From 5ad4750b8c4d1018b51c1287b8a2aa4aea4829e5 Mon Sep 17 00:00:00 2001 From: Paige Miller Date: Tue, 3 Dec 2024 21:27:20 +0000 Subject: [PATCH] cleaning up viz.qmd --- scratch/viz.qmd | 32 +++++++++++++++++++++++++++++--- 1 file changed, 29 insertions(+), 3 deletions(-) diff --git a/scratch/viz.qmd b/scratch/viz.qmd index fbd9e03..c5af195 100644 --- a/scratch/viz.qmd +++ b/scratch/viz.qmd @@ -11,6 +11,32 @@ import matplotlib.pyplot as plt ``` +# Example calculation with 2 groups + +```{python} +import numpy as np +n = np.array([200, 800]) +beta = np.array([[10, 0.1],[.1, 1]]) +n_vax = np.array([100, 0]) +ve = 1.0 + +s_i = n / n.sum() +s_vax = (n - n_vax * ve) / n + +R_vax = beta * s_i * s_vax +e = np.linalg.eig(R_vax) +i = np.argmax(np.abs(e.eigenvalues)) + +value = e.eigenvalues[i] +vector = e.eigenvectors[:, i] +vector /= sum(vector) + +value +vector +``` + +# 4 group model + ```{python} # parameters (core, kids, travelers, general) n = np.array([0.1, 0.45, 0.05, 0.4]) * 1000000 @@ -50,7 +76,7 @@ test2 ```{python} # test that equal pop sizes result in bigger Re n_equal = np.array([0.25, 0.25, 0.25, 0.25]) * 1000000 -test3 = ngm.simulate(n_equal, n_vax_0, K, p_severe, ve) +test3 = ngm.simulate(n_equal, n_vax_0, beta, p_severe, ve) test3 ``` @@ -72,7 +98,7 @@ df_n_vax = pd.DataFrame(n_vax_values, columns=["Core", "Kids", "Travelers", "Gen results = [] for index, row in df_n_vax.iterrows(): n_vax = row.values - result = ngm.simulate(n, n_vax, K, p_severe, ve) + result = ngm.simulate(n, n_vax, beta, p_severe, ve) results.append({ "Total Doses": total_doses_range[index], "Re": result["Re"], @@ -109,7 +135,7 @@ df_n_vax = pd.DataFrame(n_vax_values, columns=["Core", "Kids", "Travelers", "Gen results = [] for index, row in df_n_vax.iterrows(): n_vax = row.values - result = ngm.simulate(n, n_vax, K, p_severe, ve) + result = ngm.simulate(n, n_vax, beta, p_severe, ve) results.append({ "Core Doses": n_vax[0], "Re": result["Re"],