diff --git a/IntervalsModel/network-data/POLYMOD/AALPoisPriors.csv b/IntervalsModel/network-data/POLYMOD/AALPoisPriors.csv
index 10d5f882e2d15555d102fc91d58a2460fb628c03..a699693d9084c04076ea5dbd99fb2e9526f33552 100644
--- a/IntervalsModel/network-data/POLYMOD/AALPoisPriors.csv
+++ b/IntervalsModel/network-data/POLYMOD/AALPoisPriors.csv
@@ -1,19 +1,19 @@
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+15,O,O,home,0.00653121510676935,0.006080357668624411,0.005188338605003396,0.005544927850864068,0.006412614731911308,0.006536980663643201,0.005724093145841693,0.004819787952984139,0.004190327862237522,0.003810988755813858,0.0035961968395344713,0.0034841552794301392,0.003441565530366174,0.0034575921730097534,0.0035393130555084027,0.0037099461015401756,0.004010817050071068,0.004509468515876109,0.005318625108477837,0.006635217420188378,0.008816172497081677,0.012506689279164448,0.018729696473808517,0.028181148638627057,0.037801528162095796,0.039673533960473975,0.0335173725471372,0.026191912761515015,0.020720389792070144,0.017080251535961943,0.01470061708034618,0.013131160416744964,0.012083040715045899,0.011376594283307718,0.010898335680063808,0.010574611532590017,0.010356351104528323,0.010210198108015338,0.010113198048176782,0.010049479980989151,0.010008088715791985,0.009981511022179177,0.009964644526309038,0.009954065185400127,0.009947505146360952,0.009943482659802947,0.009941042760788277,0.009939578231133252,0.009938707994455233,0.009938195897332582,0.00993789735774971,0.009937724877495913,0.009937626087850418,0.00993756997557447,0.009937538359227808,0.00993752068261566,0.009937510873277917,0.009937505468880246,0.009937502512027074,0.009937500905124356,0.00993750003751491,0.009937499572007404,0.009937499323759577,0.009937499192150631,0.009937499122774817,0.009937499086405744,0.009937499067441536,0.009937499057603895,0.009937499052526095,0.009937499049917737,0.00993749904858409,0.009937499047905236,0.009937499047561147,0.009937499047387447,0.00993749904730009,0.00993749904725631,0.009937499047234451,0.009937499047223564,0.009937499047218154,0.009937499047215475,0.00993749904721415,0.009937499047213492,0.009937499047213173,0.009937499047213014,0.009937499047212937,0.009937499047212896,0.00993749904721288,0.009937499047212868,0.009937499047212866,0.009937499047212861,0.009937499047212861,0.009937499047212861,0.009937499047212861,0.009937499047212861,0.009937499047212861
+16,O,O,workschool,0.079336199604971,0.042764044445948404,0.027743451069894814,0.02884039959494611,0.03624372963002831,0.04082623977580251,0.03668089557776497,0.030184878778297654,0.025527000616135633,0.022758987240798098,0.021209234554533017,0.02038827208029988,0.020023780367421806,0.019995633982350627,0.02028591693023873,0.02094312005288935,0.022049029710695257,0.02367344493814067,0.025792647711625587,0.02815052275074422,0.030120238359804243,0.030817597934976317,0.02968357171599754,0.027025181690733303,0.023734635324460653,0.020590346809497066,0.017961485335486808,0.015911907775194085,0.014370706819993037,0.013233829258002532,0.012404805002259716,0.011805509469257371,0.011375850573717072,0.011070512781391498,0.010855588152644357,0.01070582729950497,0.010602543022545735,0.0105320266625635,0.010484336908898911,0.010452360431649991,0.010431079582543122,0.010417005006415639,0.010407742599071764,0.010401669634131382,0.010397697858329011,0.010395103952045312,0.010393410625358084,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+17,O,O,rest,0.052216333891883446,0.04098345560197238