Commit 487e0453 authored by Sébastien Picault's avatar Sébastien Picault
Browse files

Updated README and data files

parent 24dbe74c
......@@ -31,7 +31,7 @@ Content of the repository
- `list_of_tiles.txt`: list of non-empty 15x15 km$`^2`$ tiles
- `extract_area.py`: Python script to (re)build 1 specific tile
- `fences.csv`, `buffer.csv`: list of 15x15 km$`^2`$ tiles which represent the fenced area and the surrounding buffer, respectively
- `kick-of_meeting`: presentation slides used for the kick-off meeting (August 2020)
- `kick-of_meeting`: slides used for the kick-off meeting (August 2020)
- `Data_all_players`: initial data and situation reports provided for each challenge period
......@@ -40,8 +40,21 @@ Content of the repository
Data provided to ASF Challenge participants ("players")
-------------------------------------------------------
:warning: COMING SOON...
Directory `Data_all_players` contains the data that was given to the participants at the beginning of the Challenge (day 50 after detection of the first case) and during the subsequent periods (days 80 and 110).
- `initial_data` was provided at day 50 and contained:
- a map of the fictitious island (administrative divisions and landcover, shapefiles: `Island_ADMIN` and `Island_LANDCOVER`)
- a list of all officially registered pig farms (`herds.csv`) with their characteristics (this list was built by removing from the original input file in `model/data` 10% of small herds, and re-numbering the farms)
- the wild boar hunting bags by administrative division of the island (`WB_HundintBag.csv`)
- the list of trade movements between farms from 59 days before the detection of the first case up to day 50 (`moves.csv`)
- a description of data structure (`DataDoc_Player.pdf`)
- `DAY_50`, `DAY_80`, `DAY_110` all have the same structure and contain reports at the specified date (after the detection of the first case):
- `DAY<N>.pdf`, the situation report and proposed control measures or specific questions to the participants
- `herds_day_<N>.csv`, an updated list of pig farms (possibly with unregistered pig farms discovered since last situation report)
- `moves_Players_day_<N>.csv`, an updated list of trade movements
- `TimeSeries_day_<N>.csv`, the updated time series representing epidemiological events (detections, cullings, etc.) and their nature (pig farm vs. wild boar, detection method, etc.). From day 80, this time series was also provided with subsets (in the fenced area, in the buffer around the fences, and outside).
- `TimeSeries_hunt_negative_test_day_<N>.csv`, a specific file for wild boar that tested negative after being shot by hunters (large file)
- additional figures (cumulative incidence, maps) to illustrate the evolution of the situation, possibly with small errors.
Running model M0
......@@ -49,7 +62,7 @@ Running model M0
1. Install EMULSION
The installation procedure for EMULSION is fully described on [the software website](https://sourcesup.renater.fr/www/emulsion-public/Install.html). All simulations were carried out under Linux. Install version 1.1rc5 which was used to produce the synthetic data used for the ASF Challenge and discussed in our papers. Ensure that your `PYTHONPATH` variable is set properly (i.e. containing current repository `.`).
The installation procedure for EMULSION is fully described on [the software website](https://sourcesup.renater.fr/www/emulsion-public/Install.html). All simulations were carried out under Linux with Python 3.8 and `numpy` 1.18. **Install EMULSION version 1.1rc5** which was used to produce the synthetic data used for the ASF Challenge and discussed in the article. Ensure that your `PYTHONPATH` variable is set properly (i.e. containing current repository `.`) as explained in the installation instructions.
2. Clone the git repository and move into the `model/` directory:
......@@ -71,5 +84,19 @@ Running model M0
4. Run simulations
The parameter values defined by default in the model file (`ppa.yaml`) already correspond to the initial conditions and to the control measures implemented in the trajectory selected for the Challenge.
Hence, to reproduce this trajectory, use the following command (1 stochatic repetition with the specified random seed):
:warning: COMING SOON...
```bash
# assuming that you are currently located in directory 'model':
emulsion run ppa.yaml -r 1 --seed 23289
```
This will produce 3 files in directory `outputs/`:
- `log.txt` which contains each change occurring in epidemiological units and was used to produce the time series given to the Challenge participants
- `herd_status.txt` which tracks pig farms in protection/surveillance areas or traced as suspicious trade contacts
- `wildboars.txt` which contains statistics on wild boar per 15x15 km$`^2`$ tile
These files should be the same as those are already provided in directory `outputs/challenge`.
......@@ -591,7 +591,7 @@ parameters:
# CONTROL PARAMETERS
game_duration:
desc: 'duration (days) of the time series provided to players. Used to stop simulation when enough data to provide to players.'
value: 180
value: 300
delay_implementation:
desc: 'duration (days) between the decision to perform a preventive culling and the actual culling. The total delay between the cause of culling (e.g. setup of the protection area) and the actual culling is thus delay_confirmation + delay_implementation (used in Python)'
value: 3
......@@ -612,10 +612,10 @@ parameters:
value: 2000
cull_near_infected_carcasses:
desc: 'boolean to indicate whether or not to implement a preventive culling of pig herds within a given radius from detected infected wildboar carcasses, after a delay'
value: 0
value: 1
delay_cull_near_infected_carcasses:
desc: 'delay (days from first detection) after which the preventive culling of pig herds near infected wildboar carcasses is implemented'
value: 120
value: 90
radius_cull_near_infected_carcasses:
desc: 'radius (m) around infected wildboar carcasses to implement preventive culling of pig herds'
value: 3000
......@@ -689,7 +689,7 @@ parameters:
value: 0
delay_change_seed:
desc: 'delay (days) after detection, after which we change the seed'
value: 60
value: 110
second_seed:
desc: 'value of the new seed'
value: 0
......
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