diff --git a/data/covid19_DTM/interim/QALY_model/long_COVID/average_QALY_losses.csv b/data/covid19_DTM/interim/QALY_model/long_COVID/average_QALY_losses.csv
index 1f7eab0de..daae269d8 100644
--- a/data/covid19_DTM/interim/QALY_model/long_COVID/average_QALY_losses.csv
+++ b/data/covid19_DTM/interim/QALY_model/long_COVID/average_QALY_losses.csv
@@ -1,429 +1,429 @@
 hospitalisation,age,mean,sd,lower,upper
-Non-hospitalised,0,0.7935148886177248,0.31417909127761423,0.2423022087164734,1.3867414434929877
-Non-hospitalised,1,0.7827494130434783,0.3097146420948966,0.2395149785780798,1.3677860389502539
-Non-hospitalised,2,0.7712002244030746,0.30490267873782745,0.23650705811652592,1.347298894566754
-Non-hospitalised,3,0.7596813652244611,0.30010216731982525,0.23350584122365797,1.326856441352946
-Non-hospitalised,4,0.7482408870740573,0.29533393440503697,0.23052449642670778,1.3065493551390754
-Non-hospitalised,5,0.7368675175767143,0.2905926542348832,0.22755963232128923,1.2863535774735702
-Non-hospitalised,6,0.7255945296806691,0.28589278645854105,0.2246203862763359,1.2663321215080803
-Non-hospitalised,7,0.7144123144004324,0.28122985282734186,0.22170391132718031,1.2464649032685233
-Non-hospitalised,8,0.7033327079834,0.2766089071509234,0.2188133968142906,1.226773792050471
-Non-hospitalised,9,0.6923647074251721,0.2720337764746854,0.21595125733362705,1.2072753252261421
-Non-hospitalised,10,0.6815038211960737,0.2675023396904403,0.2131161481716525,1.1879599911054044
-Non-hospitalised,11,0.6707528947783638,0.26301574723985033,0.21030879671849867,1.1688306106519284
-Non-hospitalised,12,0.6601287137832386,0.2585813406230745,0.20753384436973676,1.1499161257235309
-Non-hospitalised,13,0.6496123391488879,0.25419066177624083,0.20478594896876634,1.1311850714457812
-Non-hospitalised,14,0.6392338755946715,0.24985697790217817,0.2020734968526022,1.1126953691559838
-Non-hospitalised,15,0.628960516394513,0.24556572673826393,0.1993872954019011,1.0943834207481642
-Non-hospitalised,16,0.6188301943300375,0.2413336593680989,0.19673792964129624,1.0763223656060572
-Non-hospitalised,17,0.6088119705630577,0.23714707108834698,0.19411675412788063,1.0584524247803198
-Non-hospitalised,18,0.5989231785197543,0.23301361891831826,0.19152861124355527,1.0408070596451031
-Non-hospitalised,19,0.5891840019609387,0.2289422159078727,0.18897913172373068,1.0234251716732503
-Non-hospitalised,20,0.5795425406404991,0.22490994590460262,0.18645386787650484,1.0062069474968398
-Non-hospitalised,21,0.5700199194502871,0.2209261665871037,0.18395873526921233,0.9891932882753016
-Non-hospitalised,22,0.560647002691227,0.21700451760302575,0.1815023433977218,0.9724436822314451
-Non-hospitalised,23,0.5513684681113125,0.21312063509026077,0.17906934283457887,0.955852156707679
-Non-hospitalised,24,0.542205012493777,0.2092836980624765,0.17666553314345293,0.9394588175861959
-Non-hospitalised,25,0.5331560027208048,0.20549348185606553,0.17429078457226863,0.923262823311068
-Non-hospitalised,26,0.5242070694194199,0.2017437291664715,0.17194116763412412,0.9072371166675576
-Non-hospitalised,27,0.5153909323713911,0.19804889040910592,0.1696257954504701,0.8914446501736304
-Non-hospitalised,28,0.5066689120771534,0.19439203743811367,0.16733401803759956,0.8758120052238206
-Non-hospitalised,29,0.4980560652033406,0.19077986410099462,0.16507006241649913,0.8603684234400621
-Non-hospitalised,30,0.48954792287719284,0.18721048966738799,0.16283275543145329,0.8451058898036843
-Non-hospitalised,31,0.48115176279359045,0.18368718150431668,0.16062416356752304,0.8300387283163306
-Non-hospitalised,32,0.47283797983823694,0.18019707257967416,0.15843620075840215,0.815111283166894
-Non-hospitalised,33,0.4646133028640916,0.17674321396277662,0.15627079859037468,0.8003369707819383
-Non-hospitalised,34,0.4564867842864103,0.17332965721442697,0.1541305158309651,0.7857335141536175
-Non-hospitalised,35,0.44843640084216596,0.16994689597496765,0.15200938347156073,0.7712598911259065
-Non-hospitalised,36,0.44047745227001206,0.16660170663701399,0.14991167285382384,0.7569456200394192
-Non-hospitalised,37,0.4325923770959779,0.16328654830229639,0.1478326517618652,0.7427582157257817
-Non-hospitalised,38,0.42479723650052387,0.1600085098872848,0.14577678554822232,0.7287285131533786
-Non-hospitalised,39,0.41707195277658343,0.1567589712364413,0.14373866393724696,0.7148193733115009
-Non-hospitalised,40,0.40943830924538743,0.1535474815097419,0.1417242961863202,0.7010722313541701
-Non-hospitalised,41,0.4018688617998613,0.15036224123277953,0.13972627456185963,0.6874362210876448
-Non-hospitalised,42,0.39434623879298997,0.14719585352625505,0.13773996034173427,0.6738795580051353
-Non-hospitalised,43,0.38691289457926703,0.14406677745828086,0.13577695786345886,0.660482115872698
-Non-hospitalised,44,0.3795152715093309,0.14095198082564428,0.1338228058153735,0.6471445907432892
-Non-hospitalised,45,0.37218106320238015,0.1378635273148763,0.13188508951675093,0.6339192134579709
-Non-hospitalised,46,0.36493160152523535,0.13481068315311648,0.12996963379233958,0.6208460675629366
-Non-hospitalised,47,0.35770584467693645,0.13176726596873148,0.1280600035240858,0.6078123604633976
-Non-hospitalised,48,0.350538460416561,0.128748290868091,0.1261656341769568,0.5948829823531331
-Non-hospitalised,49,0.34339896025101013,0.1257407593627082,0.12427837171123783,0.5820020471338104
-Non-hospitalised,50,0.33633086678332025,0.12276342788056624,0.12240999681011563,0.5692504862448811
-Non-hospitalised,51,0.3292830118786437,0.11979444512894105,0.12054679934796154,0.5565342999657885
-Non-hospitalised,52,0.32230208498696855,0.11685387944369699,0.11870138088073971,0.5439400095668674
-Non-hospitalised,53,0.31534623100648784,0.11392389543991802,0.11686255381322944,0.5313909476620361
-Non-hospitalised,54,0.30843654909027024,0.11101358376213329,0.11503602862754432,0.5189263425461095
-Non-hospitalised,55,0.3015685769068029,0.10812109781206652,0.11322065175310768,0.5065383414269583
-Non-hospitalised,56,0.2947103185646623,0.10523286646130685,0.11140791516372041,0.4941687007563716
-Non-hospitalised,57,0.2878796610194109,0.10235658781527415,0.10960265460985635,0.48185063235886616
-Non-hospitalised,58,0.281078714483823,0.09949321916481546,0.10780547603854382,0.4695883448193632
-Non-hospitalised,59,0.2743185246686917,0.09664750468010344,0.10601935961956188,0.4574022969453085
-Non-hospitalised,60,0.2675449826959003,0.09379651598408459,0.10422993228962933,0.4451941173212019
-Non-hospitalised,61,0.26085610365366424,0.0909819048529854,0.10246331653053661,0.4331427078682745
-Non-hospitalised,62,0.2542172640878498,0.088188983003333,0.10071030375636537,0.4211850270620155
-Non-hospitalised,63,0.2476623948624547,0.08543213287186598,0.09897991170612314,0.4093827973148247
-Non-hospitalised,64,0.24113485305062402,0.08268737069427638,0.09725710653920333,0.3976331422338611
-Non-hospitalised,65,0.23464605523374754,0.07995955610507398,0.09554493683268384,0.38595676715040733
-Non-hospitalised,66,0.22819820054051512,0.07724963793823228,0.09384399864742402,0.37435693562442185
-Non-hospitalised,67,0.22171127343412578,0.07452384640021663,0.09213313172079426,0.3626901056038824
-Non-hospitalised,68,0.21532235306006137,0.07184010347162018,0.09044863360836018,0.3512046363294416
-Non-hospitalised,69,0.20880271248230606,0.06910192268213117,0.08873004931400347,0.33948706771036413
-Non-hospitalised,70,0.2024116298253288,0.06641871657644091,0.08704593094089927,0.3280062732692569
-Non-hospitalised,71,0.19607635176058785,0.06375978375180816,0.0853770377989497,0.316630642353807
-Non-hospitalised,72,0.1897299795088367,0.06109696398931985,0.08370572581517342,0.3052395714186561
-Non-hospitalised,73,0.1834680812510294,0.0584705079420584,0.0820572017006151,0.2940053562653685
-Non-hospitalised,74,0.17723473424628533,0.05585688370131766,0.08041671744131272,0.2828271909309509
-Non-hospitalised,75,0.17109255761631192,0.05328242519255187,0.0788007552611922,0.2718176755572011
-Non-hospitalised,76,0.16495031121335932,0.05070879662537961,0.07718529338084695,0.2608126885650739
-Non-hospitalised,77,0.15872635231898707,0.04810178002485507,0.07554888860688104,0.24966581824540907
-Non-hospitalised,78,0.15276392009606496,0.04560547042183685,0.07398175664433843,0.2389931573301006
-Non-hospitalised,79,0.14667311781237452,0.043056281149969323,0.07238142957806416,0.22809368640775943
-Non-hospitalised,80,0.14076698744134142,0.04058554276818634,0.07083011853690484,0.2175295951878685
-Non-hospitalised,81,0.1349938153989904,0.03817155006923483,0.0693141954703488,0.207207967565043
-Non-hospitalised,82,0.12923108227153324,0.03576302600750779,0.0678015002822642,0.19690923509692723
-Non-hospitalised,83,0.12363054361234647,0.03342355461279164,0.06633180288474917,0.18690482781413256
-Non-hospitalised,84,0.11828395096128308,0.03119150057296133,0.06492908485990648,0.17735833025144296
-Non-hospitalised,85,0.11301285810714262,0.0289922809966988,0.06341548736416927,0.1679503164982513
-Non-hospitalised,86,0.10804355725144464,0.026920479952600874,0.061870929404384975,0.15908472964646467
-Non-hospitalised,87,0.10331065387783417,0.02494876678558532,0.060398683873747826,0.15064416942451891
-Non-hospitalised,88,0.09894204047042399,0.023130449094346577,0.059043579882771,0.14285634028494815
-Non-hospitalised,89,0.09486232549578025,0.021434018507333882,0.05777747743634109,0.13558608339449563
-Non-hospitalised,90,0.09090642347274541,0.019790783880827577,0.05654905725772251,0.12853856861368118
-Non-hospitalised,91,0.08745567935323478,0.018359307167724277,0.05547649889161276,0.12239333147447279
-Non-hospitalised,92,0.08428190916290065,0.017044530582410105,0.05448932467408543,0.116743019734608
-Non-hospitalised,93,0.0810221461407603,0.015696050946339683,0.053475106242703116,0.11094022417338502
-Non-hospitalised,94,0.07803624561500065,0.014463270736131318,0.05254541162429662,0.1056240421490883
-Non-hospitalised,95,0.07547168361778242,0.013406894799561975,0.0520623275658384,0.10105956153602945
-Non-hospitalised,96,0.0732158168989003,0.012479980565553977,0.05125475863913113,0.09704573928308177
-Non-hospitalised,97,0.07121992174987073,0.011662126420764942,0.050539496838064885,0.09349558738536061
-Non-hospitalised,98,0.0694951062481589,0.010957518873527404,0.049920565908253146,0.09042875852937317
-Non-hospitalised,99,0.0676621558481355,0.010211005131325116,0.04926764636460227,0.08694604233024915
-Non-hospitalised,100,0.06660967501050723,0.00978464969843044,0.04888019548180275,0.08516312927937361
-Non-hospitalised,101,0.06444900691655336,0.008911326539880656,0.04824326244189084,0.08134413602632591
-Non-hospitalised,102,0.0632482299581572,0.008430091001136241,0.047857918548256675,0.07906973277082777
-Non-hospitalised,103,0.06089595018634276,0.00749521963573015,0.047100342826921646,0.07490610407480318
-Non-hospitalised,104,0.05781585450864537,0.006304275173193031,0.045725965163107,0.06979073120140537
