Abstract
Major infectious diseases have exerted a serious influence on people's lives. Through quantifying the effect of prevention and control, we can deeply understand the transmission mechanism of infectious diseases. This paper estimates the intensity of detection, the degree of isolation and other indicators, and analyzes the influence mechanism of these indicators on the scale of the epidemic, using computer programming to simulate the extended dynamics model of infectious diseases, based on the infectious disease in Hubei. The mortality rate and recovery rate, according to the data of Hubei, in the model are set as time variables, and the threshold is set at the same time. As a result, the improved analysis mechanism of the model will get more realistic simulation prediction results. It is concluded that isolation measures can effectively control the scale of the epidemic, but there is a phenomenon of marginal utility degression with excessively strict isolation measures by analysing and comparing. The increasing detection efforts will reduce the epidemic duration of the later stage, accelerating the arrival of the epidemic peak, although the peak will be slightly larger. All in all, we can comprehensively consider the testing cost and maintain a moderate detection intensity.
Highlights
As a recent major epidemic disease, the COVID-19 epidemic poses a threat to people’s health in many countries
This paper, denoting the detection intensity, isolation and other measures as numerical indicators, and using the actual epidemic data to estimate the values of these indicators, constructs the mathematical model of infectious diseases
The dead: marked as Dt, it means that the people who die of infectious diseases at time t will no longer affect the dynamic behavior of the system
Summary
As a recent major epidemic disease, the COVID-19 epidemic poses a threat to people’s health in many countries. This paper, denoting the detection intensity, isolation and other measures as numerical indicators, and using the actual epidemic data to estimate the values of these indicators, constructs the mathematical model of infectious diseases. In this way, various measures in different regions can be compared intuitively, we can observe the relationship between the implementation of various measures and the effect of prevention and control, and predict the trend of epidemic in the future, to provide references for the formulation of relevant decisions
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