Merge pull request #4 from garethjns/imperfect_immunity

Update readme, tidy

README.MD

@@ -9,7 +9,8 @@ This package models disease spread through a population consisting of sub commun
 
 The code aims to be as simple and understandable as possible, and can be summarised in two graphs: 
 
-![Example cats vs responsible](https://github.com/garethjns/social-distancing-sim/blob/master/images/joined.gif) 
+![Example cats vs responsible](https://github.com/garethjns/social-distancing-sim/blob/master/images/joined.gif) 
+([Discussion](https://new.reddit.com/r/dataisbeautiful/comments/fov56p/oc_comparing_the_effect_of_social_distancing_on/))
 
 # Set up
 
@@ -119,4 +120,15 @@ In the observed model, a proportion of the population (here ~4%) is tested per
  - For known infected nodes, status is switched to "dead" or "immune" at the end of disease, depending on survival
  - For unknown infected nodes, status is set to "dead" if they die, but left as unknown if they survive - this is based on the assumption we know about all deaths, but don't know about acquired immunity unless we know about infection. 
 
-![Example cats vs responsible](https://github.com/garethjns/social-distancing-sim/blob/master/images/joined_2.gif) 
+![Example testing rate](https://github.com/garethjns/social-distancing-sim/blob/master/images/joined_2.gif) 
+([Discussion](https://new.reddit.com/r/dataisbeautiful/comments/fse6l1/oc_the_importance_of_testing_and_effect_on/))
+
+# Immunity effects
+Version 0.2.0 adds incomplete immunity and decay of immunity. These are part of the disease definition, and allow reinfection after a node has survived infection.
+
+Run example with
+````bash
+python3 -m social-distancing-sim.scripts.compare_two_diseases_immunity
+```` 
+
+![Example testing rate](https://github.com/garethjns/social-distancing-sim/blob/master/images/joined_3.gif)

requirements.txt

@@ -4,3 +4,6 @@ matplotlib
 imageio
 dataclasses
 seaborn
+tqdm
+joblib
+

scripts/compare_testing_rate.py

@@ -5,28 +5,20 @@
 from social_distancing_sim.population.population import Population
 
 if __name__ == "__main__":
+ save = True
+
  disease = Disease(name='COVID-19')
  healthcare = Healthcare()
 
- pop_full_knowledge = Population(name='A herd of cats, reality',
- disease=disease,
- healthcare=healthcare,
- observation_space=ObservationSpace(graph=Graph(community_n=40,
- community_size_mean=16,
- seed=123),
- test_rate=1))
- pop_poor_testing_knowledge = Population(name='A herd of cats, observed',
- disease=disease,
- healthcare=healthcare,
- observation_space=ObservationSpace(graph=Graph(community_n=40,
- community_size_mean=16,
- seed=123),
- test_rate=0.01))
-
- pop_full_knowledge.run(steps=130,
- plot=False)
- pop_full_knowledge.replay(duration=0.1)
+ pop = Population(name='A herd of cats, observed',
+ disease=disease,
+ healthcare=healthcare,
+ observation_space=ObservationSpace(graph=Graph(community_n=40,
+ community_size_mean=16,
+ seed=123),
+ test_rate=0.01))
 
- pop_poor_testing_knowledge.run(steps=130,
- plot=False)
- pop_poor_testing_knowledge.replay(duration=0.1)
+ pop.run(steps=130,
+ plot=False)
+ if save:
+ pop.replay(duration=0.1)

scripts/compare_two_diseases_immunity.py

@@ -0,0 +1,53 @@
+"""Run a single population for 2 years with imperfect immunity and limited test rate."""
+
+from joblib import Parallel, delayed
+
+from social_distancing_sim.disease.disease import Disease
+from social_distancing_sim.population.graph import Graph
+from social_distancing_sim.population.healthcare import Healthcare
+from social_distancing_sim.population.observation_space import ObservationSpace
+from social_distancing_sim.population.population import Population
+
+
+def run_and_replay(pop, *args, **kwargs):
+ pop.run(*args, **kwargs)
+ if save:
+ pop.replay(duration=duration)
+
+
+if __name__ == "__main__":
+ save = True
+ duration = 0.1
+
+ common_args = {'plot_ts_fields_g2': ["Mean immunity (of immune nodes)",
+ "Mean immunity (of all alive nodes)"],
+ 'plot_ts_obs_fields_g2': ["Known mean immunity (of immune nodes)",
+ "Known mean immunity (of all alive nodes)"],
+ 'observation_space': ObservationSpace(graph=Graph(community_n=50,
+ community_size_mean=15,
+ community_p_in=0.08,
+ community_p_out=0.04),
+ test_rate=0.05),
+ 'healthcare': Healthcare(capacity=200)}
+
+ pop_low_immunity = Population(name="Low immunity population",
+ disease=Disease(name='COVID-19',
+ virulence=0.005,
+ recovery_rate=0.99,
+ immunity_mean=0.66,
+ immunity_decay_mean=0.1),
+ **common_args)
+
+ pop_high_immunity = Population(name="High immunity population",
+ disease=Disease(name='COVID-19',
+ virulence=0.005,
+ recovery_rate=0.99,
+ immunity_mean=0.98,
+ immunity_decay_mean=0.01),
+ **common_args)
+
+ Parallel(n_jobs=2,
+ backend='loky')(delayed(run_and_replay)(pop,
+ steps=750,
+ plot=False,
+ save=True) for pop in [pop_low_immunity, pop_high_immunity])