-Non-hospitalised,105,0.05433204300154784,0.005054278460054339,0.04482553774948081,0.06380253286448412
-Non-hospitalised,106,0.04259856995048379,0.0025554534387112883,0.037669669526621076,0.04764855470370469
-Cohort,0,2.0388817915749544,0.8992141967774195,0.5285961407065378,3.980240087870274
-Cohort,1,2.0134230834527584,0.8883216919041147,0.5225216223479228,3.9322309279392007
-Cohort,2,1.9847195763351742,0.8756827870389561,0.5157198246577558,3.8768241463635977
-Cohort,3,1.9560070244021366,0.8630201205795083,0.5089184342877962,3.8213261295778276
-Cohort,4,1.9274548056137717,0.8504205393786162,0.5021560053814846,3.766107506046169
-Cohort,5,1.8989978158261847,0.8378461386377747,0.4954183257906335,3.711010166476132
-Cohort,6,1.8707553516469388,0.8253584528832262,0.4887324952467594,3.656296395324024
-Cohort,7,1.842675764306794,0.8129277856156978,0.4820871802419533,3.6018422099383716
-Cohort,8,1.8147965033329083,0.8005725484476655,0.4754910027153992,3.547726921975398
-Cohort,9,1.787145249718417,0.7883061863989813,0.4689503559995824,3.4940086154921612
-Cohort,10,1.7596967302087998,0.7761139755008981,0.4624597114479176,3.4406261647003626
-Cohort,11,1.732456702830841,0.7639980377048134,0.45602047992448974,3.3875895342564264
-Cohort,12,1.7054880237544654,0.7519912666631426,0.44964687716760604,3.3350385403375893
-Cohort,13,1.6787110425008647,0.7400503435440826,0.443321083650812,3.2827909406191416
-Cohort,14,1.65224360399696,0.7282378843244827,0.43706963817329364,3.2321802367690022
-Cohort,15,1.6259527390496797,0.7164823314726737,0.43086269459502313,3.181975425298696
-Cohort,16,1.5999884309039099,0.7048636748505103,0.42473399738692313,3.132324424983575
-Cohort,17,1.5742271773860805,0.6933157150742842,0.4186557876081998,3.082913170431326
-Cohort,18,1.5487379041299882,0.6818752474043206,0.41264356561644,3.0339166404283153
-Cohort,19,1.52360027094271,0.670584751516135,0.4067152573709569,2.985536382187579
-Cohort,20,1.4986115597519816,0.6593362413693363,0.4008252077736613,2.9372619278643395
-Cohort,21,1.4738569227228404,0.6481753981066387,0.3949925422884715,2.8893107554112394
-Cohort,22,1.449458179172876,0.63716725179694,0.389244672715591,2.8419910859717934
-Cohort,23,1.4252032840060007,0.6261994514956624,0.3835337601693089,2.79477363572516
-Cohort,24,1.4011759842255296,0.6153170123398394,0.37787860788799466,2.747872652779641
-Cohort,25,1.3773774162012642,0.6045210705947947,0.372279398876615,2.7012951734454296
-Cohort,26,1.3537569921977228,0.5937855130305209,0.3667246271042562,2.6549202165548405
-Cohort,27,1.330442779360257,0.5831785972170478,0.3612431316453844,2.6090705798522125
-Cohort,28,1.307292980449148,0.5726262127639976,0.35580278716922636,2.5633999347766183
-Cohort,29,1.28436914387444,0.5621615775241053,0.350417400454356,2.518066435217357
-Cohort,30,1.261659370027181,0.551779222725452,0.3450841783337321,2.473046541656386
-Cohort,31,1.23919523067138,0.5414964710274143,0.3398101677362464,2.428423394114581
-Cohort,32,1.2168733540213308,0.5312601795771764,0.33457183629213977,2.3839508720098106
-Cohort,33,1.1947245850290604,0.5210875217239584,0.3293760381100992,2.3397119236717665
-Cohort,34,1.1727872918589524,0.5109994381535137,0.32423135349487575,2.2958066465001026
-Cohort,35,1.1509882197543881,0.5009589386952332,0.31912102269615916,2.2520653398529835
-Cohort,36,1.1293880375007925,0.49099852392734006,0.31405869163902583,2.208641827241521
-Cohort,37,1.10793049833849,0.48109021395063456,0.30903143970080216,2.165408751228977
-Cohort,38,1.0866778293389552,0.47126711690283324,0.30405329274979614,2.1225222219145503
-Cohort,39,1.0655657303503345,0.46149725479863085,0.2991094799211967,2.0798366548310137
-Cohort,40,1.04467490388135,0.4518229592895164,0.29421826757361597,2.03755020256099
-Cohort,41,1.0239164884910839,0.4421998476223247,0.2893592666991986,1.995460302284042
-Cohort,42,1.0032381935507533,0.4326025936633972,0.2845203816633372,1.9534532760666588
-Cohort,43,0.9827888610733586,0.4231078065833193,0.2797354836678798,1.911884289260335
-Cohort,44,0.9623947121578841,0.4136286255022229,0.2749647081219969,1.8703566980807
-Cohort,45,0.9421540655754211,0.4042158828082801,0.2702304010127748,1.8291066905776687
-Cohort,46,0.9221412285408568,0.3949077345137071,0.26554948321257377,1.7883109585695522
-Cohort,47,0.90216185597366,0.385607785576177,0.2608772804226187,1.7475310920479352
-Cohort,48,0.882334292132964,0.37637635483446563,0.25624079671797495,1.7070453621029646
-Cohort,49,0.8625657831801716,0.36716831449218124,0.25161860230331257,1.6666506542618256
-Cohort,50,0.8429997933271356,0.35805581191325264,0.24704355291097338,1.6266775475558584
-Cohort,51,0.8234786674703262,0.3489617193077022,0.24247927605981223,1.586777852655728
-Cohort,52,0.8041534436979831,0.3399614247273939,0.23796043159954386,1.5472956747805133
-Cohort,53,0.7848973039454056,0.33099333363912453,0.23345770579373523,1.507954070692058
-Cohort,54,0.7657797396972051,0.3220923711846878,0.22898701492150123,1.468913087611602
-Cohort,55,0.7467903076451918,0.3132541112351842,0.2245458639751767,1.4301545073178001
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-ICU,94,0.19433859188431016,0.046625249528626594,0.10907530058381415,0.2890669263220663
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-ICU,97,0.1661747090699182,0.037861461919284904,0.09719003830120573,0.24179743272827448
-ICU,98,0.15906842025853898,0.035662807415821736,0.09411435312387309,0.22987335209895202
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-ICU,100,0.1472028402001833,0.03200702516852799,0.08897402202927242,0.20996716005425145
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-ICU,106,0.06338673047498927,0.00897350431789788,0.04768255774619171,0.08022856831025232
-Non-hospitalised (no AD),0,0.06018970662432619,0.0029729533081749764,0.05458845828834137,0.06621683529190213
-Non-hospitalised (no AD),1,0.06018887377052802,0.0029729533081749734,0.05458762543454323,0.06621600243810398
-Non-hospitalised (no AD),2,0.06018761788132908,0.002972953308174975,0.05458636954534426,0.06621474654890504
-Non-hospitalised (no AD),3,0.06018606558792035,0.0029729533081749734,0.05458481725193558,0.06621319425549631
-Non-hospitalised (no AD),4,0.060184270466377525,0.0029729533081749742,0.05458302213039274,0.06621139913395348
-Non-hospitalised (no AD),5,0.06018226478015189,0.002972953308174975,0.05458101644416707,0.06620939344772783
-Non-hospitalised (no AD),6,0.06018007074524419,0.002972953308174975,0.05457882240925936,0.06620719941282015
-Non-hospitalised (no AD),7,0.06017770486662893,0.0029729533081749742,0.05457645653064412,0.0662048335342049
-Non-hospitalised (no AD),8,0.06017518003040379,0.0029729533081749742,0.05457393169441901,0.06620230869797973
-Non-hospitalised (no AD),9,0.06017250665743683,0.002972953308174973,0.05457125832145206,0.06619963532501279
-Non-hospitalised (no AD),10,0.06016969339957393,0.0029729533081749742,0.05456844506358912,0.0661968220671499
-Non-hospitalised (no AD),11,0.06016674758828379,0.0029729533081749755,0.05456549925229897,0.06619387625585975
-Non-hospitalised (no AD),12,0.060163675538624714,0.0029729533081749747,0.054562427202639936,0.06619080420620067
-Non-hospitalised (no AD),13,0.06016048276351087,0.0029729533081749755,0.05455923442752605,0.06618761143108683
-Non-hospitalised (no AD),14,0.06015717412968474,0.002972953308174973,0.05455592579369996,0.06618430279726069
-Non-hospitalised (no AD),15,0.060153753974322585,0.0029729533081749755,0.05455250563833781,0.06618088264189854
-Non-hospitalised (no AD),16,0.06015022619419019,0.002972953308174976,0.05454897785820538,0.06617735486176615
-Non-hospitalised (no AD),17,0.06014659431513313,0.0029729533081749742,0.054545345979148306,0.06617372298270911
-Non-hospitalised (no AD),18,0.06014286154714716,0.002972953308174976,0.05454161321116234,0.06616999021472311
-Non-hospitalised (no AD),19,0.060139030828658596,0.002972953308174975,0.05453778249267379,0.06616615949623456
-Non-hospitalised (no AD),20,0.06013510486258882,0.002972953308174975,0.054533856526604044,0.06616223353016477
-Non-hospitalised (no AD),21,0.060131086146061996,0.002972953308174973,0.05452983781007721,0.06615821481363794
-Non-hospitalised (no AD),22,0.06012697699512739,0.0029729533081749734,0.05452572865914258,0.06615410566270334
-Non-hospitalised (no AD),23,0.0601227795655218,0.002972953308174973,0.054521531229537006,0.06614990823309774
-Non-hospitalised (no AD),24,0.06011849587025124,0.0029729533081749742,0.05451724753426645,0.0661456245378272
-Non-hospitalised (no AD),25,0.06011412779459189,0.002972953308174974,0.054512879458607104,0.06614125646216783
-Non-hospitalised (no AD),26,0.06010967710897696,0.0029729533081749764,0.05450842877299215,0.06613680577655293
-Non-hospitalised (no AD),27,0.0601051454801377,0.0029729533081749734,0.054503897144152914,0.06613227414771365
-Non-hospitalised (no AD),28,0.06010053448079093,0.0029729533081749742,0.05449928614480614,0.06612766314836688
-Non-hospitalised (no AD),29,0.060095845598107794,0.002972953308174973,0.054494597262123,0.06612297426568374
-Non-hospitalised (no AD),30,0.06009108024115403,0.002972953308174973,0.054489831905169255,0.06611820890872998
-Non-hospitalised (no AD),31,0.06008623974745676,0.0029729533081749725,0.05448499141147197,0.06611336841503272
-Non-hospitalised (no AD),32,0.060081325388824956,0.002972953308174977,0.054480077052840144,0.06610845405640091
-Non-hospitalised (no AD),33,0.060076338376529544,0.0029729533081749725,0.05447509004054478,0.0661034670441055
-Non-hospitalised (no AD),34,0.06007127986593051,0.002972953308174974,0.05447003152994574,0.06609840853350644
-Non-hospitalised (no AD),35,0.06006615096062477,0.002972953308174975,0.05446490262463997,0.06609327962820073
-Non-hospitalised (no AD),36,0.060060952716176584,0.0029729533081749755,0.05445970438019177,0.06608808138375252
-Non-hospitalised (no AD),37,0.06005568614348299,0.0029729533081749764,0.054454437807498215,0.06608281481105895
-Non-hospitalised (no AD),38,0.0600503522118191,0.0029729533081749794,0.05444910387583428,0.06607748087939506
-Non-hospitalised (no AD),39,0.06004495185160074,0.0029729533081749725,0.054443703515615983,0.06607208051917669
-Non-hospitalised (no AD),40,0.060039485956898134,0.002972953308174975,0.054438237620913314,0.0660666146244741
-Non-hospitalised (no AD),41,0.06003395538772788,0.0029729533081749755,0.05443270705174306,0.06606108405530385
-Non-hospitalised (no AD),42,0.060028360972148025,0.002972953308174977,0.0544271126361632,0.06605548963972398
-Non-hospitalised (no AD),43,0.06002270350817776,0.0029729533081749742,0.054421455172192976,0.0660498321757537
-Non-hospitalised (no AD),44,0.060016983765559556,0.0029729533081749742,0.05441573542957477,0.0660441124331355
-Non-hospitalised (no AD),45,0.06001120248738046,0.002972953308174976,0.054409954151395636,0.06603833115495643
-Non-hospitalised (no AD),46,0.060005360391566474,0.0029729533081749734,0.05440411205558169,0.06603248905914244
