# How a day is modelled¶

You have now used multiple model runs to refine the disease parameters for the lurgy. However, these parameters have been refined for the model that metawards uses to represent interactions of individuals.

## Work and play¶

metawards models disease transmission via two main modes;

1. Infections at “work”

These are random infections that individuals acquire in the electoral ward in which they “work”, based on random interactions with others who are also in that “work” ward. In this nomenclature, “work” means any regular (daily), predictable travel that an individual makes.

1. Infections at “play”

These are random infections that individuals acquire in the electoral ward in which they live, based on random interactions with others who are also in their ward. In this nomenclature, “play” means random, unpredictable interactions that are not regular.

Warning

Do not read too much into the definitions of “work” and “play”. They have very broad meanings in metawards, and, in essence, capture the difference between predicatable daily travel and interactions (commuting, colleagues in an office, students in a school), and the random interations (partying, shopping, playing in the park). Most of the hard data science behind metawards is constructing the information in MetaWardsData to gain this very broad overview of how individuals move and interact.

## What is a normal day?¶

metawards uses an iterator to iterate the model outbreak forward day by day. All of the iterators are in the metawards.iterators module. The default iterator is iterate_default.

An iterator applies a sequence of functions that advance the disease step by step through each day. These advance_functions control exactly what happens in each electoral ward on each day.

The iterate_default() iterator applies the following advance_functions in sequence;

1. advance_additional() is applied to add any additional seeds of the disease in the ward, which leads to new infections. These additional seeds represent, e.g. new sources of infection arriving in the ward via outside travel.

2. advance_foi() is applied to advance the calculation of the force of infection (foi) for each ward. This must be called at the beginning of the day after advance_additional(), as the foi parameters are used to guide the path of the outbreak in each ward for the rest of the day.

3. advance_recovery() is applied to all individuals in a ward who are infected. This will see whether an individual progresses from one stage of the disease to the next. This decision is based on the progress disease parameter for the stage that the individual is at.

4. advance_infprob() is applied to recalculate the infection probabilities needed to guide new infections. These are based on the foi parameters for each ward and the number of individuals who are at each stage of the disease (based on the contrib_foi disease parameter).

5. advance_fixed() is applied to advance all infections that are based on fixed (predictable) movements of individuals (the so-called “work” mode). Infected individuals continue to “work” unless they become too symptomatic (too_ill_to_move, based on the parameter for the stage of the disease at which the individual is at).

6. Finally advance_play() is applied to advance all infections that are based on random movements of individuals (the so-called “play” mode). Infected individuals continue to “play” unless they become too symptomatic (again controlled by the too_ill_to_move disease parameters).

Once all of these functions have been applied, then a day is considered complete. The statistics for the day, e.g. numbers of individuals who are in the S, E, I, IW, and R states are collected and printed, and then the next day begins and all of these functions are applied again.