,0.028988597607230784,0.033051989152861595,0.04721913895263862,0.054095619760174476,0.04220851874499575,0.030558322712437212,0.02402162710131033,0.020608164913000136,0.01882146186220746,0.017908434773749374,0.017512748391566624,0.017490012999071564,0.01782392024034913,0.018585504087041432,0.019909732587802462,0.021970970959791152,0.024920206353448073,0.028703230481552773,0.03268181710650293,0.03533381559383407,0.0350163629173102,0.03170786998648613,0.027057683703350075,0.02262893597894859,0.01906874277661031,0.016413834653791908,0.014494260270086321,0.013122185268372258,0.012145551044108076,0.011452197124789296,0.010961660394996756,0.01061639409304638,0.010375027141189043,0.010207653831832694,0.010092611966468545,0.010014250693663074,0.009961339415792069,0.009925899786771983,0.009902331033602102,0.009886750710464442,0.00987650059778831,0.009869781423634772,0.00986538761513412,0.009862518339363838,0.009860645356976892,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
diff --git a/IntervalsModel/network-data/POLYMOD/AALfreqdist.csv b/IntervalsModel/network-data/POLYMOD/AALfreqdist.csv
index 96b879a9d2c0a769a5cc03591d1e32f98e9df9b4..5cd9c2b0c2c9c9e28d1461804057d0bb94d1a49b 100644
--- a/IntervalsModel/network-data/POLYMOD/AALfreqdist.csv
+++ b/IntervalsModel/network-data/POLYMOD/AALfreqdist.csv
@@ -1,13 +1,13 @@
 ,Age_in,Age_out,location,0,1,2
-0,Y,Y,workschool,0.6041160611803646,0.24808477697995354,0.1477991618396818
-1,Y,Y,rest,0.24129405028369164,0.28543542761959295,0.4732705220967154
-2,Y,M,workschool,0.6041160611803646,0.24808477697995354,0.1477991618396818
-3,Y,M,rest,0.24129405028369164,0.28543542761959295,0.4732705220967154
-4,Y,O,workschool,0.6041160611803646,0.24808477697995354,0.1477991618396818
-5,Y,O,rest,0.24129405028369164,0.28543542761959295,0.4732705220967154
-6,M,M,workschool,0.6041160611803646,0.24808477697995354,0.1477991618396818
-7,M,M,rest,0.24129405028369164,0.28543542761959295,0.4732705220967154
-8,M,O,workschool,0.6041160611803646,0.24808477697995354,0.1477991618396818
-9,M,O,rest,0.24129405028369164,0.28543542761959295,0.4732705220967154
-10,O,O,workschool,0.6041160611803646,0.24808477697995354,0.1477991618396818
-11,O,O,rest,0.24129405028369164,0.28543542761959295,0.4732705220967154
+0,Y,Y,workschool,0.77870480731029,0.19355052310952192,0.027744669580188053
+1,Y,Y,rest,0.37273777435502503,0.33153638814016173,0.29572583750481324
+2,Y,M,workschool,0.4929277756915759,0.3358367755664038,0.1712354487420203
+3,Y,M,rest,0.24455087479229792,0.26116704134493207,0.49428208386277
+4,Y,O,workschool,0.5075757575757576,0.21212121212121213,0.2803030303030303
+5,Y,O,rest,0.20387453874538744,0.3274907749077491,0.46863468634686345
+6,M,M,workschool,0.4924376199616123,0.2583493282149712,0.24921305182341652
+7,M,M,rest,0.1579316799251287,0.25869599126501325,0.583372328809858
+8,M,O,workschool,0.3647859922178988,0.24610894941634243,0.3891050583657587
+9,M,O,rest,0.1687344913151365,0.2924937965260546,0.5387717121588089
+10,O,O,workschool,0.2,0.2545454545454545,0.5454545454545455
+11,O,O,rest,0.1589895988112927,0.35066864784546803,0.4903417533432392
diff --git a/IntervalsModel/network-data/POLYMOD/Duration-priors.py b/IntervalsModel/network-data/POLYMOD/Duration-priors.py
index caac4383a793dd336e91b5010e8c36daa0875111..01d25f9500433cc6d535f309de2c3e1656f9d895 100644
--- a/IntervalsModel/network-data/POLYMOD/Duration-priors.py
+++ b/IntervalsModel/network-data/POLYMOD/Duration-priors.py
@@ -3,14 +3,12 @@
 """
 Created on 2021-03-11
 
