A GENERAL FRAMEWORK FOR AGENT-BASED MODELLING WITH APPLICATIONS TO INFECTIOUS DISEASE DYNAMICS

摘要

Advanced information and communication technologies are increasingly applied to collect heterogeneous data on various aspects of population health, including the emergence of novel diseases and their spread in human populations. Because information evolves rapidly during an emerging disease epidemic, and information on key epidemiological features of the disease may be subject to substantial uncertainty, more advanced methods and computational capabilities are required to guide optimal disease control policies and support decision-making under uncertainty. Agent Based Modelling appears to be an effective method to analyze such information and data and, in principle, capture the true mechanisms underlying disease processes at both the individual and population levels. In practice, the fidelity of agent-based models is only limited by the amount, variety, and richness of data available; by the human resources necessary to implement the model; and by the availability of computational resources for simulation. Here, we propose a general framework for an agent-based modelling computational system to encapsulate important variables and parameters of demographic, health, and infection, and to simulate the infection dynamics. This framework has the potential to be used for decision-making support in real-time event of an emerging disease.