6.3 The Steps of Building a Model

The Model Function

\(y = f(x_1, x_2, ...) + \epsilon\)

where

\(y\) is the response, e.g., number of deaths or infections.

\(x_1, x_2, ...\) are predictors that might affect \(y\), e.g., vaccinations, demographics.

\(\epsilon\) is the error term (difference between observed values of \(y\) and predicted values from the model.

• Machine learning algorithms optimise the coefficients to minimise the difference between the observed and predicted values, and can handle complex structures.

• Model function can be used on new \(x\) values to predict future values of \(y\).