Epidemic models which incorporate disease evolution

[*melinda*]

I was wondering if anyone knew anything about epidemic models which take into account the ability of a disease to mutate. Basically I’m curious if there are any existing models which could predict how a rapidly changing disease might affect the progression of an epidemic, or how slower mutations in a disease might affect populations where that disease is endemic.

Based on what I’ve read so far it seems like the SIR (susceptible, infectious, recovered) model described by systems of differential equations is the standard epidemic model in use. And the only thing I know about disease evolution (i.e. genetic drift and shift) is that it seems to be modeled probabilistically. I’ve seen both topics covered independently, but so far I have not read anything that deals with how these things might interact.

Is this question in any way biologically relevant or realistic?

 

[jim mcnamara]

In general, most mutations do not work to the favor of the pathogen.

So, what you are asking is: how can mutation rate affect the S component of SIR?
My opinion is that it can change. See Paul Ewald's 'Evolution of Infectious Disease' - the section on the increase in susceptibility/virulence that occurred during the Flu Pandemic of 1918-1919.

How you would quantify this is beyond me.

 

[ravenprp]

Yes, incorporating the ability of a disease to mutate is a crucial aspect of epidemic modeling and is a biologically relevant and realistic question. In fact, there are several epidemic models that take into account the evolution of a disease. One example is the SEIR (susceptible, exposed, infectious, recovered) model which includes a latent period where individuals are exposed to the disease but not yet infectious. This model can also be extended to include mutations by incorporating a compartment for mutated strains of the disease.

Another approach is to use a network-based model, which takes into account the interactions between individuals and how the disease can spread through a population. In this type of model, the ability of a disease to mutate can significantly impact the spread of the disease, as it can change the transmissibility and severity of the disease.

There are also models that specifically focus on the evolution of a disease and how it may affect its ability to spread and cause an epidemic. For example, the quasispecies model is used to study the evolution of viruses and how mutations can lead to the emergence of new strains that may be more virulent or resistant to treatments.

In terms of predicting the effects of a rapidly changing disease, there are models that use real-time data to track the evolution of a disease and make predictions about its spread and potential impact. These models are constantly updated as new information becomes available and can be used to inform public health interventions and strategies.

Overall, incorporating disease evolution into epidemic models is crucial for understanding and predicting the spread of diseases and their potential impact on populations. It is an active area of research and there are various models and approaches being developed and used to address this important aspect of epidemiology.