Future perspectives

"In terms of crystal ball modelling, subclinical disease is fantastic. Subclinical disease allows you to predict the future for an infectious disease, but what comes next? There's been a lot of work on spatial modelling but it's not clear that we really need it. Subclinical disease shows you where the disease has spread to when it finally shows clinical signs, so we can already trace the spread. What we actually need to know, is what happens between epidemics, and where does the disease go between epidemics? If we can find the disease when it's subdued, then maybe we can stop it from producing the next epidemic. So a good guess for the future of disease modelling is that it lies in looking for disease carriers."

First generation mathematical models introduced the concept of applying mathematics to epidemiology. Subsequently, experienced epidemiologists pointed out some of the pitfalls involved with these early models [24]. Experienced veterinary surgeons have highlighted the need to separate politics from disease control policies [17], and regional adminstrators and academic veterinarians alike have stressed the need for intelligently targeted disease control [11][25]. The evolution of epidemiological disease models has been rapid and interesting: it continues to be an area of fascinating expansion, both currently and for the foreseeable future.