"If you've got a problem with the biology of a disease, you don't throw maths at it, you look at the biology." [1][24][25]

Two relevant questions have been mooted with respect to biomodels:

(A) If biomodels are models and models are maths, and maths should be used cautiously within biology, then what exactly are biomodels? (B) How are they also being used for disease control?"

The explanations stem from the different ways in which (1) first generation mathematical disease models are created, compared to the ways in which (2) second generation biomodels are created.

(1) Mathematical (or first generation) disease models divide a population of animals or people, according to their disease states (ie. recovered, infected, susceptible, etc.) [20]. The model subsequently moves individuals between the various disease states, and repeats those movements for each subsequent time period (ie. every day, every week, every two weeks, etc.). The dynamic movement of individuals between disease states (and every time period) creates a simulation of the epidemic. Some models add locations or relevant maps to the disease spread. The inaccurate guessing commences for the number of individuals that are moved at each time period, and the first guess puts a numerical value to a theoretical 'Reproductive Rate' (Ro), or an 'Effective Contact Rate' (ECR). Ro and ECR determine the rate at which individuals pass between the disease states (for each new time period). Ro and ECR are mathematical guesses, and are produced from additional multiple guesses about the biology, such as the length of the infectious period for a disease, and the incubation period for a disease, and the level of population immunity against a disease. It would be correct to conclude that this inaccurate guessing is cumulative.

First generation models can mimic what an epidemic looked like [examples: ] in terms of the shape of their (incidence and prevalence) graphs. This becomes possible by manipulating the values of Ro, ECR, the incubation period, infectious period, immunity factors, etc., and if the graphs produced have not created a good match, more 'factors' can be added until a good graphical match is created. [The inclusion of the additional factors can be justified.] It is therefore straight forward to match first generation models to past epidemics. The difficulty for first generation models is to predict what will happen in the future, because models can be manipulated when they have an epidemic to copy, yet without a past epidemic to copy any model manipulations become guesswork.

(2) Biomodels dispense with disease states, or moving individuals between diseases states, and move towards the end of an epidemic [22] where prevalence and duration are measured directly: this is possible because the complex biology has already occured, so there is no need to guess it [23]. Effectively, the guessing has become redundant. The ability to predict prevalence (ie. the final number of infected individuals) and likewise the duration of an epidemic, stems from biomodel factors such as subclinical disease. Subclinical disease spreads before the clinical signs are shown, so when the signs are shown, the prevalence and duration of an epidemic has mostly been predetermined. The control of disease spread still remains possible for individuals that have not become infected with subclinical disease. By measuring the level of subclinical disease it's possible to both predict prevalence and duration, and then simulate what will happen during an epidemic before it has happened. This is again possible because the disease has already spread but the clinical signs have not yet appeared [8]. Biomodel factors such as subclinical disease can also be incorporated into mathematical simulators to replace Ro or ECR - this allows model simulations to test whether certain disease control measures or contingency plans will be effective in the future.

Measuring subclincal disease (or similar disease factors), means that guesswork is no longer necessary when constructing disease models. Biomodels represent a second generation of disease modelling.