diff --git a/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age.csv b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age.csv
deleted file mode 100644
index fe0c1f79a..000000000
--- a/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age.csv
+++ /dev/null
@@ -1,425 +0,0 @@
-hospitalisation,age,mean,sd,lower,upper
-Non-hospitalised,0,0.8215390373308605,0.29465763266328043,0.28036124200002444,1.3797523076334535
-Non-hospitalised,1,0.810249580714112,0.29039818992861366,0.2769852774180494,1.3604003061293022
-Non-hospitalised,2,0.7981948554741376,0.285841919474182,0.27336024506043904,1.3397172228972618
-Non-hospitalised,3,0.7861726683690834,0.2812976613337425,0.26974367302420305,1.3190924627289895
-Non-hospitalised,4,0.7742310181904402,0.276783850148402,0.2661507395802473,1.2986074017144333
-Non-hospitalised,5,0.7623597824208911,0.27229641074371363,0.2625778752224441,1.2782420165438027
-Non-hospitalised,6,0.7505922324068292,0.26784813880903346,0.2590356229257264,1.2580539885393545
-Non-hospitalised,7,0.7389196539971072,0.2634355489131123,0.2555209651842476,1.2380278926619026
-Non-hospitalised,8,0.7273542025615221,0.259063273581703,0.25203767740157956,1.218184709131199
-Non-hospitalised,9,0.7159051930829388,0.2547348558457339,0.24858864057367006,1.1985405009581087
-Non-hospitalised,10,0.704568700036771,0.2504487039642586,0.24517247189117855,1.1790882910847285
-Non-hospitalised,11,0.6933479110049804,0.24620599839128626,0.2417901124386,1.1598335208408852
-Non-hospitalised,12,0.6822595958667521,0.24201320806696178,0.23844691263031265,1.140805310123417
-Non-hospitalised,13,0.6712855507910495,0.23786321376135258,0.23513692718204116,1.1219718783957662
-Non-hospitalised,14,0.6604554956016628,0.23376753369291944,0.23186971738301207,1.10338493008675
-Non-hospitalised,15,0.6497375691042816,0.22971375050461335,0.22863495739734038,1.0849889390772687
-Non-hospitalised,16,0.6391690977596199,0.2257163523277481,0.22544467946264515,1.0668488728915135
-Non-hospitalised,17,0.6287199619089534,0.22176360952483382,0.2222891510867207,1.0489122747100692
-Non-hospitalised,18,0.6184072718800155,0.21786216044036844,0.21917389236395854,1.031208929548278
-Non-hospitalised,19,0.6082509363286763,0.2140197339549186,0.21610531778646627,1.0137734845239275
-Non-hospitalised,20,0.598200152231286,0.2102165763688773,0.21306708176341793,0.9965175450927654
-Non-hospitalised,21,0.5882756723827264,0.2064607556955396,0.21006589820754556,0.9794772599247389
-Non-hospitalised,22,0.5785078412994047,0.20276406566922078,0.20711154608006413,0.9627054419513457
-Non-hospitalised,23,0.5688423364657366,0.1991054066250663,0.20418660929655075,0.9461076457203343
-Non-hospitalised,24,0.559299452502155,0.1954926778626316,0.20129765905211638,0.9297192286101269
-Non-hospitalised,25,0.5498785228893656,0.1919256370900449,0.19844453421813824,0.9135390915604021
-Non-hospitalised,26,0.5405653281882314,0.1883987861595056,0.19562274317967335,0.8975425899679833
-Non-hospitalised,27,0.5313920257941709,0.18492462748434868,0.19284263386094147,0.8817856748317611
-Non-hospitalised,28,0.5223203972698036,0.1814883584027685,0.19009205143212551,0.8662020138746829
-Non-hospitalised,29,0.5133651678985742,0.1780957221096589,0.18737577383266538,0.8508172732403577
