Comparative AnalysisOfComputational ModelsForInfectiousDiseases

In recent years, computational epidemiology has gained momentum due to the need to understand, forecast, and manage the spread of infectious diseases. This research paper offers a systematic comparative assessment of three main types of computational models of infectious diseases: compartmental models, agent-based models and network models. Through a thorough review of the relevant literature, case studies and interviews with experts, we discuss the advantages, disadvantages and use cases of each modelling approach in epidemiological studies. We consider the theoretical principles on which models are based, the data needed to develop models, the computational complexity involved, the accuracy of models prediction and their transferability to new epidemiological settings. Our findings - detailing the strengths and weaknesses of each modelling approach - also illustrates the ways in which these modelling approaches can complement each. And, the analysis artifacts reveal that the choice of a modelling approach should be based on the respective disciplinary and methodological knowledge of researchers and availability of data with which questions related to infectious diseases are to be addressed. In addition, we provide insights on emerging trends in computational epidemiology such as the use of big data and machine learning approaches that work towards improving model accuracy and predictive capabilities. This study offers a practical contribution for researchers and decision-makers involved in infectious disease modelling by drawing attention to the complexity that surrounds efforts to model infectious diseases and by providing practical information in support of a research and decision-making position regarding which modelling approaches may be considered or preferred to support the work undertaken across various research and policy contexts in the future.