-Non-hospitalised (no AD),47,0.05999945817226218,0.002972953308174975,0.0543982098362774,0.06602658683983813
-Non-hospitalised (no AD),48,0.05999349650110758,0.0029729533081749742,0.054392248165122795,0.06602062516868354
-Non-hospitalised (no AD),49,0.0599874760284208,0.002972953308174974,0.05438622769243602,0.06601460469599676
-Non-hospitalised (no AD),50,0.05998139738429579,0.002972953308174975,0.054380149048310976,0.06600852605187174
-Non-hospitalised (no AD),51,0.05997526117962301,0.0029729533081749716,0.05437401284363822,0.06600238984719899
-Non-hospitalised (no AD),52,0.059969068007039106,0.0029729533081749725,0.054367819671054314,0.06599619667461504
-Non-hospitalised (no AD),53,0.059962818441812976,0.002972953308174975,0.05436157010582817,0.06598994710938892
-Non-hospitalised (no AD),54,0.0599565130426722,0.0029729533081749734,0.05435526470668741,0.06598364171024815
-Non-hospitalised (no AD),55,0.05995015235257605,0.0029729533081749734,0.05434890401659127,0.065977281020152
-Non-hospitalised (no AD),56,0.05994373689943891,0.0029729533081749734,0.054342488563454125,0.06597086556701486
-Non-hospitalised (no AD),57,0.0599372671968079,0.0029729533081749742,0.054336018860823114,0.06596439586438387
-Non-hospitalised (no AD),58,0.0599307437444992,0.0029729533081749725,0.05432949540851441,0.06595787241207517
-Non-hospitalised (no AD),59,0.059924167029195,0.002972953308174975,0.054322918693210175,0.06595129569677095
-Non-hospitalised (no AD),60,0.05991753752500556,0.0029729533081749742,0.05431628918902077,0.0659446661925815
-Non-hospitalised (no AD),61,0.05991085569399765,0.002972953308174973,0.054309607358012865,0.06593798436157361
-Non-hospitalised (no AD),62,0.0599041219866929,0.0029729533081749695,0.054302873650708135,0.06593125065426884
-Non-hospitalised (no AD),63,0.05989733684253742,0.0029729533081749725,0.05429608850655263,0.06592446551011337
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-Non-hospitalised (no AD),65,0.059883613948719924,0.002972953308174976,0.054282365612735105,0.06591074261629588
-Non-hospitalised (no AD),66,0.05987667702644561,0.002972953308174974,0.05427542869046083,0.06590380569402159
-Non-hospitalised (no AD),67,0.059869690322867805,0.002972953308174976,0.05426844198688298,0.06589681899044375
-Non-hospitalised (no AD),68,0.05986265422824638,0.0029729533081749747,0.0542614058922616,0.06588978289582233
-Non-hospitalised (no AD),69,0.05985556912409298,0.0029729533081749734,0.054254320788108205,0.06588269779166894
-Non-hospitalised (no AD),70,0.05984843538349119,0.0029729533081749742,0.05424718704750637,0.06587556405106713
-Non-hospitalised (no AD),71,0.059841253371400366,0.002972953308174977,0.05424000503541553,0.06586838203897633
-Non-hospitalised (no AD),72,0.05983402344494412,0.0029729533081749725,0.05423277510895936,0.06586115211252007
-Non-hospitalised (no AD),73,0.059826745953685354,0.0029729533081749742,0.05422549761770056,0.06585387462126131
-Non-hospitalised (no AD),74,0.05981942123988732,0.002972953308174975,0.05421817290390251,0.06584654990746329
-Non-hospitalised (no AD),75,0.059812049638762674,0.002972953308174977,0.05421080130277787,0.06583917830633865
-Non-hospitalised (no AD),76,0.059804631478710825,0.0029729533081749742,0.05420338314272604,0.06583176014628678
-Non-hospitalised (no AD),77,0.059797167081543964,0.0029729533081749716,0.05419591874555917,0.06582429574911992
-Non-hospitalised (no AD),78,0.059789656762703064,0.0029729533081749708,0.05418840842671828,0.065816785430279
-Non-hospitalised (no AD),79,0.059782100831463666,0.002972953308174941,0.054180852495478944,0.06580922949903956
-Non-hospitalised (no AD),80,0.05977449959113101,0.002972953308174721,0.05417325125514671,0.06580162825870646
-Non-hospitalised (no AD),81,0.05976685333921586,0.0029729533081732334,0.05416560500323431,0.06579398200678828
-Non-hospitalised (no AD),82,0.0597591623675301,0.002972953308163265,0.05415791403156734,0.06578629103508232
-Non-hospitalised (no AD),83,0.059751426961871974,0.0029729533081019432,0.05415017862602478,0.06577855562929986
-Non-hospitalised (no AD),84,0.05974364739973688,0.002972953307764127,0.05414239906452613,0.06577077606647992
-Non-hospitalised (no AD),85,0.05973582393718804,0.002972953305958905,0.05413457560537844,0.0657629526002713
-Non-hospitalised (no AD),86,0.05972795675695905,0.002972953297509701,0.05412670844106837,0.06575508540291308
-Non-hospitalised (no AD),87,0.05972004575945764,0.002972953261262115,0.05411879751185992,0.0657471743319262
-Non-hospitalised (no AD),88,0.059712089969221845,0.0029729531265658047,0.05411084197540122,0.06573921826861784
-Non-hospitalised (no AD),89,0.05970408573562444,0.00297295267262083,0.05410283859706729,0.06573121311472857
-Non-hospitalised (no AD),90,0.059696020591985074,0.002972951187685736,0.054094776251147704,0.06572314496065011
-Non-hospitalised (no AD),91,0.059687869941636065,0.0029729472985144012,0.05408663292826477,0.06571498642570497
-Non-hospitalised (no AD),92,0.05967957475106672,0.0029729377495929625,0.05407835572851964,0.06570667187641314
-Non-hospitalised (no AD),93,0.05967094591317844,0.002972912202116854,0.05406977502383197,0.06569799124560728
-Non-hospitalised (no AD),94,0.05966167172626026,0.0029728538342118083,0.054060610806057986,0.06568859872824886
-Non-hospitalised (no AD),95,0.059651363725680875,0.002972741917394383,0.05405051366445364,0.06567806383643075
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-Non-hospitalised (no AD),98,0.05960792694467467,0.0029717508566120036,0.0540089441100678,0.06563261785775693
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+Non-hospitalised (no AD),62,0.05980518424610354,0.0030609520073688586,0.053867283280273064,0.06575307694926517
+Non-hospitalised (no AD),63,0.05979840001754861,0.003060952007368863,0.05386049905171813,0.06574629272071024
+Non-hospitalised (no AD),64,0.05979156483500218,0.003060952007368862,0.053853663869171696,0.06573945753816383
+Non-hospitalised (no AD),65,0.05978467911696856,0.0030609520073688616,0.05384677815113808,0.06573257182013025
+Non-hospitalised (no AD),66,0.05977774327213559,0.0030609520073688625,0.05383984230630512,0.06572563597529725
+Non-hospitalised (no AD),67,0.05977075769974987,0.0030609520073688625,0.0538328567339194,0.06571865040291157
+Non-hospitalised (no AD),68,0.059763722789972364,0.0030609520073688616,0.05382582182414188,0.06571161549313403
+Non-hospitalised (no AD),69,0.059756638924215864,0.0030609520073688616,0.05381873795838539,0.06570453162737755
+Non-hospitalised (no AD),70,0.059749506475464655,0.003060952007368861,0.053811605509634174,0.06569739917862635
+Non-hospitalised (no AD),71,0.059742325808578696,0.0030609520073688616,0.053804424842748215,0.06569021851174038
+Non-hospitalised (no AD),72,0.059735097280582246,0.003060952007368862,0.05379719631475177,0.0656829899837439
+Non-hospitalised (no AD),73,0.05972782124093873,0.003060952007368862,0.05378992027510826,0.06567571394410038
+Non-hospitalised (no AD),74,0.05972049803181186,0.0030609520073688608,0.05378259706598139,0.06566839073497356
+Non-hospitalised (no AD),75,0.05971312798831495,0.0030609520073688616,0.05377522702248448,0.06566102069147664
+Non-hospitalised (no AD),76,0.05970571143874809,0.0030609520073688616,0.053767810472917614,0.06565360414190974
+Non-hospitalised (no AD),77,0.05969824870482418,0.0030609520073688608,0.05376034773899371,0.06564614140798586
+Non-hospitalised (no AD),78,0.05969074010188524,0.003060952007368858,0.053752839136054775,0.06563863280504689
+Non-hospitalised (no AD),79,0.059683185939107916,0.0030609520073688265,0.053745284973277505,0.06563107864226951
+Non-hospitalised (no AD),80,0.059675586519698835,0.003060952007368601,0.05373768555386886,0.06562347922286002
+Non-hospitalised (no AD),81,0.05966794214107046,0.0030609520073670666,0.053730041175243465,0.06561583484422867
+Non-hospitalised (no AD),82,0.05966025309493671,0.0030609520073568057,0.053722352129129625,0.06560814579807495
+Non-hospitalised (no AD),83,0.059652519666999394,0.0030609520072936683,0.0537146187013148,0.06560041237001497
+Non-hospitalised (no AD),84,0.05964474213466399,0.003060952006945853,0.05370684116965411,0.0655926348370037
+Non-hospitalised (no AD),85,0.05963692075393597,0.0030609520050871933,0.053699019792531666,0.06558481345266402
+Non-hospitalised (no AD),86,0.0596290557076245,0.0030609519963878994,0.05369115476309586,0.0655769483894485
+Non-hospitalised (no AD),87,0.05962114689674435,0.003060951959067391,0.05368324602461326,0.06556903950604895
+Non-hospitalised (no AD),88,0.05961319334808661,0.003060951820384107,0.05367529274498545,0.06556108568790862
+Non-hospitalised (no AD),89,0.05960519141826775,0.003060951353002472,0.053667291721834126,0.06555308284989654
+Non-hospitalised (no AD),90,0.05959712866416511,0.003060949824113668,0.05365923193360298,0.06554501712493174
+Non-hospitalised (no AD),91,0.05958898053463163,0.0030609458198238206,0.05365109157193926,0.06553686121445745
+Non-hospitalised (no AD),92,0.0595806881041859,0.0030609359882566582,0.053642818213622726,0.06552854967978977
+Non-hospitalised (no AD),93,0.0595720626090189,0.003060909684581541,0.053634243744612786,0.06551987307260371
+Non-hospitalised (no AD),94,0.05956279290583925,0.003060849589000841,0.05362509062006809,0.06551048659462162
+Non-hospitalised (no AD),95,0.05955249121784922,0.0030607343594730705,0.05361501246433945,0.06549996099823127
+Non-hospitalised (no AD),96,0.0595405599591667,0.0030605312297731168,0.05360347525431849,0.06548763502781946
+Non-hospitalised (no AD),97,0.059526266429100264,0.003060200301371463,0.053589823687951926,0.06547269845381742
+Non-hospitalised (no AD),98,0.05950909542852074,0.0030597139635340524,0.0535735961278143,0.06545458242526378
+Non-hospitalised (no AD),99,0.05948499837999192,0.003058851094606883,0.053551172947488464,0.0654288086919051
+Non-hospitalised (no AD),100,0.05946313360449935,0.003058114262249683,0.05353073754349697,0.06540551213970049
+Non-hospitalised (no AD),101,0.05940997771502873,0.0030556764613151872,0.053482310712335224,0.0653476192343096
+Non-hospitalised (no AD),102,0.05936206589542377,0.0030535326197752206,0.05343855770303505,0.06529554160634776
+Non-hospitalised (no AD),103,0.05922340749027942,0.0030464968192796425,0.053313547955712685,0.0651432115767128
+Non-hospitalised (no AD),104,0.05884938845325926,0.0030268943950772093,0.052977555406309454,0.06473110206470405
+Non-hospitalised (no AD),105,0.05787749740401882,0.0029756968363162917,0.05210498183505715,0.06365972641543047
+Non-hospitalised (no AD),106,0.04741897410018828,0.002430409946310288,0.04270425347313574,0.05214162821255264
diff --git a/notebooks/analysis/woldmuyn_long_COVID.py b/notebooks/analysis/woldmuyn_long_COVID.py
index a621ba470..0f8d0e004 100644
--- a/notebooks/analysis/woldmuyn_long_COVID.py
+++ b/notebooks/analysis/woldmuyn_long_COVID.py
@@ -16,6 +16,12 @@
 import matplotlib.cm as cm
 import os
 
