The dynamics of Clostridium Difficile and commensal bacteria through the lens of evolutionary game theory from perspectives of artificial intelligence

Clostridium difficile (C. Diff) Infection (CDI) is one of the most severe hospital-acquired diseases, it is caused by the disturbance of intestinal commensal bacteria such as the antimicrobial treatment. It can result in several symptoms including diarrhea, pseudomembranous colitis and even death. CDI can hardly be treated with antibiotic agents due to its high resistance to antibiotics. The most commonly used treatment for C. diff infection is a faecal transplant, which aims to recover the normal population of the commensal bacteria. To improve the effectiveness of prevention and treatment, a simulation of the population dynamics between commensal bacteria and C. diff would be helpful. This project mainly focused on the establishment of such a model with the application of evolutionary game theory. The simulation was able to give the critical value of the population of commensal bacteria that shifts the population dynamic from healthy to disease state. It suggested that the CDI is not caused by the gradual decrease of commensal bacteria but by the population of commensal bacteria decreased to a certain level. Antibiotics were also involved in the simulation. The result showed the antibiotics could kill a large proportion of commensal bacteria thus resulting in the CDI. Increase in the antibiotic resistance of C. diff will increase the incidence of CDI. The high flexibility of this model also allowed other types of population dynamics to be simulated. However, this model is still of concept, there is a long way to go before its practical application.