-Non-hospitalised,30,0.504521799341788,0.17474499095773946,0.18469242488322107,0.8356236534864001
-Non-hospitalised,31,0.4957973572704282,0.17143893481701297,0.18204432492666953,0.8206335018920143
-Non-hospitalised,32,0.48716238573097215,0.1681661843613463,0.17939377719391136,0.8057957857426612
-Non-hospitalised,33,0.47862334494482295,0.1649292868911291,0.17675354775773436,0.7911218246594802
-Non-hospitalised,34,0.4701890260689875,0.1617316860536543,0.17414676953091013,0.776626966349164
-Non-hospitalised,35,0.4618373685210375,0.1585648973442301,0.1715669232946057,0.7622730547589391
-Non-hospitalised,36,0.4535833572093073,0.155434760235213,0.16901822859732585,0.7480861768957078
-Non-hospitalised,37,0.44540930948113655,0.1523344927312203,0.16649541316375474,0.7340357867203892
-Non-hospitalised,38,0.43733100049974966,0.14927023198289396,0.16400295074401017,0.7201493213119681
-Non-hospitalised,39,0.4293281995354428,0.14623422028009536,0.16153480875983017,0.7063918334141682
-Non-hospitalised,40,0.4214224334707542,0.1432348026790002,0.15909717271329082,0.6928006985199666
-Non-hospitalised,41,0.4135860735160336,0.14026137772728842,0.1566818242978751,0.6793281732947786
-Non-hospitalised,42,0.4058014658770356,0.1373072053472055,0.15428342724158417,0.6659438204324394
-Non-hospitalised,43,0.39811088562422303,0.13438860300398603,0.15191431258083338,0.6527208900038631
-Non-hospitalised,44,0.390460410151424,0.13148487872982212,0.149558447784905,0.6395661966249212
-Non-hospitalised,45,0.3828775544216494,0.12860666389251516,0.1472238316247107,0.626527437341959
-Non-hospitalised,46,0.37538356567990244,0.1257621555057488,0.14491667174133224,0.6136414190496952
-Non-hospitalised,47,0.3679168179491672,0.12292773840486991,0.14261856474817317,0.6008017091694056
-Non-hospitalised,48,0.3605119432856676,0.12011675236057387,0.14033968122711746,0.5880682588416734
-Non-hospitalised,49,0.3531379616026996,0.11731736315277197,0.1380706765317534,0.5753876386072225
-Non-hospitalised,50,0.3458385656403181,0.1145463553642724,0.13582449286245354,0.5628353946963154
-Non-hospitalised,51,0.3385618545904483,0.11178388612546244,0.13358551764958249,0.550321982563712
-Non-hospitalised,52,0.33135481922366394,0.10904798141564866,0.13136773176880925,0.5379286007722702
-Non-hospitalised,53,0.3241749003208389,0.10632238793707893,0.12915828661098816,0.5255818592343968
-Non-hospitalised,54,0.31704325670912287,0.10361523209026473,0.12696345028274256,0.5133183417201846
-Non-hospitalised,55,0.3099551815911332,0.1009247413296398,0.12478173544090514,0.501129985486209
-Non-hospitalised,56,0.3028779702004582,0.0982384548509314,0.12260319352244585,0.48896045449766096
-Non-hospitalised,57,0.29582956476274375,0.09556325897401566,0.12043314855159899,0.47684076385310425
-Non-hospitalised,58,0.28881191908110476,0.09289992572273993,0.11827211919644792,0.46477434040520327
-Non-hospitalised,59,0.2818361148344583,0.09025270310541998,0.1161234037259792,0.45278033193629663
-Non-hospitalised,60,0.27484680394061467,0.08760051314867955,0.11397014017168997,0.4407634185486647
-Non-hospitalised,61,0.26794380858012273,0.08498142150581658,0.11184259326643163,0.42889563156866717
-Non-hospitalised,62,0.2610917353145221,0.08238194136308324,0.10972999940014931,0.41711600127070186
-Non-hospitalised,63,0.25432520161373046,0.079815256335934,0.1076428965507532,0.4054841522054895