+# From Color Universal Design (CUD): https://jfly.uni-koeln.de/color/
+colorscale_okabe_ito = {"orange" : "#E69F00", "light_blue" : "#56B4E9",
+                        "green" : "#009E73", "yellow" : "#F0E442",
+                        "blue" : "#0072B2", "red" : "#D55E00",
+                        "pink" : "#CC79A7", "black" : "#000000"}
+
 if __name__ == '__main__':
     ################################
     ## Define simulation settings ##
@@ -73,9 +79,40 @@
     result_folder = '../../results/covid19_DTM/analysis/QALY/long_COVID'
 
     states = ['QALY_NH', 'QALY_C', 'QALY_ICU','QALY_D']
-    titles = ['Non-hospitalised', 'Cohort', 'ICU','Deaths']
+    titles = ['Non-hospitalised', 'Non-hospitalised (no IC)', 'Non-hospitalised (IC)','Deaths']
     colors = ['green','yellow','red','black']
 
+    age_groups=['0-12','12-18','18-25','25-35','35-45','45-55','55-65','65-75','75-85','85+']
+
+    fig,axes=plt.subplots(nrows=1, ncols=2, figsize=(8.3,0.25*11.7))
+    for ax,scenario,out in zip(axes, ['no_AD','AD'], [out_no_AD,out_AD]):
+      # group data
+      data = [out['QALY_D'].mean(dim="draws").sum(dim='doses').isel(date=-1).values/initN*100000,
+              out['QALY_NH'].mean(dim="draws").sum(dim='doses').isel(date=-1).values/initN*100000,
+              out['QALY_C'].mean(dim="draws").sum(dim='doses').isel(date=-1).values/initN*100000,
+              out['QALY_ICU'].mean(dim="draws").sum(dim='doses').isel(date=-1).values/initN*100000
+              ]
+      # settings
+      colors = ['grey', colorscale_okabe_ito['green'], colorscale_okabe_ito['orange'], colorscale_okabe_ito['red']]
+      hatches = ['....','////','\\\\\\','||||']
+      labels = ['Deaths', 'Non-hospitalised', 'Hospitalised (no IC)', 'Hospitalised (IC)']
+      # plot data
+      bottom=np.zeros(len(age_groups))
+      for d, color, hatch, label in zip(data, colors, hatches, labels):
+        p = ax.bar(age_groups, d, color=color, hatch=hatch, label=label, bottom=bottom, linewidth=0.25, alpha=0.7)
+        bottom += d
+      ax.grid(False)
+      ax.tick_params(axis='both', which='major', labelsize=10)
+      ax.tick_params(axis='x', which='major', rotation=30)
+      ax.set_ylim([0,7000])
+    axes[0].legend(loc=2, framealpha=1, fontsize=10)
+    axes[0].set_ylabel('QALYs lost per 100K inhab.', size=10)
+
+    plt.tight_layout()
+    plt.show()
+    fig.savefig('QALY_losses_per_age_group.pdf')
+    plt.close()
+
     for scenario,out in zip(['no_AD','AD'],[out_no_AD,out_AD]):
 
