From 78dffdc8b1f0de2839f3956c5c60b91dfda273ff Mon Sep 17 00:00:00 2001
From: twallema <tijs.alleman@ugent.be>
Date: Tue, 12 May 2020 10:52:27 +0200
Subject: [PATCH] Modification to ideal network parameters in hpc scripts and
 demo

---
 hpc/calibrate_stochastic.py      | 44 ++++++++-------
 hpc/sim_stochastic.py            | 34 ++++++-----
 src/SEIRSNetworkModel_demo.ipynb | 97 +++++++++++++++++---------------
 3 files changed, 97 insertions(+), 78 deletions(-)

diff --git a/hpc/calibrate_stochastic.py b/hpc/calibrate_stochastic.py
index 7fc9ccfda..5dfc20394 100755
--- a/hpc/calibrate_stochastic.py
+++ b/hpc/calibrate_stochastic.py
@@ -24,18 +24,18 @@
 
 # Construct the network G
 # ~~~~~~~~~~~~~~~~~~~~~~~
-numNodes = 30000
-baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=7)
+numNodes = 90000
+baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=3)
 # Baseline normal interactions:
-G_norm     = models.custom_exponential_graph(baseGraph, scale=200)
+G_norm     = models.custom_exponential_graph(baseGraph, scale=500)
 models.plot_degree_distn(G_norm, max_degree=40)
 
 # Construct the network G under social distancing
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-numNodes = 30000
-baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=2)
+numNodes = 90000
+baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=1)
 # Baseline normal interactions:
-G_dist     = models.custom_exponential_graph(baseGraph, scale=20000)
+G_dist     = models.custom_exponential_graph(baseGraph, scale=200000)
 models.plot_degree_distn(G_dist, max_degree=40)
 
 # Define model
@@ -43,29 +43,31 @@
 model = models.SEIRSNetworkModel(
                                  # network connectivty
                                  G = G_norm,
-                                 p       = 0.6,
+                                 p       = 0.51,
                                  # clinical parameters
-                                 beta    = 0.05, 
-                                 sigma   = 5.2,
+                                 beta    = 0.20, 
+                                 sigma   = 4.0,
+                                 omega   = 1.5,
                                  zeta    = 0,
                                  a = 0.43, # probability of an asymptotic (supermild) infection
                                  m = 1-0.43, # probability of a mild infection
                                  h = 0.20, # probability of hospitalisation for a mild infection
                                  c = 2/3, # probability of hospitalisation in cohort
                                  mi = 1/6, # probability of hospitalisation in midcare
-                                 da = 14, # days of infection when asymptomatic (supermild)
-                                 dm = 14, # days of infection when mild
+                                 da = 6.5, # days of infection when asymptomatic (supermild)
+                                 dm = 6.5, # days of infection when mild
                                  dc = 7,
                                  dmi = 14,
                                  dICU = 14,
                                  dICUrec = 6,
                                  dmirec = 6,
-                                 dhospital = 3, # days before reaching the hospital when heavy or critical
+                                 dhospital = 5, # days before reaching the hospital when heavy or critical
                                  m0 = 0.49, # mortality in ICU
                                  maxICU = 2000,
                                  # testing
                                  theta_S = 0,
                                  theta_E = 0,
+                                 theta_I = 0,
                                  theta_A = 0,
                                  theta_M = 0,
                                  theta_R = 0,
@@ -75,11 +77,13 @@
                                  # back-tracking
                                  phi_S   = 0,
                                  phi_E   = 0,
+                                 phi_I   = 0,
                                  phi_A   = 0,
                                  phi_R   = 0,
                                  # initial condition
                                  initN = 11.43e6, #results are extrapolated to entire population
-                                 initE = 100,
+                                 initE = 0,
+                                 initI = 3,
                                  initA = 0, 
                                  initM = 0,
                                  initC = 0,
@@ -89,13 +93,15 @@
                                  initD = 0,
                                  initSQ = 0,
                                  initEQ = 0,
+                                 initIQ = 0,
                                  initAQ = 0,
                                  initMQ = 0,
                                  initRQ = 0,
                                  # monte-carlo sampling
