Several models of infectious diseases have been developed in the literature [22 27].
Mathematical models of infectious diseases play an important role in informing public health policy.
It has long been observed that interpersonal contacts with an ability to transmit a disease are not random as assumed by simple models of infectious disease spreading.
But for many mathematical models of infectious diseases in a patchy environment, the global stability of the endemic equilibrium is still an open problem.
Much research on this topic has been done on compartmental models of infectious diseases such as influenza, malaria, dengue, cholera, etc. [21 25].
Epidemic models of infectious diseases go back to Daniel Bernoulli's mathematical analysis of smallpox in 1760, and they have been developed extensively since the early 1900s.
In different models of infectious disease, it has been demonstrated that DCs can serve as vaccine carriers, mediating protection against various types of pathogens.
We discuss the various levels of coarse-graining that can be used to represent the data in order to inform models of infectious disease transmission.
Rabies virus and its associated host pathogen population dynamics have proven a remarkable model system for developing mathematical models of infectious disease emergence and spread.
Among these studies, MIMP displays protective effects in several in vivo models of infectious disease following administration by one of several routes including oral.
In the design of social network studies to inform mathematical models of infectious disease spread, it is important to note the contextual differences between developed and resource poor settings.