       # With confidence interval
@@ -95,7 +132,7 @@
           ax.grid(False)
 
       plt.subplots_adjust(hspace=0.5)
-      fig.savefig(os.path.join(abs_dir,result_folder,f'QALY_losses_{scenario}.png'))
+      fig.savefig(os.path.join(abs_dir,result_folder,f'QALY_losses_{scenario}.pdf'))
 
       # QALYS per age group
       Palette=cm.get_cmap('tab10_r', initN.size).colors
@@ -113,4 +150,4 @@
       axs[0].legend(fancybox=True, frameon=True, framealpha=1, fontsize=15,title='Age Group', loc="upper left", bbox_to_anchor=(1,1)) 
 
       plt.subplots_adjust(hspace=0.5)
-      fig.savefig(os.path.join(abs_dir,result_folder,f'QALY_losses_per_age_group_{scenario}.png'), dpi=600)
\ No newline at end of file
+      fig.savefig(os.path.join(abs_dir,result_folder,f'QALY_losses_per_age_group_{scenario}.pdf'))
\ No newline at end of file
diff --git a/notebooks/calibration/plot_fit_BASE-COVID19_SEIQRD_hybrid_vacc.py b/notebooks/calibration/plot_fit_BASE-COVID19_SEIQRD_hybrid_vacc.py
index 216ea1ab3..a1d286160 100644
--- a/notebooks/calibration/plot_fit_BASE-COVID19_SEIQRD_hybrid_vacc.py
+++ b/notebooks/calibration/plot_fit_BASE-COVID19_SEIQRD_hybrid_vacc.py
@@ -68,7 +68,7 @@
 ################################
 
 # Start and end of simulation
-end_sim = datetime(2022,1,1)
+end_sim = datetime(2021,7,1)
 # Confidence level used to visualise model fit
 conf_int = 0.05
 
@@ -146,7 +146,7 @@
 
 print('2) Visualizing fit')
 
-fig,(ax1,ax2,ax3,ax4,ax5) = plt.subplots(nrows=5,ncols=1,figsize=(8.3,11.7),sharex=True)
+fig,(ax1,ax2,ax3,ax4,ax5) = plt.subplots(nrows=5,ncols=1,figsize=(8.3,0.75*11.7),sharex=True)
 
