Evaluation of infectious diseases control using an individual model under the test-trace-isolate program

The global public health situation is constantly threatened by infectious diseases. To effectively control the spread of these diseases, it is crucial to quickly disrupt the transmission pathways of the pathogens. During the COVID-19 pandemic, testing, tracing, and isolation programs effectively responded to disease outbreaks in some areas but have largely failed in many other countries. This study presents a computational model to evaluate the effectiveness of various prevention and control measures in managing epidemic transmission dynamics. The model utilizes an individual-based model and dynamic close-contact networks to simulate the spread of infectious diseases. By considering the dynamic contact network formed by different individuals and their activities in various social environments, the model can track the spread of the disease and changes in the infection status of each individual through simulation. Using COVID-19 as an example, the model simulations demonstrate that infections increase rapidly after a local outbreak without preventive measures, quickly reach a peak of daily new infections. However, implementing test-trace-isolate measures significantly decreases the scale of infections and the number of daily new cases. Further stringent preventive measures to reduce individual contact are required to achieve the goal of zero infections. The results emphasize the importance of early detection and isolation in curbing the spread of the virus. The model established in this study can be used to evaluate and optimize prevention and control measures to achieve the goal of zero infections.