-                                 monteCarlo = True,
+                                 monteCarlo = False,
                                  repeats = 1
                             )
+
 # Load data
 # ~~~~~~~~~~~~
 #[index,data] = model.obtainData()
@@ -111,19 +117,19 @@
 # vector with dates
 index=pd.date_range('2020-03-13', freq='D', periods=ICUvect.size)
 # data series used to calibrate model must be given to function 'plotFit' as a list
-idx = -26
+idx = -23
 index = index[0:idx]
 data=[np.transpose(ICUvect[:,0:idx]),np.transpose(hospital[:,0:idx])]
 # set optimisation settings
-parNames = ['beta','p'] # must be a list!
+parNames = ['beta'] # must be a list!
 positions = [np.array([6]),np.array([4,5,6])] # must be a list!
-bounds=[(10,100),(0.1,0.5),(0.4,0.8)] # must be a list!
+bounds=[(10,100),(0.25,0.60)] # must be a list!
 weights = np.array([0,1])
 # run optimisation
-theta = model.fit(data,parNames,positions,bounds,weights,setvar=True,maxiter=10,popsize=5)
+theta = model.fit(data,parNames,positions,bounds,weights,setvar=True,maxiter=15,popsize=multiprocessing.cpu_count())
 stop = timeit.default_timer()
 print('Required time: ', stop - start,' seconds')
 
 # Make a graphical representation of results
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-model.plotFit(index,data,positions,modelClr=['red','orange'],legendText=('ICU (model)','ICU (data)','Hospital (model)','Hospital (data)'),titleText='Belgium',filename-'calibration.svg')
+model.plotFit(index,data,positions,modelClr=['red','orange'],legendText=('ICU (model)','ICU (data)','Hospital (model)','Hospital (data)'),titleText='Belgium',filename-'calibration90K.svg')
diff --git a/hpc/sim_stochastic.py b/hpc/sim_stochastic.py
index 6b3238a38..94d88dc99 100755
--- a/hpc/sim_stochastic.py
+++ b/hpc/sim_stochastic.py
@@ -24,18 +24,18 @@
 
 # Construct the network G
 # ~~~~~~~~~~~~~~~~~~~~~~~
-numNodes = 60000
-baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=7)
+numNodes = 90000
+baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=3)
 # Baseline normal interactions:
-G_norm     = models.custom_exponential_graph(baseGraph, scale=200)
+G_norm     = models.custom_exponential_graph(baseGraph, scale=500)
 models.plot_degree_distn(G_norm, max_degree=40)
 
 # Construct the network G under social distancing
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-numNodes = 60000
-baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=2)
+numNodes = 90000
+baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=1)
 # Baseline normal interactions:
-G_dist     = models.custom_exponential_graph(baseGraph, scale=20000)
+G_dist     = models.custom_exponential_graph(baseGraph, scale=200000)
 models.plot_degree_distn(G_dist, max_degree=40)
 
 # Define model
@@ -43,24 +43,25 @@
 model = models.SEIRSNetworkModel(
                                  # network connectivty
                                  G = G_norm,
-                                 p       = 0.6,
+                                 p       = 0.51,
                                  # clinical parameters
-                                 beta    = 0.05, 
-                                 sigma   = 5.2,
+                                 beta    = 0.03, 
+                                 sigma   = 4.0,
+                                 omega   = 1.5,
                                  zeta    = 0,
                                  a = 0.43, # probability of an asymptotic (supermild) infection
                                  m = 1-0.43, # probability of a mild infection
                                  h = 0.20, # probability of hospitalisation for a mild infection
                                  c = 2/3, # probability of hospitalisation in cohort
                                  mi = 1/6, # probability of hospitalisation in midcare
-                                 da = 14, # days of infection when asymptomatic (supermild)
-                                 dm = 14, # days of infection when mild
+                                 da = 6.5, # days of infection when asymptomatic (supermild)
+                                 dm = 6.5, # days of infection when mild
                                  dc = 7,
                                  dmi = 14,
                                  dICU = 14,
                                  dICUrec = 6,
                                  dmirec = 6,
-                                 dhospital = 3, # days before reaching the hospital when heavy or critical
+                                 dhospital = 5, # days before reaching the hospital when heavy or critical
                                  m0 = 0.49, # mortality in ICU
                                  maxICU = 2000,
                                  # testing
@@ -75,11 +76,13 @@
                                  # back-tracking
                                  phi_S   = 0,
                                  phi_E   = 0,
+                                 phi_I   = 0,
                                  phi_A   = 0,
