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2 Commits

Author SHA1 Message Date
aaron
2c9b70e075 merged 2021-11-30 14:03:11 +01:00
aaron
154b193da9 update numbers 2021-11-30 14:01:33 +01:00

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@@ -16,10 +16,10 @@ def p_meet_positive_bayes(groupsize:int):
'''
Bayesian approach
'''
n_positive_tests = 824.11 # number of positive tests in the last 14 days per 100k
n_tests = 6514.83 # number of tests in the last 14 days per 100k
n_population = 100000 # sample size (per 100k)
p_test_positive = 0.5 # probability of a positive person getting tested (0.5 is optimistic...)
n_positive_tests = 1863.6 # number of positive tests in the last 14 days
n_tests = 7366.1 # total number of tests in the last 14 days
n_population = 100000 # sample size -> per 100k for switzerland
p_test_positive = 0.3 # probability of a positive person getting tested (0.5 is optimistic...)
p_positive_test = n_positive_tests / n_tests
p_test = n_tests / n_population
@@ -29,14 +29,14 @@ def p_meet_positive_bayes(groupsize:int):
def plot_data(data):
plt.plot(data)
plt.title("probability of having a covid positive in groupsize of n")
plt.title("likelihood of having a covid positive in groupsize of n")
plt.xlabel("group size / n")
plt.ylabel("probability / %")
plt.grid(visible=True)
plt.grid(linestyle='--', linewidth='0.1')
plt.show()
data = []
for i in range(1, 325):
for i in range(1, 70):
data.append(p_meet_positive_bayes(i))
print(f'The chance of meeting a positive in a group of {i} is: {p_meet_positive_bayes(i)}%')