# Responding to changing conditions¶

We’ve now created a lock-down advance function, but are currently triggering this function in an iterator based on a fixed number of days since the outbreak started.

A better approach would be to trigger the lock-down based on the number of individuals who are detected as infected in the model.

To do this, edit your lockdown.py script and copy in the following;

from metawards.iterators import iterate_working_week, \
from metawards.utils import Console

Console.debug("We are on lockdown")

def iterate_lockdown(population, **kwargs):
if not hasattr(population, "lockdown_state"):
population.lockdown_state = "before"
population.is_locked_down = False

if population.lockdown_state == "before":
if population.total > 5000:
population.lockdown_state = "lockdown"
population.lockdown_started = population.day
population.is_locked_down = True

if population.is_locked_down:
else:
return iterate_working_week(population=population,
**kwargs)


The first thing we do here is see if the population has a lockdown_state variable using the standard Python hasattr function. This variable won’t exist on the first call to to iterate_lockdown, and so here we set the lockdown_state to before and set the flag population.is_locked_down to False.

Next, we check if the lockdown is in the before state. If it is, then if the total infected population is greater than 5000 we change the lockdown_state to lockdown, save the day the lockdown started to population.lockdown_started, and set the flag population.is_locked_down to True.

Finally, we either return our advance_lockdown advance function, or the standard advance functions for a working week depending on the value of the population.is_locked_down flag.

Run the model and draw the overview graph using;

metawards -d lurgy3 --additional ExtraSeedsLondon.dat --iterator lockdown
metawards-plot -i output/results.csv.bz2 --format jpg --dpi 150


You should now see that the lockdown takes effect some time after the infected population grows above 5000. This tips the curve and reduces the spread of the disease. You can see what my graphs looked like here;

## Releasing lockdown¶

We can use the data in population to decide when to release the lockdown as well. For example, we could release when the size of the infected population drops below 2000. To do this, edit your lockdown.py file to read;

from metawards.iterators import iterate_working_week, \
from metawards.utils import Console

Console.debug("We are on lockdown")

def iterate_lockdown(population, **kwargs):
if not hasattr(population, "lockdown_state"):
population.lockdown_state = "before"
population.is_locked_down = False

if population.lockdown_state == "before":
if population.total > 5000:
population.lockdown_state = "lockdown"
population.lockdown_started = population.day
population.is_locked_down = True

elif population.lockdown_state == "lockdown":
if population.total < 2000:
population.lockdown_state = "after"
population.lockdown_ended = population.day
population.is_locked_down = False

if population.is_locked_down:
else:
return iterate_working_week(population=population,
**kwargs)


Run the model as before and see what happens…

To start, the lockdown has worked and the number of infections has fallen, with the number falling below 2000 on day 78. However, releasing the lockdown completely leads to a rapid growth in the infection, with over 85,000 infected three weeks after lockdown ended. This is unsurprising, as there was still a lot of infected individuals remaining once lockdown ended, and a large population that was still susceptible to infection (as you can see from the print of my run and the overview graph below).

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 76 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
We are on lockdown                        lockdown.py:9
S: 56055907  E: 201  I: 2192  R: 23777  IW: 215  POPULATION: 56082077
Number of infections: 2393

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 77 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
We are on lockdown                        lockdown.py:9
S: 56055697  E: 223  I: 2144  R: 24013  IW: 202  POPULATION: 56082077
Number of infections: 2367

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 78 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
We are on lockdown                        lockdown.py:9
S: 56055515  E: 210  I: 2109  R: 24243  IW: 174  POPULATION: 56082077
Number of infections: 2319

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 79 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
[15:41:25]                        We are on lockdown                        lockdown.py:9
S: 56055304  E: 182  I: 2099  R: 24492  IW: 205  POPULATION: 56082077
Number of infections: 2281

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 80 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
We are on lockdown                        lockdown.py:9
S: 56055121  E: 211  I: 2055  R: 24690  IW: 175  POPULATION: 56082077
Number of infections: 2266

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 81 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
We are on lockdown                        lockdown.py:9
S: 56054939  E: 183  I: 2004  R: 24951  IW: 176  POPULATION: 56082077
Number of infections: 2187

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 82 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
We are on lockdown                        lockdown.py:9
S: 56054770  E: 182  I: 1964  R: 25161  IW: 166  POPULATION: 56082077
Number of infections: 2146

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 83 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
S: 56053695  E: 169  I: 1923  R: 26290  IW: 916  POPULATION: 56082077
Number of infections: 2092

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 84 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
S: 56052615  E: 1075  I: 1879  R: 26508  IW: 920  POPULATION: 56082077
Number of infections: 2954

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 85 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
S: 56051552  E: 1080  I: 2730  R: 26715  IW: 908  POPULATION: 56082077
Number of infections: 3810

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 86 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
S: 56050161  E: 1063  I: 3598  R: 27255  IW: 1187  POPULATION: 56082077
Number of infections: 4661

