Mechanistic models that predict disease rates based on intrinsic
(i.e. those inherent to the epidemiological system, such as dynamics of immune
response) or extrinsic (i.e. independent of the epidemiological system, such as
climate change) can anticipate future patterns due to non-linear interactions
that might not be apparent in future dynamics.
However, it is challenging to develop and parameterize these models to
individual settings (e.g. because transmission and contact rates, or human
behavior differ from place to place).
Forecasting has been more effectively used to predict changes in disease
rates or invasion risk for vector-borne disease [e.g.Bhatt 2013], because the
environmental conditions that are suitable for the vector can be reasonable
studied in the absence of the pathogen.