## 1 Introduction  
With the aging trend of the world population, the prevalence of chronic and comorbid diseases is rising rapidly in various countries around the world [1; 2; 3; 4; 5]. A study showed that most elderly people in China are suffering from one or more chronic diseases [6]. Since ensuring the health of residents has become a major task for the country, chronic diseases and their comorbidities place a heavy burden on patients' families as well as on the Chinese national healthcare system. Although with the continuous improvement of national medical facilities, elderly patients can detect physical chronic diseases earlier and obtain treatment earlier, Chinese scholars have also carried out various related studies on chronic diseases and their comorbidities. However, most of the current studies on chronic diseases generally have shortcomings such as single survey content and only analysis of related symptoms. Some studies have also carried out chronic disease analysis based on population cohort observation, but they cannot substantially reflect the disease course and influencing factors of patients with chronic diseases and their comorbidities from the perspective of the observed population alone. Because each patient has different disease courses, the different stages of the comorbidity of chronic diseases evolve under the combination of different conditions and factors. In the modern social living environment, there are many risk factors for chronic diseases. In addition to social, environmental, and population-related accidents, these risk factors are also closely related to the lives of people with chronic diseases. A prerequisite for precision medicine is to estimate subsequent disease progression from the current patient status. The factors that affect the development of patients' diseases include age, sex, constitution, and previous chronic diseases, which should be taken into account in the study. Different individuals in the population have different physical conditions, different living habits and eating habits, which may lead to different chronic diseases and their comorbidities. In the field of chronic diseases and their comorbidity research [7; 8], many researchers have made many attempts to study the development process and risk factors of chronic diseases comorbidity. Recently, the approach of analytical studies using collected resident medical data or population cohorts has been increasingly adopted by many researchers, and multiple quantified indicators have been considered. However, there is no clear way to describe how multiple chronic diseases evolve over time under different conditions. Traditional statistical learning methods are very useful for evaluating a single disease, but multiple diseases are linked to coevolve and incorporating other conditions into the analysis requires a better model or method. Although the population cohort method can assist in the study of comorbid transfer of chronic diseases, the population cohort collection requires a lot of manpower and material resources and takes too long. The data simulation method can simulate large populations in a relatively short time, so as to verify which factors will be the key factors for the occurrence and transfer of comorbidities. The focus and goal of this study is to explore a way to build a relatively accurate simulation framework for the evolution of chronic diseases and their comorbidities in the whole life cycle of the population cohort. We will continue to explore adding more factors into the simulation framework, so as to more accurately simulate the influence of each factor. In this work, we focus on three main chronic conditions either as isolated presentation or combined with the others: ischemic heart disease (IA), ischemic stroke (IS), type II diabetes (DM), ischemic heart disease and ischemic stroke (IA and IS), ischemic heart disease and type 2 diabetes (IA and DM), and ischemic stroke and type 2 diabetes (IS and DM).