A Mathematical Approach to Prevent and Control COVID-19 Outbreaks: The Improved M-E Model

The COVID-19 virus, the eighth in the family of Coronaviridae, will spread swiftly among people, birds, and other animals. This virus spreads mostly via human infections and primarily affects the respiratory and nervous systems. There were more patients reported to hospitals in Wuhan, China, in the month of December 2019. They recognized it as a brand-new Corona virus known as COVID-19. The uncontrolled virus presents humans with two significant problems. Inadequate medical resources are a result of the anomalous development of COVID-19 cases. Lockdowns and other emergency procedures are used as a precaution. We provide a preliminary mathematical model for COVID-19 control and prevention that is based on evolutionary graph theory in our first paper. The well-known social distance approach is used in the proposed model in a variety of ways. In present research an updated model was generated and named as an Improved M-E (Mathematical-Based Epidemic Model) to Prevent and Control COVID-19 through random clustering. Lockdown in many nations causes a drop in GDP and an increase in mental health issues among the population. The distribution of anti-virus has done based on clustering the total population into small groups based on location, relation, culture etc. In the next process identify the random person among the clusters and inject the anti-virus, so that the proposed model will generate quick recovery then the previous model.