scripts/compare_two_diseases_immunity_small.py

@@ -0,0 +1,58 @@
+"""Run a single population for 2 years with imperfect immunity and limited test rate."""
+
+from joblib import Parallel, delayed
+
+from social_distancing_sim.disease.disease import Disease
+from social_distancing_sim.population.graph import Graph
+from social_distancing_sim.population.healthcare import Healthcare
+from social_distancing_sim.population.observation_space import ObservationSpace
+from social_distancing_sim.population.population import Population
+
+
+def run_and_replay(pop, *args, **kwargs):
+ pop.run(*args, **kwargs)
+ if save:
+ pop.replay(duration=duration)
+
+
+if __name__ == "__main__":
+ save = True
+ duration = 0.1
+
+ common_args = {'plot_ts_fields_g2': ["Mean immunity (of immune nodes)",
+ "Mean immunity (of all alive nodes)"],
+ 'plot_ts_obs_fields_g2': ["Known mean immunity (of immune nodes)",
+ "Known mean immunity (of all alive nodes)"],
+ 'observation_space': ObservationSpace(graph=Graph(community_n=8,
+ community_size_mean=5,
+ community_p_in=0.2,
+ community_p_out=0.1,
+ seed=200),
+ test_rate=0.05,
+ seed=123),
+ 'healthcare': Healthcare(capacity=300),
+ 'seed': 124}
+
+ pop_low_immunity = Population(name="Low immunity population (small)",
+ disease=Disease(name='COVID-19',
+ virulence=0.035,
+ recovery_rate=0.99,
+ immunity_mean=0.66,
+ immunity_decay_mean=0.1,
+ seed=125),
+ **common_args)
+
+ pop_high_immunity = Population(name="High immunity population (small)",
+ disease=Disease(name='COVID-19',
+ virulence=0.035,
+ recovery_rate=0.99,
+ immunity_mean=0.95,
+ immunity_decay_mean=0.02,
+ seed=125),
+ **common_args)
+
+ Parallel(n_jobs=2,
+ backend='loky')(delayed(run_and_replay)(pop,
+ steps=365,
+ plot=False,
+ save=True) for pop in [pop_low_immunity, pop_high_immunity])

scripts/compare_two_pop_testing_rate.py

@@ -1,61 +1,43 @@
+from joblib import Parallel, delayed
+
 from social_distancing_sim.disease.disease import Disease
 from social_distancing_sim.population.graph import Graph
 from social_distancing_sim.population.healthcare import Healthcare
 from social_distancing_sim.population.observation_space import ObservationSpace
 from social_distancing_sim.population.population import Population
 
+
+def run_and_replay(pop, *args, **kwargs):
+ pop.run(*args, **kwargs)
+ if save:
+ pop.replay()
+
+
 if __name__ == "__main__":
+ save = True
  disease = Disease(name='COVID-19')
  healthcare = Healthcare()
 
