44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
#!/bin/env python
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import matplotlib.pyplot as plt
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def p_meet_positive(groupsize:int):
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'''
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Naive approach, only works if each individual gets tested.
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'''
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active_cases = 777.69 # 7 day incidence rate
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population_size = 100000 # sample size -> per 100k
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p_positive = active_cases / population_size
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return (1 - ( 1 - p_positive )**groupsize) * 100
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def p_meet_positive_bayes(groupsize:int):
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'''
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Bayesian approach
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'''
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n_positive_tests = 824.11 # number of positive tests in the last 14 days per 100k
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n_tests = 6514.83 # number of tests in the last 14 days per 100k
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n_population = 100000 # sample size (per 100k)
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p_test_positive = 0.5 # probability of a positive person getting tested (0.5 is optimistic...)
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p_positive_test = n_positive_tests / n_tests
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p_test = n_tests / n_population
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p_positive = p_positive_test * p_test / p_test_positive
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return (1 - ( 1 - p_positive )**groupsize) * 100
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def plot_data(data):
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plt.plot(data)
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plt.title("probability of having a covid positive in groupsize of n")
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plt.xlabel("group size / n")
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plt.ylabel("probability / %")
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plt.grid(visible=True)
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plt.show()
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data = []
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for i in range(1, 325):
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data.append(p_meet_positive_bayes(i))
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print(f'The chance of meeting a positive in a group of {i} is: {p_meet_positive_bayes(i)}%')
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plot_data(data)
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