add code

covidrisk.py

@@ -0,0 +1,42 @@
+#!/bin/env python
+
+import matplotlib.pyplot as plt
+
+def p_meet_positive(groupsize:int):
+    '''
+        Naive approach, only works if each individual gets tested.
+    '''
+    active_cases = 777.69 # 7 day incidence rate
+    population_size = 100000 # sample size -> per 100k
+    p_positive = active_cases / population_size
+
+    return (1 - ( 1 - p_positive )**groupsize) * 100
+
+def p_meet_positive_bayes(groupsize:int):
+    '''
+        Bayesian approach
+    '''
+    n_positive_tests = 1073.754     # number of positive tests in the last 14 days
+    n_tests = 6316.2                # total number of tests in the last 14 days
+    n_population = 100000           # sample size -> per 100k for switzerland
+    p_test_positive =  0.2          # 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
+    p_positive = p_positive_test * p_test / p_test_positive
+
+    return (1 - ( 1 - p_positive )**groupsize) * 100
+
+def plot_data(data):
+    plt.plot(data)
+    plt.title("probability of meeting a covid positive in groupsize of n")
+    plt.xlabel("group size / n")
+    plt.ylabel("probability")
+    plt.show()
+
+data = []
+for i in range(1, 300):
+    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)}%')
+
+plot_data(data)