                                  phi_R   = 0,
                                  # initial condition
                                  initN = 11.43e6, #results are extrapolated to entire population
-                                 initE = 100,
+                                 initE = 0,
+                                 initI = 1,
                                  initA = 0, 
                                  initM = 0,
                                  initC = 0,
@@ -89,12 +92,13 @@
                                  initD = 0,
                                  initSQ = 0,
                                  initEQ = 0,
+                                 initIQ = 0,
                                  initAQ = 0,
                                  initMQ = 0,
                                  initRQ = 0,
                                  # monte-carlo sampling
-                                 monteCarlo = True,
-                                 repeats = 10
+                                 monteCarlo = False,
+                                 repeats = 3
                             )
 
 # Create checkpoints dictionary
diff --git a/src/SEIRSNetworkModel_demo.ipynb b/src/SEIRSNetworkModel_demo.ipynb
index cf349c03b..0ed0f320b 100644
--- a/src/SEIRSNetworkModel_demo.ipynb
+++ b/src/SEIRSNetworkModel_demo.ipynb
@@ -155,12 +155,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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AmDFjyMzMNGtXRESkHhq9D2bNmjVERkYSHx9PSUkJAA6Hg4CAANc8/v7+OByOOtudTidt27alWbNmAAQEBOBwOBp3R0RE5KIaNWDGjRvH1q1bSU1NxWazsWjRIgAMw7hgXovFUmd7bepqB0hKSsJut2O323E6nfWsXkRELkejBkyHDh2wWq14eXkRExPDoUOHgOozkIKCAtd8DocDm81WZ7uvry+nTp2ioqICgIKCAmw2W53bjY2NJTk5meTkZHx9fU3aOxER+alGDZjCwkLX9xkZGXTr1g2AsLAw0tLSKCsrIzc3l5ycHHr27EmPHj3IyckhNzeXsrIy0tLSCAsLw2KxMGDAALZs2QLAhg0bCAsLa8xdERERN5qZteK4uDiys7NxOp2EhoYybdo0srOzOXr0KACBgYEkJCQA0K1bNx544AFGjBiB1Wplzpw5WK1WAObMmcOkSZOorKxk7NixrlB69tlnmTlzJkuWLOHOO+8kJibGrF0REZF6sBi1dXRcw+x2O8nJyaZuI7f4DOv35tU5PbpvJ4La32hqDSIiDak+vzv1JL+IiJhCASMiIqZQwIiIiCkUMCIiYgoFjIiImEIBIyIiplDAiIiIKRQwIiJiCgWMiIiYQgEjIiKmUMCIiIgpFDAiImIKBYyIiJhCASMiIqZQwIiIiCkUMCIiYgoFjIiImEIBIyIipjAtYOLj4wkJCWHUqFGutldeeYXhw4cTGRnJ1KlTOXXqFAB5eXn07NmT0aNHM3r0aObMmeNa5vDhw0RGRhIeHs5LL73E+Tc8nzx5kgkTJhAREcGECRMoKSkxa1dERKQeLitgqqqqKC0tvaR57XY7q1evrtE2cOBANm3axMaNG+nSpQurVq1yTbv55ptJTU0lNTWVhIQEV/vcuXNJSEggPT2dnJwcsrKyAEhMTCQkJIT09HRCQkJITEy8nF0RERGTuQ2Yp59+mtLSUs6cOcOIESMYPnz4BcFRm379+tGuXbsabYMGDaJZs2YA9O7dm4KCgouuo7CwkNLSUvr06YPFYiEqKorMzEwAMjMziYqKAiAqKoqMjAy3NYmISONxGzDHjh3D29ubjIwMBg8ezLZt20hNTb3iDb/33nuEhoa6fs7LyyMqKopHHnmEzz//HACHw0FAQIBrnoCAABwOBwBFRUXYbDYAbDYbxcXFdW4rKSkJu92O3W7H6XRece0iIuJeM3czVFRUUF5eTkZGBo888gjNmzfHYrFc0UZXrFiB1WrlF7/4BVAdENu2bcPX15fDhw8zdepU0tLSXP0tP1WfbcfGxhIbGwtUX7oTERHzuT2DiY2NJSwsjB9//JF+/fqRn5+Pt7d3vTe4YcMGPv74YxYvXuwKixYtWuDr6wvAXXfdxc0338zx48cJCAiocRmtoKDAddbi5+dHYWEhUH0prX379vWuSUREGp7bgHnsscfYsWMHb775JhaLhcDAQP7yl7/Ua2NZWVm8+eabrFixglatWrnai4uLqaysBCA3N5ecnByCgoKw2Wy0bt2aAwcOYBgGKSkpDBkyBICwsDBSUlIAarSLiEjT4PYS2ffff8/rr79OYWEhq1ev5tixY+zfv5+YmJiLLhcXF0d2djZOp5PQ0FCmTZtGYmIiZWVlTJgwAYBevXqRkJDAnj17eOONN7BarVitVubNm4ePjw9QfRdZfHw8Z8+eJTQ01NVvM3nyZGbMmMH69evp2LEjS5cuvdJjISIiDchi1NbR8ROTJk3CbrezcuVK3n//fSoqKhgzZgwbN25srBoblN1uJzk52dRt5BafYf3evDqnR/ftRFD7G02tQUSkIdXnd6fbS2ROp5MRI0bg5VU9a7NmzVzfi4iI1MVtUtx44404nU5Xh/yBAwdo06aN6YWJiMjVzW0fzKxZs5gyZQonTpzgwQcfxOl0qr9DRETcchsw3bt3569//SvHjx/HMAxuueUWmjdv3hi1iYjIVazOgElPT6+1PScnB4CIiAhTChIRkWtDnQGzbds2oHpIlv3793PvvfcCsHv3bvr376+AERGRi6ozYF5++WUAfvWrX5GWluZ6gr6wsLDGaMciIiK1cXsXWX5+vitcADp06OC6TCYiIlIXt538/fv3Z+LEiYwcORKLxUJaWhoDBgxojNpEROQq5jZg5syZw9atW9mzZw9QPfhleHi46YWJiMjVzW3AAPTp0wer1YrFYqFnz55m1yQiItcAt30wH3zwATExMWzZsoXNmzcTExPDhx9+2Bi1iYjIVcztGczKlStZv349fn5+QPXQ+uPHj2f48OGmFyciIlcvt2cwhmG4wgXAx8en1jdNioiI/JTbM5hBgwa57iKD6ktm59/JIiIiUhe3AfPb3/6WLVu2sG/fPgzD0F1kIiJySS7pLrJhw4YxbNgws2sREZFriNs+mPT0dCIiIujbty933303ffr04e67776klcfHxxMSEsKoUaNcbSdPnmTChAlEREQwYcIESkpKgOq+npdeeonw8HAiIyP58ssvXcts2LCBiIgIIiIi2LBhg6v98OHDREZGEh4ezksvvaS+IRGRJsRtwLz22musWLGCvXv3sm/fPvbv38++ffsuaeV2u53Vq1fXaEtMTCQkJIT09HRCQkJITEwEICsri5ycHNLT05k/fz5z584FqgNp+fLlrFu3jnfffZfly5e7Qmnu3LkkJCSQnp5OTk4OWVlZl7PvIiJiIrcB4+fnx6233lqvlffr14927drVaMvMzCQqKgqAqKgoMjIyarRbLBZ69+7NqVOnKCwsZOfOnQwcOBAfHx/atWvHwIED2bFjB4WFhZSWltKnTx8sFgtRUVFkZmbWq04REWl4bvtg7rrrLmbMmMHQoUNp0aKFq72+w/UXFRW5Bs+02WwUFxcD4HA4CAgIcM0XEBCAw+G4oN3f37/W9vPzi4hI0+A2YE6fPk2rVq345JNParQ39Ptgaus/sVgsl91em6SkJJKSkgBwOp1XWKmIiFwKtwFz/r0wDcXPz4/CwkJsNhuFhYW0b98eqD4DKSgocM1XUFCAzWYjICCA7OxsV7vD4aB///51zl+b2NhYYmNjgep+IRERMZ/bPpiGFhYWRkpKCgApKSkMGTKkRrthGBw4cIA2bdpgs9kYNGgQO3fupKSkhJKSEnbu3MmgQYOw2Wy0bt2aAwcOYBhGjXWJiIjnXdJzMPUVFxdHdnY2TqeT0NBQpk2bxuTJk5kxYwbr16+nY8eOLF26FIDBgwezfft2wsPDadWqFQsXLgSqh6Z58skniY6OBmDq1Kn4+PgA1XeRxcfHc/bsWUJDQzXCgIhIE2IxrrOHR+x2O8nJyaZuI7f4DOv35tU5PbpvJ4La32hqDSIiDak+vzvdnsGcOnWKlJQU8vPzqaysdLX/7ne/u/wKBYAqwyC3+MxF5/G+oRm+rVtcdB4RkabMbcBMnjyZXr16cdttt+Hl1ehdNteks+WVfHCo4KLzRPftpIARkaua24A5d+4c8fHxjVGLiIhcQ9yekowePZp169ZRWFjIyZMnXV8iIiIX4/YMpnnz5rz66qusXLnS1WaxWDQsi4iIXJTbgHn77bdJT093PRApIiJyKdxeIuvatSutWrVqjFpEROQa4vYMxmq1EhUVxYABA2oMdqnblEVE5GLcBszQoUMZOnRoY9QiIiLXELcBM2bMmMaoQ0RErjFuAyYsLKzWYfB1F5mIiFyM24B57733XN+XlZWxefNm1yuLRURE6uL2LjJfX1/Xl7+/P+PHj2fXrl2NUZuIiFzF3J7BfPnll67vq6qqOHz4MKdPnza1KBERufq5DZhFixb9a+ZmzQgMDGTJkiWmFiUiIlc/twHzzjvvNEYdIiJyjXEbMGVlZWzZsoX8/HwqKipc7U899ZSphYmIyNXNbcBMmTKFNm3a0L179xpP8ouIiFyM24BxOBz86U9/arANfvPNN8ycOdP1c25uLtOnT+eHH35g3bp1rkE14+LiGDx4MACrVq1i/fr1eHl58bvf/Y77778fgKysLBYsWEBVVRUxMTFMnjy5weoUEZEr4zZg+vTpw1dffcXtt9/eIBsMDg4mNTUVgMrKSkJDQwkPDyc5OZnx48czceLEGvMfO3aMtLQ00tLScDgcTJgwgS1btgCQkJDA22+/jb+/P9HR0YSFhdG1a9cGqVNERK6M24DZu3cvGzZsIDAwsMYlso0bN17xxj/77DOCgoIIDAysc57MzExGjhxJixYtCAoKonPnzhw8eBCAzp07ExQUBMDIkSPJzMxUwIiINBFuA+bNN980beNpaWmMGjXK9fOaNWtISUnhrrvuYtasWbRr1w6Hw0GvXr1c8/j7++NwOAAICAio0X4+eERExPPcPskfGBhY69eVKisr46OPPmL48OEAjBs3jq1bt5KamorNZnM9f2MYxgXLWiyWOttrk5SUhN1ux26343Q6r7h2ERFxz23AmCUrK4vu3bvToUMHADp06IDVasXLy4uYmBgOHToEVJ+lFBQUuJZzOBzYbLY622sTGxtLcnIyycnJ+Pr6mrhXIiJynscCJi0tjZEjR7p+LiwsdH2fkZFBt27dgOrRnNPS0igrKyM3N5ecnBx69uxJjx49yMnJITc3l7KyMtLS0ggLC2v0/RARkdq57YMxw48//sinn35KQkKCq+21117j6NGjQPVlufPTunXrxgMPPMCIESOwWq3MmTMHq9UKwJw5c5g0aRKVlZWMHTvWFUoiIuJ5HgmYVq1asXv37hptr732Wp3zT5kyhSlTplzQPnjwYNezMiIi0rR47BKZiIhc2xQwIiJiCgWMiIiYQgEjIiKmUMCIiIgpFDAiImIKBYyIiJhCASMiIqZQwIiIiCkUMCIiYgoFjIiImEIBIyIiplDAiIiIKRQwIiJiCgWMiIiYQgEjIiKm8MgLx8S9KsMgt/hMndO9b2iGb+sWjViRiMjlUcA0UWfLK/ngUEGd06P7dlLAiEiT5rGACQsLo3Xr1nh5eWG1WklOTubkyZPMnDmT/Px8AgMDWbJkCe3atcMwDBYsWMD27dtp2bIlixYtonv37gBs2LCBFStWANWvVh4zZoyndklERH7Co30wf/7zn0lNTSU5ORmAxMREQkJCSE9PJyQkhMTERACysrLIyckhPT2d+fPnM3fuXABOnjzJ8uXLWbduHe+++y7Lly+npKTEU7sjIiI/0aQ6+TMzM4mKigIgKiqKjIyMGu0Wi4XevXtz6tQpCgsL2blzJwMHDsTHx4d27doxcOBAduzY4cldEBGR/8+jfTATJ07EYrEQGxtLbGwsRUVF2Gw2AGw2G8XFxQA4HA4CAgJcywUEBOBwOC5o9/f3x+FwXLCdpKQkkpKSAHA6nWbukoiI/H8eC5i1a9fi7+9PUVEREyZMIDg4uM55DcO4oM1isdTZ/nPnAwzAbrdfQdUiInKpPHaJzN/fHwA/Pz/Cw8M5ePAgfn5+FBYWAlBYWEj79u2B6jOWgoJ/3VFVUFCAzWa7oN3hcLjOgERExLM8EjBnzpyhtLTU9f0nn3xCt27dCAsLIyUlBYCUlBSGDBkC4Go3DIMDBw7Qpk0bbDYbgwYNYufOnZSUlFBSUsLOnTsZNGiQJ3ZJRER+xiOXyIqKipg6dSoAlZWVjBo1itDQUHr06MGMGTNYv349HTt2ZOnSpQAMHjyY7du3Ex4eTqtWrVi4cCEAPj4+PPnkk0RHRwMwdepUfHx8PLFLIiLyMx4JmKCgIN5///0L2n19ffnzn/98QbvFYuHFF1+sdV3R0dGugBERkaajSd2mLCIi1w4FjIiImEIBIyIiplDAiIiIKRQwIiJiCgWMiIiYQgEjIiKmUMCIiIgpFDAiImIKBYyIiJjCo++DkfqrMgxyi8/UOd37hmb4tm7RiBWJiNSkgLlKnS2v5INDBXVOj+7bSQEjIh6lS2QiImIKBYyIiJhCASMiIqZQwIiIiCkUMCIiYopGD5jvvvuORx99lAceeICRI0e63mC5bNky7r//fkaPHs3o0aPZvn27a5lVq1YRHh7OsGHD2LFjh6s9KyuLYcOGER4eTmJiYmPvioiIXESj36ZstVqZNWsW3bt3p7S0lLFjxzJw4EAAxo8fz8SJE2vMf+zYMdLS0khLS8PhcDBxg/S6AAAM7ElEQVRhwgS2bNkCQEJCAm+//Tb+/v5ER0cTFhZG165dG3uXRESkFo0eMDabDZvNBoC3tzfBwcE4HI4658/MzGTkyJG0aNGCoKAgOnfuzMGDBwHo3LkzQUFBAIwcOZLMzEwFjIhIE+HRPpi8vDyOHDlCr169AFizZg2RkZHEx8dTUlICgMPhICAgwLWMv78/DoejznYREWkaPBYwp0+fZvr06cyePRtvb2/GjRvH1q1bSU1NxWazsWjRIgAMw7hgWYvFUmd7bZKSkrDb7djtdpxOZ8PuiIiI1MojAVNeXs706dOJjIwkIiICgA4dOmC1WvHy8iImJoZDhw4BEBAQQEHBv4ZEcTgc2Gy2OttrExsbS3JyMsnJyfj6+pq4ZyIicl6jB4xhGDz//PMEBwczYcIEV3thYaHr+4yMDLp16wZAWFgYaWlplJWVkZubS05ODj179qRHjx7k5OSQm5tLWVkZaWlphIWFNfbuNFnnB8O82JfzdJmnyxSRa1ijd/Lv3buX1NRUbrvtNkaPHg1AXFwcmzZt4ujRowAEBgaSkJAAQLdu3XjggQcYMWIEVquVOXPmYLVaAZgzZw6TJk2isrKSsWPHukJJ3A+GCRoQU0TM1egBc8899/DVV19d0D548OA6l5kyZQpTpkypdZmLLSciIp6jJ/lFRMQUChgRETGFAkZEREyhgBEREVPolcnXsfO3MtfF+4ZmustMROpNAVMPztNllJ6rqHN6WUVVI1ZTf+5uZdZtzCJyJRQw9VB6roL1e/PqnD6iR0Cd00RErhfqgxEREVMoYERExBQKGBERMYX6YKRO7u4yA91pJiJ1U8BInTRgpohcCV0iExERU+gMRq6IHtYUkbooYOSK6GFNEamLAkZMpTMckeuXAkZM5e4Mx3534EWH3QGFkMjVSgEjHqU71USuXVd9wGRlZbFgwQKqqqqIiYlh8uTJni5JGpi7y2zNvCxUVBl1TtcZkIhnXNUBU1lZSUJCAm+//Tb+/v5ER0cTFhZG165dPV2aNCB3ZzkjegRc8WU4hZRIw7uqA+bgwYN07tyZoKAgAEaOHElmZuYVBYy7ofjh6hmOX6pdymW4Kw0pdwF1KfMoxORac1UHjMPhICDgX0Pj+/v7c/DgwYsuU1l18cstZRVVvP/FPy+6Dg3Hf/250rOoS5mnIc60rnT61bKNq6XO6/2Phqs6YAzjwn9Yi8VyQVtSUhJJSUkA/O+xf/CbSY9c0XZ3mDwd4H2nE19fX1O3cb3U6a7GhthGQ+zHpdTZFDhVZ4O6Wuo8fvz45S9kXMX27dtnPPHEE66fV65caaxcufKiy4wZM8bsshqE6mw4V0ONhqE6G5rqbFj1qfOqHousR48e5OTkkJubS1lZGWlpaYSFhXm6LBER4Sq/RNasWTPmzJnDpEmTqKysZOzYsXTr1s3TZYmICGCdO3fuXE8XcSW6dOnCo48+ymOPPUa/fv0uaZm77rrL5KoahupsOFdDjaA6G5rqbFiXW6fFMGrpKRcREblCV3UfjIiINF1XdR/M5bhahpQJCwujdevWeHl5YbVaSU5O9nRJAMTHx/Pxxx/j5+fHpk2bADh58iQzZ84kPz+fwMBAlixZQrt27ZpcncuWLWPdunW0b98egLi4OAYPHuzJMvnuu+947rnn+P777/Hy8uKXv/wljz/+eJM6pnXV2NSO57lz53j44YcpKyujsrKSYcOGMX36dHJzc4mLi6OkpIR///d/59VXX6VFC889k1JXnbNmzSI7O5s2bdoAsGjRIu68806P1Xne+X5tf39/Vq1aVb/j2eD3sjVBFRUVxpAhQ4wTJ04Y586dMyIjI41//OMfni6rVv/5n/9pFBUVebqMC2RnZxuHDx82Ro4c6Wp75ZVXjFWrVhmGYRirVq0yXn31VU+V51JbnW+88YaxevVqD1Z1IYfDYRw+fNgwDMP44YcfjIiICOMf//hHkzqmddXY1I5nVVWVUVpaahiGYZSVlRnR0dHG/v37jenTpxubNm0yDMMwXnjhBWPNmjWeLLPOOn/7298amzdv9mhttXnrrbeMuLg4Y/LkyYZhGPU6ntfFJbKfDinTokUL15Aycun69et3wV/SmZmZREVFARAVFUVGRoYnSquhtjqbIpvNRvfu3QHw9vYmODgYh8PRpI5pXTU2NRaLhdatWwNQUVFBRUUFFouFXbt2MWzYMADGjBnj8c98XXU2RQUFBXz88cdER0cD1Q+11+d4XhcBU9uQMk3xg3LexIkTsdvtrtEHmqqioiJsNhtQ/cuouLjYwxXVbc2aNURGRhIfH09JSYmny6khLy+PI0eO0KtXryZ7TH9aIzS941lZWcno0aO57777uO+++wgKCqJt27Y0a1bdCxAQENAkPvM/r/P88fz9739PZGQkCxcupKyszMNVwsKFC3n22Wfx8qqOCKfTWa/jeV0EjHGJQ8o0BWvXrmXDhg28+eabrFmzhj179ni6pKveuHHj2Lp1K6mpqdhsNhYtWuTpklxOnz7N9OnTmT17Nt7e3p4up1Y/r7EpHk+r1Upqairbt2/n4MGDfPPNNxfM0xQ+8z+v8+uvvyYuLo4PP/yQ9957j5KSEhITEz1a47Zt22jfvr3bW5Iv5XheFwETEBBAQcG/Bhl0OByuvxKbGn9/fwD8/PwIDw93O3inJ/n5+VFYWAhAYWGhq9O3qenQoQNWqxUvLy9iYmI4dOiQp0sCoLy8nOnTpxMZGUlERATQ9I5pbTU21eMJ0LZtWwYMGMCBAwc4deoUFRXVg4cWFBQ0qc/8+Tp37NiBzWbDYrHQokUL7Ha7x4/nvn37+OijjwgLCyMuLo5du3axYMGCeh3P6yJgrpYhZc6cOUNpaanr+08++aRJj0wQFhZGSkoKACkpKQwZMsTDFdXu/C9sgIyMjCZxTA3D4Pnnnyc4OJgJEya42pvSMa2rxqZ2PIuLizl16hQAZ8+e5dNPP+XWW29lwIABbNmyBYANGzZ4/DNfW53BwcGu42kYRpM4nk8//TRZWVl89NFHvP7669x7773813/9V72O53XzoOX27dtZuHCh69a7KVOmeLqkC+Tm5jJ16lSg+lrtqFGjmkydcXFxZGdn43Q68fPzY9q0aQwdOpQZM2bw3Xff0bFjR5YuXYqPj0+TqzM7O5ujR48CEBgYSEJCgsf/mv388895+OGHue2221zXuePi4ujZs2eTOaZ11bhp06YmdTyPHj3KrFmzqKysxDAMhg8fzlNPPUVubi4zZ86kpKSEO++8k8WLF3v0NuW66nzsscdwOp0YhsEdd9zBvHnzXDcDeNru3bt56623XLcpX+7xvG4CRkREGtd1cYlMREQanwJGRERMoYARERFTKGBERMQUChgRETGFAkbEBMuWLeNPf/qTp8sQ8SgFjEgTVVlZ6ekSRK7IdfM+GBGzrVixgpSUFDp27Ej79u3p3r07J06cYN68eTidTlq2bMn8+fO59dZbOXHiBM888wyVlZWEhoby3//93+zfv5/du3ezfPlybDYbR44c4YMPPiA1NZV33nmH8vJyevXqxYsvvojVamXnzp0sW7aMsrIygoKCePnll5vMA3oiwPXxPhgRsx06dMgYNWqUcebMGeOHH34whg4daqxevdp47LHHjOPHjxuGYRgHDhwwHn30UcMwDGPy5MnGxo0bDcMwjL/97W9G7969DcMwjF27dhm9evUyTpw4YRiGYRw7dsz41a9+ZZSVlRmGYRgvvviisWHDBqOoqMh46KGHjNOnTxuGUf3umGXLljXmLou4pTMYkQbw+eefM3ToUFq1agVUjyl27tw59u/fz29+8xvXfOeHYj9w4AB/+MMfAIiMjOTVV191zdOjRw+CgoIA+Oyzzzh8+LDrvRxnz57Fz8+PL774gmPHjjFu3DigelDK3r17m7+jIpdBASPSQH4+fHlVVRVt27YlNTX1stZz4403ur43DIMxY8bw9NNP15jno48+YuDAgbz++uv1L1jEZOrkF2kA/fr1Y+vWrZw9e5bS0lK2bdtGq1at6NSpE5s3bwaqw+L8IJG9evUiPT0dgLS0tDrXGxISwpYtWygqKgLg5MmT5Ofn07t3b/bt28e3334LwI8//sjx48fN3EWRy6YzGJEG0L17d0aMGMHo0aMJDAykb9++ALz22mvMnTuXFStWUFFRwYgRI7jjjjuYPXs2zz77LG+99Rb/8R//UefLxrp27cqMGTN44oknqKqqonnz5syZM4fevXvz8ssvExcX57rsNmPGDG655ZZG22cRdzSasogH/Pjjj7Rs2RKLxUJaWhqbNm1ixYoVni5LpEHpDEbEA7788ksSEhIwDIO2bduycOFCT5ck0uB0BiMiIqZQJ7+IiJhCASMiIqZQwIiIiCkUMCIiYgoFjIiImEIBIyIipvh/dBEXHX/iwhIAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -170,7 +170,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -181,14 +181,14 @@
    ],
    "source": [
     "# Construct the network G\n",
-    "numNodes = 10000\n",
+    "numNodes = 50000\n",
     "baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=3)\n",
     "# Baseline normal interactions:\n",
     "G_norm     = models.custom_exponential_graph(baseGraph, scale=500)\n",
     "models.plot_degree_distn(G_norm, max_degree=40)\n",
     "\n",
     "# Construct the network G under social distancing\n",
-    "numNodes = 10000\n",
+    "numNodes = 50000\n",
     "baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=1)\n",
     "# Baseline normal interactions:\n",
     "G_dist     = models.custom_exponential_graph(baseGraph, scale=200000)\n",
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,8 +242,8 @@
     "                                 phi_R   = 0,\n",
     "                                 # initial condition\n",
     "                                 initN = 11.43e6, #results are extrapolated to entire population\n",
-    "                                 initE = 1,\n",
-    "                                 initI = 0,\n",
+    "                                 initE = 0,\n",
+    "                                 initI = 3,\n",
     "                                 initA = 0, \n",
     "                                 initM = 0,\n",
     "                                 initC = 0,\n",
@@ -258,8 +258,8 @@
     "                                 initMQ = 0,\n",
     "                                 initRQ = 0,\n",
     "                                 # monte-carlo sampling\n",
-    "                                 monteCarlo = True,\n",
-    "                                 repeats = 3\n",
+    "                                 monteCarlo = False,\n",
+    "                                 repeats = 1\n",
     "                            )"
    ]
   },
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
@@ -301,11 +301,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.beta = 0.30"
+    "model.beta = 0.50"
    ]
   },
   {
@@ -319,38 +319,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "t = 10.94\n",
-      "t = 20.01\n",
+      "t = 0.09\n",
+      "t = 10.09\n",
+      "t = 20.03\n",
       "t = 30.01\n",
       "t = 40.00\n",
-      "t = 50.02\n",
-      "t = 60.10\n",
-      "t = 70.24\n",
-      "t = 80.11\n",
-      "t = 90.04\n",
-      "t = 100.04\n",
-      "t = 0.30\n",
-      "t = 20.01\n",
-      "t = 30.24\n",
-      "t = 40.07\n",
-      "t = 50.02\n",
-      "t = 60.22\n",
-      "t = 70.19\n",
-      "t = 80.00\n",
-      "t = 90.02\n",
-      "t = 100.08\n"
+      "t = 50.01\n",
+      "t = 60.00\n"
      ]
     }
    ],
    "source": [
-    "y = model.sim(100)"
+    "y = model.sim(60)"
    ]
   },
   {