-Extracts the coarse duration distributions stratified by symmetric age 
-and location from the POLYMOD dataset. 
-
-Uses this duration data to deduce priors on the poisson
+Uses this POLYMOD duration data to deduce priors on the poisson
 random variables controlling individual contact durations
 
-Determines the fraction of interactions which are fixed vs rewrote in the ABM.
-Fixed are those with frequency_mult =1, rewrote are the rest
+Determines the fraction of interactions which are daily, 3x/week or singular
+ in the ABM. Daily are those with frequency_mult =1, 3x/week are 2 and
+ singular are 3-5. 
 
 @author: mark
 """
@@ -18,6 +16,7 @@ import numpy as np
 import pandas as pd
 import math
 from itertools import product
+import matplotlib.pyplot as plt
 
 # Read the cleaned survey as a dataframe
 Contact = pd.read_csv("AALContact_data.csv")
@@ -28,39 +27,46 @@ Ages = ['Y', 'M', 'O']
 SymAge = [('Y','Y'), ('Y', 'M'), ('Y', 'O'), ('M', 'M'),('M', 'O'), ('O','O')]
 Locales = ['home', 'workschool', 'rest']
 
+# define row function for symage
+def symage_row(row):
+    agesrc = row['age_source']
+    agetar = row['age_target']
+    for symage in SymAge:
+        #x, y = symage[0], symage[1]
+        if symage == (agesrc, agetar) or symage == (agetar, agesrc):
+            return symage
+Contact['symage'] = Contact.apply(symage_row, axis=1)
+#print(Contact[Contact['symage']==('M', 'M')].head(10))
+
 # Dictionary separating contact data by symage-location
 ContAAL = {}
-columns = ['part_id', 'cont_id', 'duration_multi', 'frequency_multi']
+columns = ['part_id', 'cont_id', 'age_source', 'age_target', 
+           'duration_multi', 'frequency_multi']
 for symage, loc in list(product(SymAge,Locales)):
     x,y,z = symage[0], symage[1], loc
-    C = Contact[Contact['cnt_'+z] == True].copy(deep=True)
-    def symage_check(row):
-        agesrc = row['age_source']
-        agetar = row['age_target']
-        if (agesrc == x or agesrc == y) and (agetar==x or agetar==y):
-            return True
-        else:
-            return False
-    C["check"] = C.apply(symage_check, axis=1)
+    C = Contact[Contact['cnt_'+z] == True][Contact['symage']==symage]
     ContAAL[(x,y,z)] = C[columns].copy(deep=True)
-
-
+test1 = ('Y', 'Y', 'home')
+test2 = ('Y', 'M', 'home')
+print(ContAAL[test1].head(15))
+print(ContAAL[test2].head(25))
 
 # Duration distributions stratified by SymAge-location
 DurFreqAAL = {}
 for symage, loc in list(product(SymAge,Locales)):
     x,y,z = symage[0], symage[1], loc
-    C = ContAAL[(x,y,z)].copy(deep=True)
-    #D = pd.DataFrame([[ID, C[C['part_id']==ID]['duration_multi']] for ID in PartsAge[x]], columns = ["ID", "Dur"])
+    C = ContAAL[(x,y,z)]
     Durlist = [C[C['duration_multi']==i]["duration_multi"].count() for i in range(1,6)]
     DurFreqAAL[(x,y,z)] = Durlist/np.sum(Durlist)
+    
+
 
 # Define error functions for poisson random variables
 durcutoff = {'home': 6*16, 'workschool': 6*8, 'rest': 6*8}
 def PoisArray(lam, loc):
     arr = [math.exp(-lam + k*math.log(lam) - np.sum([math.log(n) for n in range(1,k+1)]) ) 
            for k in range(durcutoff[loc])]
-    return arr
+    return arr/np.sum(arr)
 
 def PoisBin(lam,loc):
     Arr = PoisArray(lam,loc)
@@ -73,13 +79,16 @@ def PoisErr(lam, label): # label is SymAge-Location
     return err
 PoisErr = np.vectorize(PoisErr, excluded=[1])
 
-PoisPrior={}
 
+
+PoisPrior={}
 for symage, loc in list(product(SymAge, Locales)):
     AAL = (symage[0],symage[1],loc)
     arr = [1/PoisErr(i,AAL) for i in range(1,durcutoff[loc])]
     zeropad = [0 for i in range(durcutoff['home'] - durcutoff[loc])]
     PoisPrior[AAL] = (arr+zeropad)/np.sum(arr)
+    
+
 
 
 # Save them to csv
@@ -95,7 +104,7 @@ freqdistAAL={}
 locales_nohh = ['workschool', 'rest']
 for symage, loc in list(product(SymAge, locales_nohh)):
     x, y, z = symage[0],symage[1],loc
-    C = ContAAL[(x,y,z)].copy(deep=True)
+    C = ContAAL[(x,y,z)]
     tot = len(C)
     freqfull = np.full(5,0.)
     for i in range(1,6):
@@ -110,4 +119,4 @@ dfkeys = pd.DataFrame([pd.Series(x) for x in df.col1])
 dfkeys.columns = ["Age_in", "Age_out", "location"]
 dfvals = pd.DataFrame([pd.Series(x) for x in df.col2])
 dfout = dfkeys.join(dfvals)
-dfout.to_csv("AALfreqdist.csv") 
+dfout.to_csv("AALfreqdist.csv")