 # Plot mildly sick
 ax1.plot(df_2plot['M_in','mean'], color='blue', linewidth=1.5)
@@ -154,8 +154,9 @@
 ax1.scatter(df_cases[start_calibration:end_sim].index,df_cases[start_calibration:end_sim], color='black', alpha=0.20, linestyle='None', facecolors='black', s=10)
 ax1 = _apply_tick_locator(ax1)
 ax1.set_xlim(start_sim,end_sim)
-ax1.set_ylabel('Incidence\nMild cases (-)', fontsize=13)
+ax1.set_ylabel('Incidence\nMild cases (-)', fontsize=10)
 ax1.get_yaxis().set_label_coords(-0.1,0.5)
+ax1.tick_params(axis='both', which='major', labelsize=10)
 ax1.grid(False)
 # Plot hospitalizations
 ax2.plot(df_2plot['H_in','mean'], color='blue', linewidth=1.5)
@@ -164,24 +165,27 @@
 ax2.scatter(df_hosp[pd.to_datetime(end_calibration)+timedelta(days=1):end_sim].index,df_hosp['H_in'][pd.to_datetime(end_calibration)+timedelta(days=1):end_sim], color='black', alpha=0.2, linestyle='None', facecolors='black', s=10)
 ax2 = _apply_tick_locator(ax2)
 ax2.set_xlim(start_sim,end_sim)
-ax2.set_ylabel('Incidence\nHospital (-)', fontsize=13)
+ax2.set_ylabel('Incidence\nHospital (-)', fontsize=10)
 ax2.get_yaxis().set_label_coords(-0.1,0.5)
+ax2.tick_params(axis='both', which='major', labelsize=10)
 ax2.grid(False)
 # Plot hospital total
 ax3.plot(simtime, df_2plot['H_tot', 'mean'], color='blue', linewidth=1.5)
 ax3.fill_between(simtime, df_2plot['H_tot', 'lower'], df_2plot['H_tot', 'upper'], alpha=0.20, color = 'blue')
 ax3.scatter(df_hosp[start_calibration:end_sim].index,df_hosp['H_tot'][start_calibration:end_sim], color='black', alpha=0.2, linestyle='None', facecolors='black', s=10)
 ax3 = _apply_tick_locator(ax3)
-ax3.set_ylabel('Load\nHospital (-)', fontsize=13)
+ax3.set_ylabel('Load\nHospital (-)', fontsize=10)
 ax3.get_yaxis().set_label_coords(-0.1,0.5)
+ax3.tick_params(axis='both', which='major', labelsize=10)
 ax3.grid(False)
 # Plot ICU
 ax4.plot(simtime, df_2plot['ICU_R', 'mean']+df_2plot['ICU_D', 'mean']+df_2plot['C_icurec', 'mean'], color='blue', linewidth=1.5)
 ax4.fill_between(simtime, df_2plot['ICU_R', 'lower']+df_2plot['ICU_D', 'lower']+df_2plot['C_icurec', 'lower'], df_2plot['ICU_R', 'upper']+df_2plot['ICU_D', 'upper']+df_2plot['C_icurec', 'upper'], alpha=0.20, color = 'blue')
 ax4.scatter(df_hosp[start_calibration:end_sim].index,df_hosp['ICU_tot'][start_calibration:end_sim], color='black', alpha=0.2, linestyle='None', facecolors='black', s=10)
 ax4 = _apply_tick_locator(ax4)
-ax4.set_ylabel('Load\nIntensive Care (-)', fontsize=13)
+ax4.set_ylabel('Load\nIntensive Care (-)', fontsize=10)
 ax4.get_yaxis().set_label_coords(-0.1,0.5)
+ax4.tick_params(axis='both', which='major', labelsize=10)
 ax4.grid(False)
 # Plot fraction of immunes
 ax5.plot(df_2plot['R','mean'][start_calibration:'2021-03-01']/sum(initN)*100, color='blue', linewidth=1.5)
@@ -190,12 +194,13 @@
 ax5.errorbar(x=df_sero_herzog.index,y=df_sero_herzog['rel','mean'].values*100,yerr=yerr, fmt='x', color='black', elinewidth=1, capsize=5)
 yerr = np.array([df_sero_sciensano['rel','mean']*100 - df_sero_sciensano['rel','LL']*100, df_sero_sciensano['rel','UL']*100 - df_sero_sciensano['rel','mean']*100 ])
 ax5.errorbar(x=df_sero_sciensano.index,y=df_sero_sciensano['rel','mean']*100,yerr=yerr, fmt='^', color='black', elinewidth=1, capsize=5)
-ax5.legend(['model (mean)', 'model (95% CI)', 'Herzog et al. 2020', 'Sciensano'], loc='upper right', fontsize=13)
+ax5.legend(['model (mean)', 'model (95% CI)', 'Herzog et al. 2020', 'Sciensano'], loc=2, fontsize=8)
 ax5.axvline(x=pd.Timestamp('2020-12-27'), linewidth=1.5, linestyle='--', color='black')
 ax5 = _apply_tick_locator(ax5)
+ax5.tick_params(axis='both', which='major', labelsize=10)
 ax5.set_xlim(start_sim,end_sim)
 ax5.set_ylim(0,35)
-ax5.set_ylabel('Seroprelevance (%)', fontsize=13)
+ax5.set_ylabel('Seroprelevance (%)', fontsize=10)
 ax5.get_yaxis().set_label_coords(-0.1,0.5)
 ax5.grid(False)
 
diff --git a/notebooks/preprocessing/woldmuyn_long_covid_average_QALY_loss.py b/notebooks/preprocessing/woldmuyn_long_covid_average_QALY_loss.py
index 0f6862f4d..6502ca2e5 100644
--- a/notebooks/preprocessing/woldmuyn_long_covid_average_QALY_loss.py
+++ b/notebooks/preprocessing/woldmuyn_long_covid_average_QALY_loss.py
@@ -36,10 +36,11 @@
 from covid19_DTM.visualization.output import _apply_tick_locator 
 from covid19_DTM.models.QALY import life_table_QALY_model, lost_QALYs_hospital_care
 
-import copy
 import emcee
 from tqdm import tqdm
 
+import sys
+
 ###############
 ## Load data ##
 ###############
@@ -54,8 +55,8 @@
 # --------------- #
 
 severity_groups = ['Mild','Moderate','Severe-Critical']
-hospitalisation_groups = ['Non-hospitalised','Cohort','ICU']
-color_dict = {'Mild':'g','Non-hospitalised':'g','Non-hospitalised (no AD)':'g','Moderate':'y','Cohort':'y','Severe-Critical':'r','ICU':'r'}
+hospitalisation_groups = ['Non-hospitalised','Hospitalised (no IC)','Hospitalised (IC)']
+color_dict = {'Mild':'g','Non-hospitalised':'g','Non-hospitalised (no AD)':'g','Moderate':'y','Hospitalised (no IC)':'y','Severe-Critical':'r','Hospitalised (IC)':'r'}
 
 # raw prevalence data per severity group
 prevalence_data_per_severity_group = pd.read_csv(os.path.join(abs_dir,rel_dir,'Long_COVID_prevalence.csv'),index_col=[0,1])
@@ -91,7 +92,7 @@
 sd_QoL_decrease_non_hospitalised =  0.33/np.sqrt(1146) #(0.66-0.64)/1.96
 QoL_difference_data = pd.DataFrame(data=np.array([[mean_QoL_decrease_non_hospitalised,mean_QoL_decrease_hospitalised,mean_QoL_decrease_hospitalised],
                                                   [sd_QoL_decrease_non_hospitalised,sd_QoL_decrease_hospitalised,sd_QoL_decrease_hospitalised]]).transpose(),
-                                                  columns=['mean','sd'],index=['Non-hospitalised','Cohort','ICU'])
+                                                  columns=['mean','sd'],index=['Non-hospitalised','Hospitalised (no IC)','Hospitalised (IC)'])
 
 # ------- #
 # results #
@@ -157,11 +158,15 @@ def WSSE(theta,x,y):
 
 prevalence_func = lambda t,tau, p_AD: p_AD + (1-p_AD)*np.exp(-t/tau)
 
-for scenario,(fig,ax) in zip(('AD','no_AD'),(plt.subplots(),plt.subplots())):
-    for hospitalisation in hospitalisation_groups:
+fig,axes=plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(8.3,0.3*11.7))
+
+for ax, scenario, in zip(axes, ('AD','no_AD')):
+    markers = ['o', 's', '^']
+    linestyles = ['-', '--', '-.']
+    for (hospitalisation, marker, linestyle) in zip(hospitalisation_groups, markers, linestyles):
         x = prevalence_data_per_hospitalisation_group.loc[hospitalisation].index.values
         y = prevalence_data_per_hospitalisation_group.loc[hospitalisation].values.squeeze()
-        ax.plot(x,y,color_dict[hospitalisation]+'o',label=f'{hospitalisation} data')
+        ax.scatter(x,100*y,label=f'{hospitalisation} data', color='black', marker=marker)
 
         if hospitalisation =='Non-hospitalised' and scenario == 'no_AD':
             tau = taus['Non-hospitalised (no AD)']
@@ -169,15 +174,17 @@ def WSSE(theta,x,y):
         else:
             tau = taus[hospitalisation]
             p_AD = p_ADs[hospitalisation]
-        ax.plot(time,prevalence_func(time,tau,p_AD),color_dict[hospitalisation]+'--',alpha=0.7,
-            label=f'{hospitalisation} fit\n'rf'($\tau$:{tau:.2f},'' $f_{AD}$'f':{p_AD:.2f})')
+        ax.plot(time,100*prevalence_func(time,tau,p_AD),color='black',alpha=0.8, linewidth=1.5,
+            label=f'{hospitalisation} fit\n'rf'($\tau$:{tau:.2f},'' $f_{AD}$'f':{p_AD:.2f})', linestyle=linestyle)
     