-Non-hospitalised,64,0.247586218898992,0.07725929159838732,0.10556359741017085,0.39390023399428786
-Non-hospitalised,65,0.2408863488939791,0.07471845931246644,0.10349559690834549,0.38238418027738297
-Non-hospitalised,66,0.2342277461649845,0.07219358738553895,0.10143953091867877,0.37093971822823135
-Non-hospitalised,67,0.22752822469347891,0.06965344011174374,0.09937020214209752,0.35942543693161805
-Non-hospitalised,68,0.22092827133882642,0.06715144264973379,0.09733060282400066,0.34808312520745227
-Non-hospitalised,69,0.2141929206147025,0.06459831444866929,0.09524864170971621,0.3365085401691877
-Non-hospitalised,70,0.2075883277837957,0.06209520765338958,0.09320594159770919,0.325159615422559
-Non-hospitalised,71,0.20103976239484728,0.059613724412468,0.09117959243420541,0.31390776707284707
-Non-hospitalised,72,0.1944783171733291,0.0571277123157421,0.08914838825088399,0.3026344965636937
-Non-hospitalised,73,0.18800229636620622,0.05467450214092357,0.08714257866195978,0.29150885836952317
-Non-hospitalised,74,0.1815540843840758,0.05223223445022761,0.08514443057022125,0.2804317737372173
-Non-hospitalised,75,0.17519820704003888,0.0498254006192376,0.08317385624070082,0.2695141621146458
-Non-hospitalised,76,0.16884053797190265,0.04741831487325376,0.08120181812352226,0.25859421332379123
-Non-hospitalised,77,0.1623966392309089,0.0449790091537707,0.07920218081597095,0.24752684256718743
-Non-hospitalised,78,0.15622095215118487,0.04264186571279184,0.07728454129997278,0.23692115506720438
-Non-hospitalised,79,0.14991068962779747,0.04025426351717106,0.0753243342380585,0.226084979313972
-Non-hospitalised,80,0.14378937109095363,0.037938828949273994,0.07342170829500086,0.2155741897595765
-Non-hospitalised,81,0.13780366998374308,0.03567538096786722,0.07156023385145904,0.20529709676524885
-Non-hospitalised,82,0.1318268133144025,0.03341598511448448,0.0697006398907472,0.19503589965289256
-Non-hospitalised,83,0.12601593000617897,0.031220188606581146,0.0678917076680402,0.18506046509950685
-Non-hospitalised,84,0.1204662387515517,0.02912405113942511,0.06616310372339515,0.17553424426075548
-Non-hospitalised,85,0.11499292647383268,0.0270577611134951,0.06445750304738462,0.16613978200304064
-Non-hospitalised,86,0.10983067601930302,0.025110101241605298,0.06284792671356508,0.15728001067681757
-Non-hospitalised,87,0.10491181845750684,0.0232555524783403,0.06131345411161404,0.1488386389154069
-Non-hospitalised,88,0.10036913608228248,0.021544257640149042,0.05989551932563392,0.14104354705962574
-Non-hospitalised,89,0.09612443047250667,0.019946708017952546,0.05856990794796159,0.1337603796673272
-Non-hospitalised,90,0.09200616960066314,0.01839837064692916,0.05728327629875338,0.12669460662955054
-Non-hospitalised,91,0.08840992877601823,0.017048140044741235,0.056123355923305286,0.12052283661702154
-Non-hospitalised,92,0.08509828260039594,0.015806554908086853,0.05504671819333304,0.11479637556570743
-Non-hospitalised,93,0.08169343976287013,0.014532026312261572,0.05393948350208276,0.10890828375675707
-Non-hospitalised,94,0.0785673878910018,0.013364364548968444,0.0529222268477569,0.10350217089774663
-Non-hospitalised,95,0.0758705453625101,0.012359605159546915,0.05204383529605412,0.09883846172266919
-Non-hospitalised,96,0.07348176786679582,0.011472120796686226,0.051262631030770466,0.09470769769527977
