SIMULATION OF MATHEMATICAL MODEL OF CORONAVIRUS

Abstract

We propose a mathematical model to investigate the current outbreak of the coronavirus disease 2019 (COVID-19). Our model describes the multiple transmission pathways in the infection dynamics, and emphasizes the role of the environmental reservoir in the transmission and spread of this disease. In addition to this, our model employs non-constant transmission rate changes that reflect the effect of continuous diseases control measures with regard to epidemiological condition and environmental conditions. We evaluate this model in depth and use the publicly reported data to explain its application. Our analytical and numerical results, among others, suggest that the coronavirus infection is still widespread and that long-term disease prevention and intervention programs are important. In particular, a new approach takes into account the fraction of observed cases over the actual total infected cases, which allows the influence of this relationship on the effect of COVID-19 to be examined. In addition, the model will estimate bed requirements in clinics. This is difficult enough to define the most significant but also easy effects so that its parameters can be affordably identified using the data on the pandemic published by the authorities. We are analyzing the specific case of the country that spreads the disease and using its reporting data to identify models that can be useful to estimate COVID-19 spreading in other countries (including the Chinese mainland, Macau, Hong Kong and Taiwan, as per the World Health Organization report in its report COVID-19). We show a strong understanding between the data reported and our model's estimates. In addition, when considering incomplete reported data (through cutting it down on some dates prior to and after the maximum number of reports), we analyze the actions of the tests that our model returns. By comparing these effects, the error produced by the model can be calculated by defining the parameters in the early stage of the pandemic. Finally, we research different scenarios, which show how the various values of the percentage of identified cases have altered China's global magnitude of COVID-19 that may be of concern to policymakers, taking account of the advantages of developments implemented by our model.