Multiple model runs¶
In the last page you successfully performed a single run modelling an outbreak of the lurgy that started in London. This run (which we call a model run) is stochastic, meaning that the results will be slightly different every time it is performed.
To gain confidence in any predictions, we need to perform a model run multiple times, and average over the results.
Performing multiple model runs¶
metawards has the command line option --repeats (or -r) to
set the number of times a model run should be repeated. For example,
run the below command to repeat the model run four times;
metawards -d lurgy -a ExtraSeedsLondon.dat --repeats 4
metawards will automatically use as many of the cores on your computer
as it can to parallelise the jobs. On my computer, the output shows;
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Running the model ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Using random number seed: 87340504
Running 4 jobs using 4 process(es)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ MULTIPROCESSING ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
I have four processor cores on my laptop, so I see the four repeats run
in parallel using four processes, with each model run performed
using 1 thread. You will see a different distribution of threads
and processes if you have a different number of cores on your computer.
You can set the number of processes that metawards should use via
the --nprocs command line option. You can set the number of threads
that metawards should use via the --nthreads command line option.
This calculation may take some time (2-3 minutes). This time, instead
of seeing a summary of the outbreak, metawards will show a summary
of the different model run jobs. Something similar to this should
be printed;
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ MULTIPROCESSING ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Computing model run ✔
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ Completed job 1 of 4 │
│ (NO_CHANGE)[repeat 1] │
│ 2021-01-13: DAY: 238 S: 11784852 E: 0 I: 0 R: 44297225 IW: 0 UV: 1.0 │
│ TOTAL POPULATION 56082077 │
│ │
└────────────────────────────────────────────────────────────────────────────────────────┘
Computing model run ✔
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ Completed job 2 of 4 │
│ (NO_CHANGE)[repeat 2] │
│ 2021-01-05: DAY: 230 S: 11770162 E: 0 I: 0 R: 44311915 IW: 1 UV: 1.0 │
│ TOTAL POPULATION 56082077 │
│ │
└────────────────────────────────────────────────────────────────────────────────────────┘
Computing model run ✔
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ Completed job 3 of 4 │
│ (NO_CHANGE)[repeat 3] │
│ 2021-02-05: DAY: 261 S: 11789449 E: 0 I: 0 R: 44292628 IW: 1 UV: 1.0 │
│ TOTAL POPULATION 56082077 │
│ │
└────────────────────────────────────────────────────────────────────────────────────────┘
Computing model run ✔
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ Completed job 4 of 4 │
│ (NO_CHANGE)[repeat 4] │
│ 2021-01-04: DAY: 229 S: 11779688 E: 0 I: 0 R: 44302389 IW: 0 UV: 1.0 │
│ TOTAL POPULATION 56082077 │
│ │
└────────────────────────────────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ Writing a summary of all results into the csv file │
│ /Users/chris/GitHub/tutorial/test/output/results.csv.bz2. You can use this to │
│ quickly look at statistics across all runs using e.g. R or pandas │
│ │
└────────────────────────────────────────────────────────────────────────────────────────┘
Note
metawards prints a progress spinner to the screen while the jobs
are running, so show you that it hasn’t crashed. You can switch off
the spinner using the --no-spinner option if it annoys you.
Similarly, metawards by default writes output to the screen using
a very colourful theme. You can change this to a more simple and
less-colourful theme by passing in the option --theme simple.
In this case, all four outbreaks completed within 229-261 days, while the number of the population who progressed to the ‘R’ state were all around 44.3 million.
The results.csv.bz2 file¶
The day-by-day progress of each the outbreak for each model run is
recorded in the output file results.csv.bz2. This is a comma-separated
file that has been compressed using bzip2.
You can read this file easily using
Python Pandas or with
R. You can even import this into Excel
(although you may need to uncompress this file first using bunzip2).
For example, if you have Pandas installed, then you can read this file via an ipython or Jupyter notebook session via;
>>> import pandas as pd
>>> df = pd.read_csv("output/results.csv.bz2")
>>> df
fingerprint repeat day date S E I R IW
0 NO_CHANGE 1 0 2020-04-20 56082077 0 0 0 0
1 NO_CHANGE 1 1 2020-04-21 56082077 0 0 0 0
2 NO_CHANGE 1 2 2020-04-22 56082072 5 0 0 0
3 NO_CHANGE 1 3 2020-04-23 56082072 0 5 0 0
4 NO_CHANGE 1 4 2020-04-24 56082068 0 5 4 4
.. ... ... ... ... ... .. .. ... ..
929 NO_CHANGE 4 224 2020-11-30 11782419 0 4 44299654 0
930 NO_CHANGE 4 225 2020-12-01 11782419 0 3 44299655 0
931 NO_CHANGE 4 226 2020-12-02 11782419 0 1 44299657 0
932 NO_CHANGE 4 227 2020-12-03 11782419 0 1 44299657 0
933 NO_CHANGE 4 228 2020-12-04 11782418 0 0 44299659 1
[934 rows x 9 columns]
Each repeat is given its own number, which is in the repeat column.
The day of the outbreak is given in the day column. This counts up
from day zero when the outbreak started, to the last day when the
outbreak was over. You can control the start day of the outbreak using
the --start-day command line option.
The date column contains the date of each day in the outbreak. By
default, metawards assumes that day zero is today. You can set the
date of day zero using the --start-date command line option, e.g.
--start-date tomorrow would start tomorrow, while
--start-date Jan 1 would start on January 1st this year.
The values of S, E, I, R and IW for each repeat for each day are then given in their correspondingly named columns.
The fingerprint column not used for this calculation - we will see what it is later.