC MODEL ANALYSIS OF THE IMPACT OF MEDIA CAMPAIGN ON TRANSMISSION OF COVID – 19 EPIDEMIC.

K. Bashiru, T. Ojurongbe, M. L. Olaosebikan, N. Adeboye, H. A. Afolabi, Ife Olukotun
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Abstract

The COVID - 19 pandemic is currently causing authorities and public health officials more concern. The goal of the project is to convert a deterministic model for COVID-19 transmissions to a stochastic model, and then analyze the results to see how media-driven awareness campaigns have an impact on the disease's spread. The dynamic COVID-19 model was converted to a stochastic model, which was then examined. The model includes the following categories: Susceptible (S), Exposed (E), Infected class (I),  Isolated class ( ), Aware class  and Recovered class (R), as well as the Cumulative density of awareness programs by media denoted by   . With the help of MATLAB, the converted model is then numerically solved using the Eula Maruyama approach, allowing the existence and uniqueness of the model to be examined. The implementation of awareness programs has been found to have a significant positive impact on the spread of COVID-19. As the rate of implementation of these programs rises, the population that is exposed to the virus and those who are infected with it declines, and it has been hypothesized that this will eventually cause COVID-19 to become extinct. According to the report, putting awareness campaigns into place can help stop the COVID-19 epidemic from spreading.
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媒体宣传对covid - 19疫情传播影响的随机模型分析。
COVID - 19大流行目前引起了当局和公共卫生官员的更多关注。该项目的目标是将COVID-19传播的确定性模型转换为随机模型,然后分析结果,以了解媒体驱动的宣传活动如何影响疾病的传播。将动态COVID-19模型转换为随机模型,然后对其进行检验。模型包括易感人群(S)、暴露人群(E)、感染人群(I)、隔离人群()、意识人群(R)和康复人群(R),以及媒介意识项目的累积密度。在MATLAB的帮助下,利用Eula Maruyama方法对转换后的模型进行数值求解,从而检验模型的存在性和唯一性。人们发现,宣传方案的实施对COVID-19的传播产生了重大的积极影响。随着这些计划的实施率上升,接触病毒的人口和感染病毒的人口减少,据推测,这最终将导致COVID-19灭绝。根据该报告,开展提高认识运动可以帮助阻止COVID-19流行病的蔓延。
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