-    ax.set_xlabel('Months after infection')
-    ax.set_ylabel('Prevalence')
-    ax.set_title('Prevalence of long-COVID symptoms')
-    ax.legend(prop={'size': 12})
+    ax.set_xlabel('Months after infection', size=10)
+    ax.legend(prop={'size': 8})
     ax.grid(False)
-    fig.savefig(os.path.join(abs_dir,fig_result_folder,f'prevalence_first_fit_{scenario}'))
+    ax.tick_params(axis='both', which='major', labelsize=10)
+
+axes[0].set_ylabel('Symptom prevalence (%)', size=10)
+plt.tight_layout()
+fig.savefig(os.path.join(abs_dir,fig_result_folder,f'prevalence_first_fit.pdf'))
 
 print('\n(2.2) MCMC to estimate f_AD\n')
 # objective functions for MCMC
@@ -216,63 +223,60 @@ def log_probability(theta, tau, x, y, bounds):
 p_AD_summary = pd.DataFrame(index=hospitalisation_groups,columns=['mean','sd','lower','upper'],dtype='float64')
 
 for hospitalisation in hospitalisation_groups:
-    
+    # slice data
     x = prevalence_data_per_hospitalisation_group.loc[hospitalisation].index.values
     y = prevalence_data_per_hospitalisation_group.loc[hospitalisation].values.squeeze()
-
+    # set parameters
     tau = taus[hospitalisation]
     p_AD = p_ADs[hospitalisation]
-    
+    # setup sampler
     nwalkers = 32
     ndim = 1
     pos = p_AD + p_AD*1e-1 * np.random.randn(nwalkers, ndim)
-
     bounds = (0,1)
     sampler = emcee.EnsembleSampler(
         nwalkers, ndim, log_probability, args=(tau,x,y,bounds)
     )
     samplers.update({hospitalisation:sampler})
-    sampler.run_mcmc(pos, 20000, progress=True)
-
-    flat_samples = sampler.get_chain(discard=1000, thin=30, flat=True)
+    # run sampler
+    sampler.run_mcmc(pos, 25000, progress=True)
+    # extract chains
+    flat_samples = sampler.get_chain(discard=5000, thin=100, flat=True)
+    # print results
     p_AD_summary['mean'][hospitalisation] = np.mean(flat_samples,axis=0)
     p_AD_summary['sd'][hospitalisation] = np.std(flat_samples,axis=0)
     p_AD_summary['lower'][hospitalisation] = np.quantile(flat_samples,0.025,axis=0)
     p_AD_summary['upper'][hospitalisation] = np.quantile(flat_samples,0.975,axis=0)
 
 # visualise MCMC results    
-fig,axs = plt.subplots(2,2,figsize=(10,10),sharex=True,sharey=True)
-axs = axs.reshape(-1)
-for ax,hospitalisation in zip(axs[:-1],hospitalisation_groups):
+fig,axes = plt.subplots(nrows=1,ncols=3,figsize=(8.3,0.25*11.7),sharey=True)
+
+for ax,hospitalisation in zip(axes,hospitalisation_groups):
     x = prevalence_data_per_hospitalisation_group.loc[hospitalisation].index.values
     y = prevalence_data_per_hospitalisation_group.loc[hospitalisation].values.squeeze()
-    ax.plot(x,y,color_dict[hospitalisation]+'o',label=f'{hospitalisation} data')
-    axs[-1].plot(x,y,color_dict[hospitalisation]+'o',label=f'{hospitalisation} data')
+    ax.scatter(x,100*y,color='black',marker='o',label=f'{hospitalisation} data', alpha=0.8)
     
     tau = taus[hospitalisation]
     prevalences = []
-    for n in range(200):
+    for n in range(1000):
         prevalences.append(prevalence_func(time,tau,np.random.normal(p_AD_summary.loc[hospitalisation]['mean'],
                                                 p_AD_summary.loc[hospitalisation]['sd'])))
     mean = np.mean(prevalences,axis=0)
     lower = np.quantile(prevalences,0.025,axis=0)
     upper = np.quantile(prevalences,0.975,axis=0)
 
-    ax.plot(time,mean,color_dict[hospitalisation]+'--',alpha=0.7)
-    axs[-1].plot(time,mean,color_dict[hospitalisation]+'--',alpha=0.7,label=f'{hospitalisation} mean fit')
-    ax.fill_between(time,lower, upper,alpha=0.2, color=color_dict[hospitalisation])
-    ax.set_title(hospitalisation)
-    ax.grid(False)
+    ax.plot(time,100*mean,color='black',linestyle='--',linewidth=1.5,alpha=1)
+    ax.plot(time,100*lower,color='black',linestyle='-',linewidth=1,alpha=1)
+    ax.plot(time,100*upper,color='black',linestyle='-',linewidth=1,alpha=1)
 
-axs[-1].set_xlabel('Months after infection')
-axs[-2].set_xlabel('Months after infection')
-axs[0].set_ylabel('Prevalence')
-axs[-2].set_ylabel('Prevalence')
-axs[-1].legend(prop={'size': 12})
-axs[-1].grid(False)
+    ax.grid(False)
+    ax.set_xlabel('Months after infection',size=10)
+    ax.set_title(hospitalisation, size=10)
+    ax.tick_params(axis='both', which='major', labelsize=10)
 
-fig.suptitle('Prevalence of long-COVID symptoms')
-fig.savefig(os.path.join(abs_dir,fig_result_folder,'prevalence_MCMC_fit.png'))
+axes[0].set_ylabel('Symptom prevalence (%)', size=10)
+plt.tight_layout()
+fig.savefig(os.path.join(abs_dir,fig_result_folder,'prevalence_MCMC_fit.pdf'))
 
 #########
 ## QoL ##
@@ -283,18 +287,20 @@ def log_probability(theta, tau, x, y, bounds):
 QoL_Belgium_func = Life_table.QoL_Belgium_func
 
 # visualise fit
-fig,ax = plt.subplots()
+fig,ax = plt.subplots(figsize=(0.75*8.3,0.25*11.7))
 for index in QoL_Belgium.index:
     left = index.left
     right = index.right
     w = right-left
     ax.bar(left+w/2,QoL_Belgium[index],w-1,color='grey',alpha=0.5,label='data')
-ax.plot(QoL_Belgium_func(LE_table.index.values),label='fit',color='b')
-ax.set_xlabel('Age')
-ax.set_ylabel('QoL Belgium')
+ax.plot(QoL_Belgium_func(LE_table.index.values),label='fit',color='black')
+ax.set_xlabel('Age (years)',size=10)
+ax.set_ylabel('QoL (-)',size=10)
 ax.set_ylim([0.5, 0.9])
 ax.grid(False)
-fig.savefig(os.path.join(abs_dir,fig_result_folder,'QoL_Belgium_fit.png'))
+ax.tick_params(axis='both', which='major', labelsize=10)
+plt.tight_layout()
+fig.savefig(os.path.join(abs_dir,fig_result_folder,'QoL_Belgium_fit.pdf'))
 
 #######################
 ## Average QALY loss ##
@@ -309,7 +315,7 @@ def QALY_loss_func(t,tau,p_AD,age,QoL_after):
     beta = QoL_Belgium_func(age+t/12)-QoL_after
     return prevalence_func(t,tau,p_AD) * max(0,beta)
 
-draws = 200
+draws = 1000
 
 # Pre-allocate new multi index series with index=hospitalisation,age,draw
 multi_index = pd.MultiIndex.from_product([hospitalisation_groups+['Non-hospitalised (no AD)'],np.arange(draws),LE_table.index.values],names=['hospitalisation','draw','age'])
@@ -337,7 +343,7 @@ def QALY_loss_func(t,tau,p_AD,age,QoL_after):
     QoL_after = QoL_Belgium_func(age)-beta
     # integrate QALY_loss_func from 0 to LE  
     QALY_loss = quad(QALY_loss_func,0,LE,args=(tau,p_AD,age,QoL_after))[0]/12 
-    average_QALY_losses[idx] = QALY_loss
+    average_QALY_losses.iloc[idx] = QALY_loss
 
 # save result to dataframe
 def get_lower(x):
@@ -360,29 +366,20 @@ def get_sd(x):
 average_QALY_losses_summary.to_csv(os.path.join(abs_dir,data_result_folder,'average_QALY_losses.csv'))
 
