How to apply structural equation modelling to infectious diseases concepts.

Area of Science:

• Epidemiology
• Biostatistics
• Infectious Disease Research

Background:

• Traditional association tests have limitations in addressing causation in infectious diseases.
• Structural Equation Modelling (SEM) provides a framework to investigate causal relationships.
• SEM is particularly relevant for analyzing complex interventions and 'big data' in epidemiology.

Purpose of the Study:

• To differentiate SEM from traditional association tests.
• To highlight the computational and conceptual distinctions between these methodologies.

Main Methods:

• A literature search was conducted on Google Scholar using terms 'structural equation modelling' and 'infection.'
• Examples of SEM applications in infectious disease research were reviewed.

Main Results:

• SEM allows for direct testing of postulated causation models against empirical data.
• Models can be identified as 'importantly wrong' or potentially useful for prediction.
• SEM can address questions about the magnitude of intervention effects on unmeasurable entities.

Conclusions:

• SEM and related modeling techniques are increasingly valuable for infectious disease research.
• The rise of 'big data' will likely drive further adoption of SEM in this field.