In December 2019, there was a case of viral pneumonia in Wuhan. After confirming that the pathogen of this disease is a new coronavirus, the World Health Organization (WHO) confirmed and named it 2019-nCoV. The pneumonia caused by this pathogen infection is called a novel corona virus pneumonia.
In order to cope with this sudden public health problem, Wuhan officially implemented the "Class A infectious disease blockade" as stipulated by the "Infectious Diseases Prevention and Control Law" on January 23.
At this stage, it is important to better understand the mode of transmission of 2019-nCoV among the population and the effects of control measures. This information will help when deploying and coordinating further epidemic prevention and control, evaluating the effectiveness of control measures, and in helping reduce panic.
To study the prevalence of infection in a population, we built an agent-based epidemic simulation model to simulate an interactive environment over a certain space-time range, where asymptomatic 2019-nCoV carriers entered the uninfected population within a certain space-time range. Epidemic simulation models have been successfully applied elsewhere for epidemic simulation, evaluating disease spread, and mitigation strategies.
This study in particular assumes that after the onset of 2019-nCoV infection in this space-time range, patients can be effectively treated and isolated, and that close contact between people can be effectively reduced. Furthermore, this study based on disease transmission simulation model demonstrates the trend of 2019-nCoV infection at different levels of close contact in order to provide relevant information and references. In order to simulate the epidemic trend of 2019-nCoV in the disease transmission simulation model, a total of 10,000 subjects were set up, and one of them was randomly infected. Epidemic simulations were performed 10 times in total, and the average value was taken as the experimental result. The data was sorted and cleaned up using R3.6.2 software, and the agent-based model was implemented using AnyLogic software.