-Non-hospitalised,97,0.07134359864680294,0.010680244223530581,0.050557306874158776,0.09101059553458023
-Non-hospitalised,98,0.0694559412908773,0.00998365148768083,0.0499338748182454,0.08774713947399093
-Non-hospitalised,99,0.06740964899695494,0.00923145729557726,0.04925774329907314,0.08422707995557979
-Non-hospitalised,100,0.06608122861709463,0.008746019463868774,0.0488170521652858,0.08204061446506078
-Non-hospitalised,101,0.06349814663050812,0.0078071286548533755,0.048063465387941144,0.07778832720880326
-Non-hospitalised,102,0.061633094600094786,0.007137742351746698,0.04761806121273492,0.07462547382567204
-Non-hospitalised,103,0.05821378280490502,0.005939419359532592,0.04662975609626849,0.06898462239083715
-Non-hospitalised,104,0.05311435366572785,0.004318582116959235,0.0448510807525614,0.06162076593508076
-Non-hospitalised,105,0.04206999186763898,0.0023423025213870114,0.03782041704658469,0.04660198148526385
-Cohort,0,2.1109592359388683,0.9573039559566742,0.5826490213148227,3.9016252326650354
-Cohort,1,2.0846751453417154,0.9456313379056966,0.5759744074327631,3.8550329756897628
-Cohort,2,2.0549959507678124,0.9321309725703107,0.5684590656723775,3.801034115709317
-Cohort,3,2.0252999576516686,0.9186056805455942,0.5609405290390753,3.7469258724949492
-Cohort,4,1.9957636109575103,0.9051466835601342,0.5534627646504601,3.693076683930409
-Cohort,5,1.9663187081841949,0.8917146895203816,0.5460090702358529,3.639327238790562
-Cohort,6,1.9370897885294895,0.8783743851734922,0.5386104512117379,3.585938807943071
-Cohort,7,1.9080229446701185,0.865095012980713,0.5312536788189423,3.5327869016292572
-Cohort,8,1.8791573886684052,0.8518961080435611,0.523948571049719,3.4799504170813256
-Cohort,9,1.8505221724509509,0.8387919886555083,0.5167024121234427,3.427487189179358
-Cohort,10,1.8220910857791746,0.8257674930719923,0.5095087801776805,3.3753360659265668
-Cohort,11,1.793870352191352,0.8128250586034095,0.5023692757117838,3.323507047832017
-Cohort,12,1.7659258223417404,0.7999992860485063,0.4953002643006312,3.2721399314188573
-Cohort,13,1.7381745862580587,0.7872450547027167,0.4882812390859978,3.221052871797392
-Cohort,14,1.7107399096023856,0.7746280173492553,0.4813427786771932,3.1705109036213375
-Cohort,15,1.6834832658139813,0.7620734875190732,0.4744505722149135,3.120213096944752
-Cohort,16,1.6565614508768056,0.7496652608813339,0.4676435000078387,3.0704975375057146
-Cohort,17,1.6298458532393614,0.7373342236794079,0.4608896899005634,3.0210859141012794
-Cohort,18,1.6034086688470848,0.725118969643949,0.45420704256412914,2.9721340496524364
-Cohort,19,1.577333251629283,0.7130640318363778,0.4476162296074043,2.92382154149705
-Cohort,20,1.5514084207780028,0.7010565403550623,0.4410648730504166,2.875694099033964
-Cohort,21,1.5257232876578677,0.6891443471637283,0.43457505540931557,2.827944788659531
-Cohort,22,1.500405134151319,0.677395547264305,0.42817833935469274,2.780847969121763
-Cohort,23,1.4752331457811705,0.6656926494813022,0.4218199196567176,2.7339310340362197
-Cohort,24,1.45029492282973,0.6540827822723286,0.41552150080924066,2.6873839888750095
-Cohort,25,1.425591921217893,0.6425671597404542,0.4092834132942347,2.641212002352794
-Cohort,26,1.4010717039323788,0.6311184656624579,0.40309260203537245,2.595305487384723