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 87 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
S: 56049050  E: 1391  I: 4452  R: 27184  IW: 966  POPULATION: 56082077
Number of infections: 5843

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 88 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
S: 56047600  E: 1111  I: 5589  R: 27777  IW: 1236  POPULATION: 56082077
Number of infections: 6700

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 89 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
S: 56044865  E: 1450  I: 6352  R: 29410  IW: 2072  POPULATION: 56082077
Number of infections: 7802

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Day 90 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
S: 56041834  E: 2735  I: 7386  R: 30122  IW: 2246  POPULATION: 56082077
Number of infections: 10121


Note how the second wave of infection makes the initial wave almost invisible in this graph. The only visible evidence is the small peak in the number of infected wards (IW) plot.

## Relaxing, not removing lockdown¶

The problem is that we treated lockdown like a binary switch, and immediately went back to normal once it was lifted.

Instead, we need to release the lockdown in stages. To model this, edit your lockdown.py to contain the following.

from metawards.iterators import iterate_working_week, \
from metawards.utils import Console

Console.debug("We are in lockdown",
variables=[population.lockdown_scale_rate])

scale_rate=population.lockdown_scale_rate,
**kwargs)

def iterate_lockdown(population, **kwargs):
try:
population.lockdown_state
except Exception:
population.lockdown_state = "before"
population.is_locked_down = False
population.lockdown_scale_rate = 0.05

if population.lockdown_state == "before":
if population.total > 5000:
population.lockdown_state = "lockdown"
population.lockdown_started = population.day
population.is_locked_down = True

elif population.lockdown_state == "lockdown":
if population.total < 2000:
population.lockdown_state = "relaxed_lockdown"
population.lockdown_ended = population.day
population.lockdown_scale_rate = 0.10
population.is_locked_down = True

elif population.lockdown_state == "relaxed_lockdown":
if population.total < 1000:
population.lockdown_scale_rate = 0.20
else:
population.lockdown_scale_rate = 0.10

if population.is_locked_down:
else:
return iterate_working_week(population=population,
**kwargs)


In this code we have created a new lockdown state that we’ve called relaxed_lockdown. This is entered when the number of infections drops below 2000. In this state controls can be released that correspond to now only halving the infection rate (scale_rate is increased to 0.10 from 0.05 during the strong lockdown). In the relaxed_lockdown state the infected population is always checked. If it is below 1000 then the lockdown can be relaxed even more, with the scale_rate increasing from 0.10 to 0.20. However, if the infected population rises above 1000, then the lockdown is tightened and the scale_rate is lowered again to 0.10.

Have a go at running using this iterator. What do you see? In my case I see the model moving from lockdown (scale_factor==0.05), through relaxed lockdown (scale_factor==0.1) to light lockdown (scale_factor==0.2) during the outbreak, which is brought under control. The overview plots are here;

There is a small second peak as the lockdown is relaxed, but this seems to be under control.

Warning

Remember, we cannot read too much into single model runs as these are very stochastic simulations. We would need to run models many times and average before we could gain real insight.

## Returning to work¶

Because Python is dynamically typed, we can set whatever flags or add whatever data we want to the population object that we need (or indeed to any Python object).

Let’s now add an extra flag that will be used by advance_lockdown to call advance_fixed if the lockdown has been lifted sufficiently for people to return to work. Copy the below into your lockdown.py file;

from metawards.iterators import iterate_working_week, \
from metawards.utils import Console

Console.debug("We are in lockdown",
variables=[population.lockdown_scale_rate,
population.is_work_locked_down])

scale_rate=population.lockdown_scale_rate,
**kwargs)

if not population.is_work_locked_down:

def iterate_lockdown(population, **kwargs):
if not hasattr(population, "lockdown_state"):
population.lockdown_state = "before"
population.is_locked_down = False
population.lockdown_scale_rate = 0.25
population.is_work_locked_down = False

if population.lockdown_state == "before":
if population.total > 5000:
population.lockdown_state = "lockdown"
population.lockdown_started = population.day
population.is_locked_down = True
population.is_work_locked_down = True

elif population.lockdown_state == "lockdown":
if population.total < 2000:
population.lockdown_state = "relaxed_lockdown"
population.lockdown_ended = population.day
population.lockdown_scale_rate = 0.5
population.is_locked_down = True
population.is_work_locked_down = False

elif population.lockdown_state == "relaxed_lockdown":
population.is_work_locked_down = False

if population.total < 1000:
population.lockdown_scale_rate = 0.75
else:
population.lockdown_scale_rate = 0.5

if population.is_locked_down:

This is getting longer, but I hope you can see that all we have added is a population.is_work_locked_down flag, plus some extra code to flip this between True and False. This flag is read by advance_lockdown, which calls advance_fixed if the flag is False. We’ve also added a check to see if the infected population rises above 5000 while in “relaxed lockdown”, and if it does, to re-enter full lockdown.