- pop_close_tested = Population(name='A herd of cats',
- disease=disease,
- healthcare=healthcare,
- observation_space=ObservationSpace(graph=Graph(community_n=40,
- community_size_mean=16,
- seed=123),
- test_rate=1))
-
- pop_close_untested = Population(name='A herd of cats, observed',
- disease=disease,
- healthcare=healthcare,
- observation_space=ObservationSpace(graph=Graph(community_n=40,
- community_size_mean=16,
- seed=123),
- test_rate=0.04))
-
- pop_distanced_tested = Population(name='A socially responsible population',
- disease=disease,
- healthcare=healthcare,
- observation_space=ObservationSpace(graph=Graph(community_n=40,
- community_size_mean=16,
- community_p_in=0.05,
- community_p_out=0.04,
- seed=123),
- test_rate=1))
-
- pop_distanced_untested = Population(name='A socially responsible population, observed',
- disease=disease,
- healthcare=healthcare,
- observation_space=ObservationSpace(graph=Graph(community_n=40,
- community_size_mean=16,
- community_p_in=0.05,
- community_p_out=0.04,
- seed=123),
- test_rate=0.04))
-
- pop_close_tested.run(steps=130,
- plot=False)
- pop_close_tested.replay(duration=0.1)
-
- pop_close_untested.run(steps=130,
- plot=False)
- pop_close_untested.replay(duration=0.1)
-
- pop_distanced_tested.run(steps=130,
- plot=False)
- pop_distanced_tested.replay(duration=0.1)
-
- pop_distanced_untested.run(steps=130,
- plot=False)
- pop_distanced_untested.replay(duration=0.1)
+ pop_close = Population(name='A herd of cats, observed',
+ disease=disease,
+ healthcare=healthcare,
+ observation_space=ObservationSpace(graph=Graph(community_n=40,
+ community_size_mean=16,
+ seed=123),
+ test_rate=0.04))
+
+ pop_distanced = Population(name='A socially responsible population, observed',
+ disease=disease,
+ healthcare=healthcare,
+ observation_space=ObservationSpace(graph=Graph(community_n=40,
+ community_size_mean=16,
+ community_p_in=0.05,
+ community_p_out=0.04,
+ seed=123),
+ test_rate=0.04))
+
+ Parallel(n_jobs=2,
+ backend='loky')(delayed(run_and_replay)(pop,
+ steps=130,
+ plot=False,
+ save=save) for pop in [pop_close, pop_distanced])

scripts/compare_two_populations.py

@@ -1,10 +1,21 @@
+from joblib import Parallel, delayed
+
 from social_distancing_sim.disease.disease import Disease
 from social_distancing_sim.population.graph import Graph
 from social_distancing_sim.population.healthcare import Healthcare
 from social_distancing_sim.population.observation_space import ObservationSpace
 from social_distancing_sim.population.population import Population
 
+
+def run_and_replay(pop, *args, **kwargs):
+ pop.run(*args, **kwargs)
+ if save:
+ pop.replay()
+
+
 if __name__ == "__main__":
+ save = False
+
  disease = Disease(name='COVID-19')
  healthcare = Healthcare()
 
@@ -26,10 +37,8 @@
  seed=123),
  test_rate=1))
 
- pop_distanced.run(steps=130,
- plot=False)
- pop_distanced.replay(duration=0.1)
-
- pop.run(steps=130,
- plot=False)
- pop.replay(duration=0.1)
+ Parallel(n_jobs=2,
+ backend='loky')(delayed(run_and_replay)(pop,
+ steps=300,
+ plot=False,
+ save=save) for pop in [pop, pop_distanced])

scripts/run_single_population.py

@@ -1,3 +1,5 @@
+"""Run a single population with perfrect testing."""
+
 from social_distancing_sim.population.graph import Graph
 from social_distancing_sim.population.healthcare import Healthcare
 from social_distancing_sim.population.observation_space import ObservationSpace
@@ -16,8 +18,8 @@
  observation_space=ObservationSpace(graph=graph,
  test_rate=1))
 
- pop.run(steps=100,
- plot=False,
+ pop.run(steps=50,
+ plot=True,
  save=True)
 
  # Save .gif to './example population/replay.gif'

scripts/run_single_population_long.py

@@ -0,0 +1,31 @@
+"""Run a single population for 2 years with imperfect immunity and limited test rate."""
+from social_distancing_sim.disease.disease import Disease
+from social_distancing_sim.population.graph import Graph
+from social_distancing_sim.population.healthcare import Healthcare
+from social_distancing_sim.population.observation_space import ObservationSpace
+from social_distancing_sim.population.population import Population
+
+if __name__ == "__main__":
+ save = True
+
+ graph = Graph(community_n=50,
+ community_size_mean=15,
+ community_p_in=0.1,
+ community_p_out=0.05)
+
+ pop = Population(name="example population",
+ disease=Disease(name='COVID-19',
+ virulence=0.006,
+ immunity_mean=0.6,
+ immunity_decay_mean=0.15),
+ healthcare=Healthcare(),
+ observation_space=ObservationSpace(graph=graph,
+ test_rate=0.05))
+
+ pop.run(steps=750,
+ plot=True,
+ save=True)
+
+ # Save .gif to './example population/replay.gif'
+ if save:
+ pop.replay()

setup.py

@@ -22,5 +22,5 @@
  "Programming Language :: Python :: 3",
  "Operating System :: OS Independent"],
  python_requires='>=3.6',
- install_requires=["networkx", "numpy", "matplotlib", "imageio", "dataclasses"]
+ install_requires=["networkx", "numpy", "matplotlib", "imageio", "dataclasses", "tqdm", "joblib"]
 )

social_distancing_sim/__init__.py

@@ -1,5 +1,5 @@
 MAJOR = 0
-MINOR = 1
+MINOR = 2
 PATCH = 0
 
 __version__ = ".".join(str(v) for v in [MAJOR, MINOR, PATCH])