-Cohort,27,1.3768679551981733,0.6198081461194253,0.3969822258234029,2.549952029153434
-Cohort,28,1.3528332118296884,0.6085586267915897,0.39091562445452155,2.5048399028142145
-Cohort,29,1.3290318403811177,0.5974046631290039,0.3849087400461826,2.460109093814503
-Cohort,30,1.3054516897158877,0.5863405080467775,0.3789585091665342,2.4157366984280935
-Cohort,31,1.2821258538596105,0.5753842754056974,0.37307311301499535,2.371795702387016
-Cohort,32,1.2589469702971172,0.5644802646999062,0.36722580986972014,2.3280624168899675
-Cohort,33,1.235947388606476,0.5536464054178332,0.36142458291478446,2.2846090804122205
-Cohort,34,1.2131672472549826,0.5429045812643141,0.35567936145188844,2.2415237708847737
-Cohort,35,1.1905305587940145,0.5322159624180263,0.34997118091893414,2.1986505986447367
-Cohort,36,1.1681006316399825,0.5216145389669897,0.3443157367721894,2.1561262428302514
-Cohort,37,1.1458191742285595,0.5110708992330136,0.33869845730616077,2.1138326388183195
-Cohort,38,1.1237510616840658,0.5006196787149009,0.33313543941259705,2.0719090079209797
-Cohort,39,1.1018296295563732,0.49022725219123103,0.32761001795054917,2.0302203433536214
-Cohort,40,1.0801388969096068,0.47993794501621134,0.32214307382244384,1.9889447521392614
-Cohort,41,1.0585866794792127,0.46970509510959974,0.3167115728659061,1.9478948514992425
-Cohort,42,1.0371188317068485,0.4595019992394979,0.3113019414059486,1.9069634360531176
-Cohort,43,1.0158900689721901,0.4494089889382976,0.30595271188516165,1.8664731992999886
-Cohort,44,0.994720067834006,0.43933473488157987,0.300618830027346,1.8260573526103003
-Cohort,45,0.9737110355063819,0.42933251050996224,0.29532573817860097,1.785929898466281
-Cohort,46,0.9529401724687594,0.41944232377490537,0.29009266835226477,1.7462515498229634
-Cohort,47,0.9322058835423843,0.40956273935327075,0.2848692095115763,1.7066149395223082
-Cohort,48,0.9116310650112264,0.3997570462868545,0.2796860022463032,1.6672742889018262
-Cohort,49,0.8911195756669013,0.3899776571447156,0.2745189559966026,1.6280385100332802
-Cohort,50,0.8708202769241786,0.38030036441378706,0.26940523927404886,1.5892120419248745
-Cohort,51,0.8505697216730774,0.37064388609188625,0.2643039200133498,1.5504684832963587
-Cohort,52,0.8305245564121887,0.36108747228517646,0.259254142099856,1.5121261650996234
-Cohort,53,0.8105533308109701,0.35156615520188733,0.2542229609406009,1.4739242231595264
-Cohort,54,0.7907281013561739,0.34211660812004235,0.24922836842089732,1.436010002712291
-Cohort,55,0.7710380465948953,0.33273406736528305,0.2442676151890034,1.3983644388870555
-Cohort,56,0.7513863439447492,0.32337134434570797,0.23930156360798657,1.3607981048593791
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-ICU,98,0.15780135630659056,0.04244241045619333,0.08203460930579379,0.23109248241505237
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-ICU,100,0.1443108481374491,0.03748701583500701,0.07719630597717707,0.20934230944404836
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-ICU,105,0.062468520502315404,0.010324282153352576,0.043172180417829005,0.08019481966980326
-Non-hospitalised (no AD),0,0.06029255359492132,0.0031930620711356763,0.05334779675495315,0.0666896801150587
-Non-hospitalised (no AD),1,0.06029176427928104,0.0031930620711356755,0.053347007439312887,0.06668889079941842
