Working to build infectious disease outbreak models can be daunting especially for new students in epidemiology. Typically their first approach is to build compartment models after learning the basics of traditional statistical analyses. While compartmental models certainly have their place in epidemiology, I think it’s instructive for new students to learn and understand principles of Monte Carlo Simulation (especially Markov Chain Monte Carlo or MCMC), but sadly this material seems rarely to be covered in Masters level epidemiology courses. As I was mentoring and providing some guidance to a student today on a practicum, I was asked an excellent question: “What is the advantage of a MCMC simulation over a compartmental model?” After thinking through this a bit, I provided the following, which I think are effective guidelines to be used when considering when to select a MCMC model for an analysis, so I’d thought I’d share:
Flexibility
Monte Carlo models provide greater flexibility in modeling complex and dynamic systems. They allow for more intricate representations of real-world scenarios, including variations in individual behavior, social interactions, and other stochastic factors. Their flexibility is especially useful when dealing with heterogeneous populations or when the assumptions of compartmental models do not hold.
Incorporation of uncertainty
Monte Carlo models naturally account for uncertainty and randomness. They can include probabilistic distributions to represent uncertainty in various parameters such as transmission rates, incubation periods, or contact patterns. This allows for the exploration of a wide range of possible outcomes and the estimation of confidence intervals.
Individual-level simulations
Monte Carlo models operate at the individual level, simulating the behavior and interactions of each person in the population. This level of granularity allows for the consideration of individual variations, such as susceptibility, contact patterns, and compliance with interventions. It can capture the impact of heterogeneity on disease spread more accurately than compartmental models.
Simulation of interventions
Monte Carlo models are well-suited for simulating and evaluating the effects of different interventions and control strategies. By simulating a large number of scenarios, researchers can estimate the effectiveness of various measures, such as social distancing, vaccination campaigns, or contact tracing, and assess their impact on disease transmission.
Exploration of complex scenarios
Monte Carlo models can handle complex and multiple interacting factors. They can simulate the effects of changes in population demographics, travel patterns, or behavior modifications, allowing for a better understanding of the outcomes and the identification of critical factors driving the spread of the disease.
Other advantages exist, but I think these are perhaps the most relevant. I welcome and encourage others.