@@ -386,12 +373,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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Sk5Ph4+ODwsJCFBcXw9/fH927d8dTTz0F4M4tNCdPngzgzo05fHx8sG3bNkyfPh379u3DrVu36rQv7hZDm4iIrMLb2xuvvfYa/vGPf0Cr1UKlUiEiIgK//vorNm3aBKVSie+++w6bN2+Gr68v2rdvj61btyIqKgpt27bFhAkTYG9vj5iYGMyePVs+EW3ChAm4efMmpk+fjpKSEgBAZGQkAGD+/PlYvHgxgoKCIIoievfujcWLF+P//u//EB4ejpCQEPj4+OChhx6yZtdUi7fmpBqxz8zD/jIP+8s8DfnWnFeuXMGrr76Kb7/91tql1AnempOIiMjGMbSJiKjea9WqVYPZy64JQ5uIiMhGMLSJiIhsBEObiIjIRjC0iYiIbARDm4iIrCIvLw+ff/65yXZXrlzBtm3b7qrdyJEjq8zPysrCrFmzTK6/Y8cOjBgxAhMnTjTZtrItW7YgKyvLrHWqq7cmDG0iIrKKvLw8bNq0yWS7jIyM+zpz3MPDA++++67JdomJiVi4cCHi4+PNfo6tW7fi+vXr91KeWXhFNCIisorY2Fj89ddfCA4ORr9+/TBv3jwsW7YMP/zwAwRBwLRp0/D0008jNjYW58+fR3BwMEJCQjBkyBDMmzcPt2/fBgAsWLAAPXv2rPZ59C/MsmXLFuzZswe3b9/G5cuX5W2tXr0aP//8MxYuXIiAgADMmTMHK1aswJEjR1BaWoq///3vePbZZwEAH3/8Mb755hsIgoABAwaga9euSEtLw5w5c9CoUSMkJCTg3LlzWLp0KYqKitCsWTPExMTA3d0daWlp+Pe//w0HB4caa64OQ5uIiJCzIxbZSYugLa69G4YoGjmhxehFcBth/Jrm4eHh+OOPP5CcnAwA2LVrF86ePYvk5GTk5uZi7Nix6N27N8LDw7F+/Xr5GuS3b9/Ghg0bYG9vj0uXLiEsLMysK2Wmp6cjKSkJarUaw4cPx8SJEzFjxgwcPnwY8+bNQ7du3ZCQkIAmTZpg8+bNKC0txbPPPgs/Pz9cuHABqamp+Oqrr+Dg4ICbN2/CxcUFn3/+ubxuWVkZ3nzzTXzwwQdwdXXF9u3bsXLlSsTExCAyMhILFizAE088gbffftvsPmVoExERcnbG1mpgA4C2uAA5O2OrDe3Kjh8/jsDAQCiVSjRv3hw+Pj749ddf5bt86Wg0GixevBhnz56FQqHApUuXzKqrb9++aNKkCQCgffv2yMjIQMuWLQ3aHDhwAL/99ht27doFAMjPz8eff/6JgwcPIjQ0FA4ODgAAFxeXKtu/ePEifv/9d0yaNKm8H7RatGjRAvn5+cjPz8cTTzwBAAgODsYPP/xgVu0MbSIigtvwcIvsabsNv/s7h93trTA+/fRTNG/eHMnJydBqtXjsscfMqkutVsvjSqUSoigareX111/Hk08+aTBfd+i+JpIkoUOHDkhISDCYn5eXZ3JdUxjaREQEtxHhd71HXFscHR1RWFgoT/v4+CAhIQEhISG4desWjh07hnnz5iErK8ugXX5+Pjw9PaFQKLB161ajoXu/+vfvj02bNqFPnz5QqVS4ePEiPDw84Ofnhw8++AAjR440ODyu/1ratWuHGzduyLcRLSsrw6VLl9ChQwc4OTnh2LFj6N27912dEV8ZQ5uIiKyiWbNm6NmzJ0aOHIknn3wS8+bNw4kTJxAcHAxBEDB37ly0aNECLi4uUCqVGDVqFEJDQ/Hcc89h5syZ2LlzJ3x9fdG4ceNar23cuHHIyMhAaGgoJElCs2bN8MEHH2DAgAE4e/YsxowZA5VKBX9/f4SFhSEkJAQLFy6UT0R799138eabbyI/Px+iKOLFF19Ehw4dEBMTI5+I1r9/f7Pr4q05qUbsM/Owv8zD/jJPQ741Z0PDW3MSERHZOIY2ERGRjWBoExER2QiGNhERkY1gaBMREdkIhjYREZGNYGgTEZHV6G7CQXeHoU1ERFbz5ZdfWrsEm8IrohERkdX06NEDJ06cwOHDh/Hee+/Bzc0NZ8+exdChQ9GxY0fExcWhpKQE77//Ph5++GFERETA3t4eFy5cwNWrVxETE4OtW7fi5MmT6N69O5YuXWqwXQDYuXMn9u7di6VLlyIiIgJOTk5IS0tDdnY25s6di+HDhwMA1q1bhx07dqC0tBRDhw7FrFmzrNYv1WFoExERYtP2YtGJ3SjQlNTaNp3s7LGox1CEdx14V+3Pnj2L7du3w8XFBYMHD8a4ceOQmJiIzz77DPHx8Zg/fz6A8htvxMXFITU1Fa+++io2bdqEDh06YOzYsXd1lbfr16/jiy++wIULFzBt2jQMHz4cP/74I/78808kJiZCkiRMmzYNR48ehY+Pz/12Q63i4XEiIkJs2v5aDWwAKNCUIDZt/12379atG9zd3aFWq/Hwww/Dz88PANCxY0dkZGTI7QYNGgRBENCpUyc0b94cnTp1gkKhgLe3t0G76gwZMkRu/7///Q9A+a04Dxw4gNGjRyMkJAQXLlww+5afdYF72kREhPCuAyyypx3edcBdt9e/ZaZCoZCnFQqFwZ28dPMFQaiyjkajqbLdkhLD16S/jo4kSZgyZUq9PzGOoU1ERAjvOvCuD2PbgubNm+P8+fNo164dvv/+ezg6OtbYvn///li1ahWCgoLg6OiIrKws2NnZwc3NrY4qvjsMbSIieuCEh4dj6tSpaNmyJTp06ICioqIa2/fv3x/nz5+X97QbN26M5cuX17vQ5q05qUbsM/Owv8zD/jIPb83ZcNzzrTmXLVuGgoIClJWV4cUXX4Svry+Sk5MtUiQRERFVz2RoHzhwAE5OTti7dy88PT2xa9cufPLJJ3VRGxEREekxGdq6M/H27duHwMBAuLi43PXG9+/fj2HDhmHo0KFYu3ZtleVXr17FxIkTMXr0aAQFBWHfvn1mlE5ERPfLxr4hbRBq+jcxGdqDBg3C8OHDkZaWhr59++LGjRuwt7c3+aSiKGLx4sVYt24dUlJS8O233+LcuXMGbdasWYMRI0YgKSkJK1euRHR09F28HCIiqg2NGjVCTk4Og7sekSQJOTk5aNSokdHlJs8enzVrFl555RU4OTlBqVSiUaNGWLNmjcknPnXqFNq0aYPWrVsDAAIDA5Gamgpvb2+5jSAIKCgoAADk5+fD3d39rl4UERHdv1atWuHKlSvIzs62dimkp1GjRmjVqpXRZSZDe/z48di6das83bhxY/z97383mGdMVlYWPD095WkPDw+cOnXKoM2MGTPw8ssvY+PGjbh9+zY2bNhgqhwiIqolKpUK7dq1s3YZZIZqQzs7OxtZWVkoLi7GmTNn5MMnBQUFuH37tskNGzvcIgiCwXRKSgpCQkLwj3/8AydOnMC8efPw7bffQqEwPGqfkJCAhIQEua709HTTr8yI4uLie163oWKfmYf9ZR72l3nup7/4064HQ7Wh/eOPP2LLli3IzMxETEyMPN/R0RFhYWEmN+zp6YnMzEx5Oisrq8rh78TERKxbtw5A+R1ZSkpKkJubW+XH7OPHj8f48eMBlP9O+17ffPxNovnYZ+Zhf5mH/WUe9hdVG9ohISEICQnBrl27MGzYMLM33K1bN1y6dAmXL1+Gh4cHUlJSEBsba9CmZcuWOHjwIEJDQ3H+/HmUlJTA1dXV/FdBRETUAJj8TnvQoEHYtm0bMjIyDC7EPmPGjJo3bGeHqKgoTJ48GaIoYsyYMejQoQNWrVqFrl27YvDgwYiIiMDrr7+OTz/9FIIgYOnSpVUOoRMREVE5k6E9bdo0NGnSBF26dDF6Z5Sa+Pv7w9/f32De7Nmz5XFvb298+eWXZm2TiIiooTIZ2llZWbwCGhERUT1g8uIqPXr0wG+//VYXtRAREVENTO5pHz9+HFu3boWXl5fB4fFt27ZZtDAiIiIyZDK0P/7447qog4iIiEwweXjcy8sL165dw6FDh+Dl5QUHBwdotdq6qI2IiIj0mAzt1atXY926dfJdusrKyjB37lyLF0ZERESGTIb27t27sWbNGjg4OAAov4Z4YWGhxQsjIiIiQyZDW6VSQRAE+aInRUVFFi+KiIiIqjJ5ItqIESMQFRWFvLw8fPXVV9i8eTOeeeaZuqiNiIiI9JgM7ZdffhkHDhyAo6MjLl68iFmzZsHPz68uaiMiIiI9JkMbAPz8/BjUREREVlZtaE+YMAGbNm1Cjx49DG7iIUkSBEHAzz//XCcFEhERUblqQ3vTpk0AgBMnTtRZMURERFQ9k2ePG/tNNn+nTUREVPdMhva5c+cMpjUaDU6fPm2xgoiIiMi4ag+Pf/TRR/jwww9RUlKCnj17Aij/PlutVvMnX0RERFZQbWhPnToVU6dORWxsLMLDw+uyJiIiIjLC5E++wsPDcevWLfz5558oKSmR5/v4+Fi0MCIiIjJkMrS//vprxMXFITMzE48++ih++eUXPP7444iLi6uL+oiIiKiCyRPR4uLikJiYiIceegjx8fHYunUrXF1d66I2IiIi0mMytNVqNezt7QEApaWlaN++PS5evGjxwoiIiMiQycPjnp6eyMvLw5AhQzBp0iQ4OzvD3d29LmojIiIiPSZD+/333wcAzJw5E76+vsjPz8eTTz5p8cKIiIjI0F3dMOT06dM4fvw4BEFAz549oVarLV0XERERVWLyO+3Vq1cjIiICN2/eRG5uLiIjI/HBBx/URW1ERESkx+SedkpKCpKSkuST0aZMmYKQkBBMnz7d4sURERHRHSb3tL28vAwuqlJaWoqHH37YokURERFRVSb3tNVqNQIDA+Hn5wdBEHDgwAH06tULb775JgDg9ddft3iRREREdBehPXToUAwdOlSefuKJJyxaEBERERlnMrRDQkLqog4iIiIyweR32kRERFQ/MLSJiIhsRLWhPXfuXADAZ599VmfFEBERUfWqDe3Tp08jIyMDmzdvxq1bt3Dz5k2DgYiIiOpWtSeiPfvss5g8eTIuX76M0NBQSJIkLxMEAampqXVSIBEREZWrNrRfeOEFvPDCC1i4cCGio6PrsiYiIiIywuRPvqKjo3H27FkcO3YMANC7d288+uijFi+MiIiIDJk8ezwuLg5z5sxBTk4OcnJyMHfuXMTHx9dFbURERKTH5J72119/ja+++gqNGzcGALzyyisYP348Jk6caPHiiIiI6I67+p22Uqk0Ok5ERER1x+SedmhoKMaNGydff/z777/HmDFjLF4YERERGTIZ2pMmTcITTzyB48ePQ5IkxMTE4G9/+1td1EZERER6TIY2AHTp0gVdunSxdC1ERERUA157nIiIyEYwtImIiGxEjaEtiiJeeumlOiqFiIiIalJjaCuVSjRq1Aj5+fl1VQ8RERFVw+SJaPb29ggKCkK/fv3kC6wAwOuvv27RwoiIiMiQydAeOHAgBg4cWAelEBERUU1MhnZISAiKi4tx9epVPPLII3VRExERERlh8uzxPXv2IDg4GJMnTwYApKen49VXX7V4YURERGTIZGivXr0aiYmJcHZ2BgB07twZGRkZFi+MiIiIDJkMbaVSiSZNmtRFLURERFQDk99pd+jQAdu2bYMoirh06RLi4+PRo0ePuqiNiIiI9Jjc016wYAHOnTsHtVqNsLAwODk5Yf78+XVRGxEREekxuaft4OCAf/7zn3jllVcAAE5OThYvioiIiKoyGdqnTp3C/PnzUVhYCKA8tN966y107drV4sURERHRHSZDe/78+Vi4cCF69+4NADh27BgiIyOxbds2ixdHREREd5j8TtvR0VEObADo3bs3HB0dLVoUERERVVXtnvbp06cBAI899hiioqIQGBgIQRCwfft2+Pr61lmBREREVK7a0F66dKnB9OrVq+VxQRAsVxEREREZVW1ox8fH3/fG9+/fjyVLlkCr1WLcuHGYMmWK0XY7d+7E7NmzkZiYiG7dut338xIRET2ITJ6IlpeXh6SkJGRkZEAURXm+qVtziqKIxYsXY8OGDfDw8MDYsWMREBAAb29vg3YFBQWIj49H9+7d7/ElEBERNQwmT0SbMmUKMjIy0LFjR3Tp0kUeTDl16hTatGmD1q1bQ61WIzAwEKmpqVXarVq1CpMnT4a9vf29vQIiIqIGwuSedklJCSIjI83ecFZWFjw9PeVpDw8F27mCAAARiklEQVQPnDp1yqDNmTNnkJmZiUGDBmH9+vXVbishIQEJCQkAgOzsbKSnp5tdDwAUFxff87oNFfvMPOwv87C/zHM//dW5c+daroaswWRoBwcH46uvvsLAgQOhVqvl+S4uLjWuJ0lSlXn6J7BptVrExMQgJibGZJHjx4/H+PHjAQChoaH3/OZLT0/nG9dM7DPzsL/Mw/4yD/uLTIa2SqXCsmXL8OGHH8rzBEEweqhbn6enJzIzM+XprKwsuLu7y9OFhYX4/fff8cILLwAo34OeNm0a1qxZw5PRiIiIjDAZ2hs2bMB3330HV1dXszbcrVs3XLp0CZcvX4aHhwdSUlIQGxsrL2/SpAkOHz4sT0+cOBHz5s1jYBMREVXDZGh7e3vDwcHB/A3b2SEqKgqTJ0+GKIoYM2YMOnTogFWrVqFr164YPHjwPRVMRETUUJkMbaVSidGjR8PX19fgO21TP/kCAH9/f/j7+xvMmz17ttG2tfG7cCIiogeZydAeMmQIhgwZUhe1EBERUQ1MhnZISEhd1EFEREQmmAztgIAAo9caN3X2OBEREdUuk6G9efNmeby0tBQ7duzArVu3LFoUERERVWXyMqbNmjWTBw8PD7z00ks4dOhQXdRGREREekzuaevuqw2UX8UsLS0NhYWFFi2KiIiIqjIZ2vr31bazs4OXlxfeeecdixZFREREVZkMbf5+moiIqH4wGdqlpaXYtWsXMjIyoNFo5PkzZsywaGFERERkyGRoT5s2DU2aNEGXLl0MrohGREREdctkaGdlZeGTTz6pi1qIiIioBiZ/8tWjRw/89ttvdVELERER1cDknvbx48exdetWeHl5GRwe37Ztm0ULIyIiIkMmQ/vjjz+uizqIiIjIBJOh7eXlVRd1EBERkQkmv9MmIiKi+oGhTUREZCMY2kRERDaCoU1ERGQjGNpEREQ2gqFNRERkIxjaRERENoKhTUREZCMY2kRERDaCoU1ERGQjGNpEREQ2gqFNRERkIxjaRERENoKhTUREZCMY2kRERDaCoU1ERGQjGNpEREQ2gqFNRERkIxjaRERENoKhTUREZCMY2kRERDaCoU1ERGQjGNpEREQ2gqFNRERkIxjaRERENoKhTUREZCMY2kRERDaCoU1ERGQjGNpEREQ2gqFNRERkIxjaRERENoKhTUREZCMY2kRERDaCoU1ERGQjGNpEREQ2gqFNRERkIxjaRERENoKhTUREZCMY2kRERDaCoU1ERGQjGNpEREQ2gqFNRERkIxjaRERENsLO2gUQEVH1JElCqVZEqVZj7VKoHmBoExGhPBy1kgSNpIVGK0IjaVGm1aJUq0GZVtQb16JU1KBUK6Kk4lEXqqWiiDJ5WkSxWIYSUfeoQbGowW2xDLfFsvJxTfl4iahBiVaDElGUx29rylCkKUORWAqtJAEAfJp44qdOHWGnUFq5t8haLBra+/fvx5IlS6DVajFu3DhMmTLFYHlpaSnmzZuH06dPw8XFBStXrkSrVq0sWRKR1UmSBAkSJAnQQqqYBrSSBK2khRZSxXj5IAEV7e+0kyralG9Da9Bef31R0hqdr5uWqpmvlarfpm5arKaNKGkharXQSFqIkhYarRai3MZwnazsbDjn/wFRkqCpCEZRXk+EqNXfhghRWzFIYkU7qaKNpLee1iB4NVptpb4ob68BIEoSyiQJmorp+u5ofiZO37iG7s35d7Khslhoi6KIxYsXY8OGDfDw8MDYsWMREBAAb29vuc3XX38NZ2dn7N69GykpKVixYgXeeecdi9Rz8PCnWHfkG5TtUUIy2VqqOipUnSVVXlB5XXmWWc9Y7UzJyHjleQbzJUAS9ObJr0Uw0l4oD4SK1yMJArQAtFoJQqoACYbrSBAAoby98boEI/Oq1ioZ9J9kUEvl5RLKX4/+9iW99gCgLX+B0Ar62xGqtJOn9dppBd1yAdrqtiEY1qZbr7wfKtb5SW+dyu2Fyu8XwjVrF1D/2WlFOGjLMDT7DzySeQhoPtbaJZGVWCy0T506hTZt2qB169YAgMDAQKSmphqE9p49ezBjxgwAwLBhw7B48WJIkgShlv+wlZYUYvTJQ7ju9EitbpeIHiwKSQulpIWdJEFZMa7SirCTtFBJWthJWthpRaglESptxVDRRiWJUGm15Y8V7ey1Gqi1Iuy1ItRaDdSSiEaiBvZa/aG8nUorQl2xbXXFMoeKtiqp/OOosoUjHB/9xsq9RNZksdDOysqCp6enPO3h4YFTp05VadOyZcvyQuzs0KRJE+Tm5sLV1dWgXUJCAhISEgAA2dnZSE9PN6sWrSjCSSzF9Xt5IUQWopDKj2sIFYdAlJIWioppRcV+vCCVH1PQtSsfl6CQpIpHAChvr5QkeV2FwTbLpxWQAAlQQgtB0t9OeS2Kiu0JFc9deXt32sFgvgJaKCSU1yzpatFCWRF8Ct2jwXbKjz2o5JDUf9RbD7r1y59HWbEN3faUklQetLr5qAhWVIyjvJ0dAEGB8nYAFApAVVGnnSBBWdFeIQgVR6KE8hUE3aNS71EJKPQGpRJQ2AFKNaBSQVCqISlVgEIFQdkIUKogKVSAUgVBYVfeVqHbjgBJUAJKe0CphmBnD0lpD9ipAGVjSCpHwM4BkrIxoHJCEZzx2x8X7un91rlz53t/s1K9YbHQlowcEq68B303bQBg/PjxGD9+PAAgNDT0nt58B1z/ifXbl0NQqyqOaFe3N195viDPkpdIJjZhsKB83PjBA70NS1W3J8jPLdxZJFRaDsM+E4DyPzx3lkIQBLmNYLCuVP43qeI1KfSWK4TyP66FBYVwcmpSHhZ65er+8OoOUguCQl5453mrr1Whq0tup1tRIY8KUFQ8r0LuQ0GvL8rrluTXd2d7ApQV21DotdVfrqtFIVQ86geiIEDXg4qKeYLePP12CqGiTcXz/S87Gx4eHlBAcWc7Fc8rt1Xo/9Ky4isBSdeHwp2O1gWH/OLLX5Hhe0mQlwu6MKkIF0Helt72JAlQ2pW3USghQKlXj3Bn0IVVxbigCzC955Mr0D2nQrctofz5FYo7wQfd8vIaBUEJQVDg9z/+QMdOnQFBAUmq+LcVKtoJ+vUoav0InC1KT09n+DZwFgttT09PZGZmytNZWVlwd3ev0ubatWvw9PSERqNBfn4+XFxcLFPPQ90Q4hfJN7yZ+EfCPAXp6XiI/XXXJLvrUKibWrsMIpthsYurdOvWDZcuXcLly5dRWlqKlJQUBAQEGLQJCAjA1q1bAQC7du1Cnz59+GmaiIioGhbb07azs0NUVBQmT54MURQxZswYdOjQAatWrULXrl0xePBgjB07FnPnzsXQoUPRtGlTrFy50lLlEBER2TyL/k7b398f/v7+BvNmz54tj9vb2+Pdd9+1ZAlEREQPDF57nIiIyEYwtImIiGwEQ5uIiMhGMLSJiIhshCAZu8JJPebr6wsvL697Wjc3NxfNmjWr5YoebOwz87C/zMP+Ms/99FezZs3wySef1HJFVNdsLrTvR2hoKLZs2WLtMmwK+8w87C/zsL/Mw/4iHh4nIiKyEQxtIiIiG6FctGjRImsXUZe6du1q7RJsDvvMPOwv87C/zMP+atga1HfaREREtoyHx4mIiGwEQ5uIiMhGNJjQ3r9/P4YNG4ahQ4di7dq11i6n3omMjETfvn0xcuRIed57772HJ598EsHBwQgODsa+ffusWGH9UlJSgrFjx2LUqFEIDAyUb3xz+fJljBs3Dk899RRee+01lJaWWrnS+kUURYwePRpTp04FAERERCAgIEB+j6Wnp1u5wvojLy8Ps2bNwvDhwzFixAicOHECN2/exKRJk/DUU09h0qRJuHXrlrXLpDrWIEJbFEUsXrwY69atQ0pKCr799lucO3fO2mXVK6GhoVi3bl2V+S