-Non-hospitalised (no AD),2,0.06029055916491195,0.003193062071135676,0.05334580232494379,0.06668768568504933
-Non-hospitalised (no AD),3,0.06028905939139321,0.0031930620711356737,0.053344302551425034,0.06668618591153058
-Non-hospitalised (no AD),4,0.06028731676040839,0.003193062071135674,0.05334255992044022,0.06668444328054576
-Non-hospitalised (no AD),5,0.06028536263832295,0.003193062071135676,0.0533406057983548,0.06668248915846033
-Non-hospitalised (no AD),6,0.06028321870846666,0.0031930620711356733,0.05333846186849849,0.06668034522860404
-Non-hospitalised (no AD),7,0.06028090113002562,0.003193062071135676,0.053336144290057445,0.066678027650163
-Non-hospitalised (no AD),8,0.06027842255151544,0.0031930620711356768,0.05333366571154727,0.06667554907165282
-Non-hospitalised (no AD),9,0.06027579322412005,0.003193062071135675,0.05333103638415189,0.06667291974425744
-Non-hospitalised (no AD),10,0.060273021675130954,0.003193062071135677,0.053328264835162786,0.06667014819526833
-Non-hospitalised (no AD),11,0.06027011514278785,0.0031930620711356737,0.053325358302819696,0.06666724166292522
-Non-hospitalised (no AD),12,0.06026707987141093,0.0031930620711356785,0.05332232303144276,0.06666420639154831
-Non-hospitalised (no AD),13,0.0602639213197678,0.0031930620711356763,0.05331916447979963,0.06666104783990519
-Non-hospitalised (no AD),14,0.060260644312967605,0.0031930620711356763,0.05331588747299945,0.06665777083310499
-Non-hospitalised (no AD),15,0.060257253156165626,0.003193062071135676,0.05331249631619746,0.066654379676303
-Non-hospitalised (no AD),16,0.060253751721605416,0.0031930620711356755,0.053308994881637255,0.06665087824174279
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-Non-hospitalised (no AD),103,0.05914189828026596,0.003162609149460371,0.052263375084176306,0.06547801402500196
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diff --git a/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_SMR100.csv b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_SMR100.csv
new file mode 100644
index 000000000..a16c772c1
--- /dev/null
+++ b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_SMR100.csv
@@ -0,0 +1,425 @@
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diff --git a/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_SMR150.csv b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_SMR150.csv
new file mode 100644
index 000000000..bd0ece007
--- /dev/null
+++ b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_SMR150.csv
@@ -0,0 +1,425 @@
+hospitalisation,age,mean,sd,lower,upper
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diff --git a/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group.csv b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group.csv
deleted file mode 100644
index 4c2e9eb73..000000000
--- a/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group.csv
+++ /dev/null
@@ -1,41 +0,0 @@
-hospitalisation,age_group,mean,sd,lower,upper
-Non-hospitalised,"[0, 12)",0.7549139354806483,0.2694793460593751,0.26033009381509126,1.2654616489762913
-Non-hospitalised,"[12, 18)",0.6557682465121549,0.2319941046629199,0.23045372285893342,1.0953384536860604
-Non-hospitalised,"[18, 25)",0.5880445016279974,0.2063723398223531,0.20999398363634544,0.9790782480476492
-Non-hospitalised,"[25, 35)",0.5091859479830138,0.1765102746143206,0.186103734244279,0.8436328969220431