 # Visualise results
-fig,axs = plt.subplots(2,2,sharex=True,sharey=True,figsize=(10,10))
-axs = axs.reshape(-1)
-for ax,hospitalisation in zip(axs,['Non-hospitalised (no AD)']+hospitalisation_groups):
+fig,axes = plt.subplots(nrows=1,ncols=3,figsize=(8.3,0.25*11.7),sharey=True)
+
+for ax,hospitalisation in zip(axes,hospitalisation_groups):
     mean = average_QALY_losses_summary.loc[hospitalisation]['mean']
     lower = average_QALY_losses_summary.loc[hospitalisation]['lower']
     upper = average_QALY_losses_summary.loc[hospitalisation]['upper']
-    ax.plot(LE_table.index.values,mean,color_dict[hospitalisation],label=f'{hospitalisation}')
-    ax.fill_between(LE_table.index.values,lower,upper,alpha=0.20, color = color_dict[hospitalisation])
-    ax.set_title(hospitalisation)
+    ax.plot(LE_table.index.values,mean,color='black',linewidth=1.5, linestyle='--')
+    ax.plot(LE_table.index.values,lower,color='black',linewidth=1, linestyle='-')
+    ax.plot(LE_table.index.values,upper,color='black',linewidth=1, linestyle='-')
+    ax.set_title(hospitalisation, size=10)
     ax.grid(False)
+    ax.set_xlabel('Age of infection (years)', size=10)
+    ax.tick_params(axis='both', which='major', labelsize=10)
 
-axs[-1].set_xlabel('Age when infected')
-axs[-2].set_xlabel('Age when infected')
-axs[0].set_ylabel('Average QALY loss')
-axs[-2].set_ylabel('Average QALY loss')
-
-fig.suptitle('Average QALY loss related to long-COVID')
-fig.savefig(os.path.join(abs_dir,fig_result_folder,'average_QALY_losses.png'))
-
-# QALY losses due COVID death
-QALY_D_per_age = Life_table.compute_QALY_D_x()
-fig,ax = plt.subplots(figsize=(12,4))
-ax.plot(QALY_D_per_age,'b')
-ax.set(xlabel='age', ylabel=r'$QALY_D$')
-ax.grid(False)
-fig.savefig(os.path.join(abs_dir,fig_result_folder,'QALY_D.png'))
\ No newline at end of file
+axes[0].set_ylabel('Average QALY loss', size=10)
+plt.tight_layout()
+fig.savefig(os.path.join(abs_dir,fig_result_folder,'average_QALY_losses_per_age.pdf'))
diff --git a/results/covid19_DTM/preprocessing/QALY_model/long_COVID/QALY_D.png b/results/covid19_DTM/preprocessing/QALY_model/long_COVID/QALY_D.png
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diff --git a/results/covid19_DTM/preprocessing/QALY_model/long_COVID/QoL_Belgium_fit.pdf b/results/covid19_DTM/preprocessing/QALY_model/long_COVID/QoL_Belgium_fit.pdf
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diff --git a/results/covid19_DTM/preprocessing/QALY_model/long_COVID/QoL_Belgium_fit.png b/results/covid19_DTM/preprocessing/QALY_model/long_COVID/QoL_Belgium_fit.png
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diff --git a/results/covid19_DTM/preprocessing/QALY_model/long_COVID/QoL_decrease.pdf b/results/covid19_DTM/preprocessing/QALY_model/long_COVID/QoL_decrease.pdf
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diff --git a/results/covid19_DTM/preprocessing/QALY_model/long_COVID/average_QALY_losses_per_age.pdf b/results/covid19_DTM/preprocessing/QALY_model/long_COVID/average_QALY_losses_per_age.pdf
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diff --git a/results/covid19_DTM/preprocessing/QALY_model/long_COVID/prevalence_MCMC_fit.pdf b/results/covid19_DTM/preprocessing/QALY_model/long_COVID/prevalence_MCMC_fit.pdf
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diff --git a/results/covid19_DTM/preprocessing/QALY_model/long_COVID/prevalence_first_fit.pdf b/results/covid19_DTM/preprocessing/QALY_model/long_COVID/prevalence_first_fit.pdf
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diff --git a/results/covid19_DTM/preprocessing/QALY_model/long_COVID/prevalence_first_fit_AD.png b/results/covid19_DTM/preprocessing/QALY_model/long_COVID/prevalence_first_fit_AD.png
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diff --git a/results/covid19_DTM/preprocessing/QALY_model/long_COVID/prevalence_first_fit_no_AD.png b/results/covid19_DTM/preprocessing/QALY_model/long_COVID/prevalence_first_fit_no_AD.png
deleted file mode 100644
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diff --git a/src/covid19_DTM/data/model_parameters.py b/src/covid19_DTM/data/model_parameters.py
index f91b3956d..ec844bc11 100644
--- a/src/covid19_DTM/data/model_parameters.py
+++ b/src/covid19_DTM/data/model_parameters.py
@@ -411,7 +411,7 @@ def errorfcn(multiplier, n, n_desired):
     if not agg:
         # Set the average values for beta, seasonality, contact effectivities and mentality according to 'BASE' calibration dictionary
         samples_path = '../../../data/covid19_DTM/interim/model_parameters/calibrations/national/'
-        base_dict_name = 'national_REF_SAMPLES_2023-06-09.json'
+        base_dict_name = 'national_REF_SAMPLES_2023-06-28.json'
         base_samples_dict = load_samples_dict(os.path.join(abs_dir, samples_path+base_dict_name), age_stratification_size=age_stratification_size)
         pars_dict.update({
             'eff_home': 1,
diff --git a/src/covid19_DTM/data/utils.py b/src/covid19_DTM/data/utils.py
index c966e8bdf..602d825a2 100644
--- a/src/covid19_DTM/data/utils.py
+++ b/src/covid19_DTM/data/utils.py
@@ -39,10 +39,10 @@ def construct_initN(age_classes=None, agg=None):
 
     if age_classes is not None:
         age_struct['age_class'] = pd.cut(age_struct.age, bins=age_classes)
-        age_piramid = age_struct.groupby(['NIS','age_class']).sum().reset_index()
+        age_piramid = age_struct.groupby(['NIS','age_class'], observed=False).sum().reset_index()
         initN = age_piramid.pivot(index='NIS', columns='age_class', values='number')
     else:
-        age_piramid = age_struct.groupby(['NIS','age']).sum().reset_index()
+        age_piramid = age_struct.groupby(['NIS','age'], observed=False).sum().reset_index()
         initN = age_piramid.pivot(index='NIS', columns='age', values='number')
         initN = initN.fillna(0)
 
diff --git a/src/covid19_DTM/models/QALY.py b/src/covid19_DTM/models/QALY.py
index ddd05ea88..9b360c71d 100644
--- a/src/covid19_DTM/models/QALY.py
+++ b/src/covid19_DTM/models/QALY.py
@@ -56,7 +56,7 @@ def __init__(self, comorbidity_parameters=None):
         # Define absolute path
         abs_dir = os.path.dirname(__file__)
         # Import life table (q_x)
-        self.life_table = pd.read_csv(os.path.join(abs_dir, '../../../data/covid19_DTM/interim/QALY_model/Life_table_Belgium_2019.csv'),sep=',',index_col=0)
+        self.life_table = pd.read_csv(os.path.join(abs_dir, '../../../data/covid19_DTM/interim/QALY_model/life_table_model/Life_table_Belgium_2019.csv'),sep=',',index_col=0)
         # Define mu_x explictly to enhance readability of the code
         self.mu_x = self.life_table['mu_x']
         # Define overall Belgian QoL scores (source: ...)
@@ -460,9 +460,9 @@ def lost_QALYs(out,AD_non_hospitalised=False):
     out_enlarged = xr.concat([out]*int(np.ceil(100/sim_draws)), dim='draws')
 
     if AD_non_hospitalised:
-        hospitalisation_groups = ['Non-hospitalised','Cohort','ICU']
+        hospitalisation_groups = ['Non-hospitalised','Hospitalised (no IC)','Hospitalised (IC)']
     else:
-        hospitalisation_groups = ['Non-hospitalised (no AD)','Cohort','ICU']
+        hospitalisation_groups = ['Non-hospitalised (no AD)','Hospitalised (no IC)','Hospitalised (IC)']
 
     # Load average QALY losses
     abs_dir = os.path.dirname(__file__)
@@ -474,7 +474,7 @@ def lost_QALYs(out,AD_non_hospitalised=False):
     multi_index = pd.MultiIndex.from_product([hospitalisation_groups,ages,np.arange(QALY_draws)],names=['hospitalisation','age','draw'])
     QALY_long_COVID_per_age = pd.Series(index = multi_index, dtype=float)
     for idx,(hospitalisation,age,draw) in enumerate(QALY_long_COVID_per_age.index):
-        QALY_long_COVID_per_age[idx] = np.random.normal(average_QALY_losses['mean'][hospitalisation,age],
+        QALY_long_COVID_per_age.iloc[idx] = np.random.normal(average_QALY_losses['mean'][hospitalisation,age],
                                                         average_QALY_losses['sd'][hospitalisation,age])
         
     # bin data