+99BKSk5ORnJxc5Y5tDZlarcZnn32Gb775BklJSfjhhx9w8uRJrFixAi+99BK+++47ODs7IzEx0dql1itxcXFo3769wbx58+bJ77HOnTtbqbL6Z8mSJXjyySexc+dOJCcno3379li7di369u2L7777Dn379uUOSAPUIEL71KlTaNOmDVq3bg21Wo3AwECkpqZau6x6xcfHB02bNrV2GTZDEAQ4OjoCADQaDTQaDQRBwKFDhzBs2DAAQEhICN9nejIzM7F3716MHTvW2qXUewUFBTh69KjcV2q1Gs7OzkhNTcXo0aMBAKNHj8b3339vzTLJChpEaGdlZcHT01Oe9vDwQFZWlhUrsh2ff/45goKCEBkZyUNxlYiiiODgYPTr1w/9+vVD69at4ezsDDu78tvUe3p68n2m56233sLcuXOhUBj+2Vm5ciWCgoLw1ltv8euECpcvX4arqysiIyMxevRozJ8/H0VFRcjJyYG7uzsAwN3dHTdu3LBypVTXGkRoG/tVmyAIVqjEtkyYMAG7d+9GcnIy3N3dsXTpUmuXVK8olUokJydj3759OHXqFC5cuFClDd9n5f773//C1dW1ym+Mw8LCsHPnTmzevBm3bt3i4d4KGo0GZ86cwYQJE5CUlAQHBwf2DQFoIKHt6emJzMxMeTorK0v+tErVa968OZRKJRQKBcaNG4dff/3V2iXVS87OzvD19cXJkyeRl5cHjUYDoPxwMN9n5X7++Wfs2bMHAQEBCAsLw6FDhzBnzhy4u7tDEASo1WqEhobyPVbB09MTnp6e6N69OwBg+PDhOHPmDNzc3HD9+nUAwPXr1+Hq6mrNMskKGkRod+vWDZcuXcLly5dRWlqKlJQUBAQEWLusek/3xwEAvv/+e3To0MGK1dQvN27cQF5eHgCguLgYP/30E9q3bw9fX1/s2rULALB161a+zyqEh4dj//792LNnD/7zn/+gT58+WLFihfwekySJ7zE9LVq0gKenp3z05uDBg2jfvj0CAgKQlJQEAEhKSsLgwYOtWSZZgZ21C6gLdnZ2iIqKwuTJkyGKIsaMGcM/DpWEhYXhyJEjyM3NxYABAzBz5kwcOXIEZ8+eBQB4eXlh8eLFVq6y/rh+/ToiIiIgiiIkScLw4cMxaNAgeHt745///CfeeecddO7cGePGjbN2qfXanDlzkJubC0mS8OijjyI6OtraJdUbCxYswJw5c1BWVobWrVsjJiYGWq0Wr732GhITE9GyZUusWrXK2mVSHeNlTImIiGxEgzg8TkRE9CBgaBMREdkIhjYREZGNYGgTERHZCIY2ERGRjWBoE5nhvffewyeffGLtMoiogWJoExER2Qj+TpvIhDVr1iApKQktW7aEq6srunTpgiZNmiAhIQFlZWVo06YNli1bBlEUMWrUKOzatQsqlQoFBQXy9KZNm/Dll19CqVTC29sbK1eutPbLIiIb1CCuiEZ0r9LS0rB9+3YkJSVBFEWEhISgS5cuGDp0KJ555hkA5XepSkxMxMSJE+Hr64t9+/ZhyJAhSElJwVNPPQWVSoW1a9diz549UKvV8uVPiYjMxcPjRDU4duwYhgwZAgcHBzg5OcnXEv/jjz/w3HPPISgoCNu2bcMff/wBABg7diw2b94MANiyZQtCQ0MBAJ06dcKcOXOQnJwMpVJpnRdDRDaPoU1kgrHba0ZERCAqKgrbtm3DjBkz5PtA9+rVCxkZGThy5AhEUUTHjh0BAGvXrsVzzz2H06dPIzQ0VL4TGBGRORjaRDXw8fHB7t27UVxcjIKCAvz3v/8FABQWFqJFixYoKyvDtm3bDNYZPXo0wsLC5L1srVaLa9euoU+fPpg7dy7y8/NRVFRU56+FiGwfT0QjMkF3IpqXlxc8PDzg7e0NBwcHrFu3Dl5eXujYsSMKCwuxdOlSAEB2djYGDx6MH3/8Ec7OzigrK8MLL7yAgoICSJKEUaNGYcqUKVZ+VURkixjaRLVs586dSE1NxfLly61dChE9YHj2OFEteuONN7B//36sXbvW2qUQ0QOIe9pEREQ2gieiERER2QiGNhERkY1gaBMREdkIhjYREZGNYGgTERHZiP8HbCjIDYRS2M8AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -401,7 +388,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -421,24 +408,46 @@
    "source": [
     "#### The use of checkpoints to change parameters on the fly\n",
     "\n",
-    "A cool feature of the original SEIRSplus package by Ryan McGee was the use of so-called *checkpoints* dictionary to change simulation parameters on the fly. In our modification, this feature is preserved. Below you can find an example of a *checkpoints* dictionary. The simulation will be started with the previously initialised parameters. After 40 days, social interaction will be limited by lowering the network connectivity to an average of 4 edges per node. The chance of random encounters is lowered to 10%. After 80 days, social restrictions are lifted and beta once more assumes its *business-as-usual* value. *checkpoints* is the only optional argument of the *sim* functions and is set to *None* per default."
+    "A cool feature of the original SEIRSplus package by Ryan McGee was the use of so-called *checkpoints* dictionary to change simulation parameters on the fly. In our modification, this feature is preserved. Below you can find an example of a *checkpoints* dictionary. The simulation will be started with the previously initialised parameters. After 40 days, social interaction will be limited by lowering the network connectivity to an average of 2 edges per node. The chance of random encounters is lowered to 10%. After 80 days, social restrictions are lifted and beta once more assumes its *business-as-usual* value. *checkpoints* is the only optional argument of the *sim* functions and is set to *None* per default."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "t = 10.14\n"
+      "t = 0.18\n",
+      "t = 10.05\n",
+      "t = 20.01\n",
+      "t = 30.01\n",
+      "t = 40.03\n",
+      "t = 50.00\n",
+      "[Checkpoint: Updating parameters]\n",
+      "t = 60.00\n",
+      "t = 70.02\n",
+      "t = 80.01\n",
+      "t = 0.04\n",
+      "t = 10.03\n",
+      "t = 20.42\n",
+      "t = 30.27\n",
+      "t = 0.04\n",
+      "t = 10.06\n",
+      "t = 20.27\n",
+      "t = 30.11\n",
+      "t = 40.62\n",
+      "t = 50.05\n",
+      "[Checkpoint: Updating parameters]\n",
+      "t = 60.28\n",
+      "t = 70.67\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -448,7 +457,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -459,12 +468,12 @@
    ],
    "source": [
     "# Create checkpoints dictionary\n",
-    "chk = {'t':       [80], \n",
+    "chk = {'t':       [60], \n",
     "       'G':       [G_dist],\n",
-    "       'p':       [0.02],\n",
+    "       'p':       [0.03],\n",
     "      }\n",
     "# Run simulation\n",
-    "y = model.sim(,checkpoints=chk)\n",
+    "y = model.sim(80,checkpoints=chk)\n",
     "# Visualise\n",
     "model.plotPopulationStatus()\n",
     "model.plotInfected()"