-Non-hospitalised,"[35, 45)",0.42584808226184356,0.1449126549567518,0.1604649496836378,0.7004064743683598
-Non-hospitalised,"[45, 55)",0.3491923396383036,0.11581944544988457,0.1368567772399197,0.5686024656989533
-Non-hospitalised,"[55, 65)",0.27969699686034233,0.08944193486207846,0.1154619020140246,0.44910453369341524
-Non-hospitalised,"[65, 75)",0.2126266394094571,0.06400609633358172,0.09476051283649502,0.3338201724504505
-Non-hospitalised,"[75, 85)",0.14939012711229085,0.04005908402606354,0.07515845558458831,0.22519449849936593
-Non-hospitalised,"[85, 120)",0.09828109776916907,0.020763278513057953,0.05923471330586266,0.13746974820739163
-Cohort,"[0, 12)",1.947344085248101,0.8829639350355141,0.5412114592110516,3.604281750091514
-Cohort,"[12, 18)",1.6987183071446816,0.7690716896261184,0.4783040910288788,3.1482470035334598
-Cohort,"[18, 25)",1.5249729878066711,0.6887680123577957,0.434387154298585,2.8264323232466313
-Cohort,"[25, 35)",1.3175888898488481,0.5919806641239077,0.38202444758207266,2.438351224528888
-Cohort,"[35, 45)",1.0920935043268636,0.4855752793860406,0.3251580085312155,2.01155621562992
-Cohort,"[45, 55)",0.8801245695316176,0.3847321499777779,0.2717491661845526,1.6069922740029907
-Cohort,"[55, 65)",0.687311735355322,0.29289620531534105,0.2229126837437332,1.238524417668631
-Cohort,"[65, 75)",0.504013524458945,0.20608777240492873,0.17585731883702357,0.8902525649839934
-Cohort,"[75, 85)",0.33570615664482356,0.1271930468929509,0.1322131364633784,0.5737736324455484
-Cohort,"[85, 120)",0.2035409329616504,0.06606929554916093,0.09746170546937125,0.3283851787805579
-ICU,"[0, 12)",3.0458057335864352,1.1556055055051586,0.9479553850387902,5.033795329043032
-ICU,"[12, 18)",2.6483296474992724,1.0077417622788762,0.8292613202990119,4.389522587765419
-ICU,"[18, 25)",2.3712176184520075,0.9031283363394529,0.7465306849634975,3.935484401607709
-ICU,"[25, 35)",2.041279732573488,0.7765853791756424,0.6480557113435373,3.3887197036126837
-ICU,"[35, 45)",1.6835328129672527,0.6369160143929701,0.5413132363211832,2.788335483444341
-ICU,"[45, 55)",1.3479507354630411,0.5041738109373548,0.4412056820786608,2.219865106898985
-ICU,"[55, 65)",1.0427993720331827,0.3832494824727844,0.3501759277790381,1.7022073769061836
-ICU,"[65, 75)",0.752297215384547,0.2691716929942119,0.2634972701683917,1.2125534296753606
-ICU,"[75, 85)",0.484877070693285,0.16581782726261002,0.1836767039998967,0.7669847660173886
-ICU,"[85, 120)",0.2743083448199896,0.08590104372637732,0.12078862220202156,0.4206731519138677
-Non-hospitalised (no AD),"[0, 12)",0.060282821089509275,0.0031930620711356746,0.05333806424954111,0.06667994760964666
-Non-hospitalised (no AD),"[12, 18)",0.060258956115156394,0.0031930620711356768,0.05331419927518823,0.06665608263529377
-Non-hospitalised (no AD),"[18, 25)",0.06023431230082882,0.003193062071135676,0.05328955546086065,0.0666314388209662
-Non-hospitalised (no AD),"[25, 35)",0.06019667198670331,0.003193062071135676,0.05325191514673514,0.06659379850684069
-Non-hospitalised (no AD),"[35, 45)",0.06014543739797787,0.003193062071135674,0.05320068055800971,0.06654256391811524
-Non-hospitalised (no AD),"[45, 55)",0.060086650014045964,0.0031930620711356763,0.05314189317407779,0.06648377653418333
-Non-hospitalised (no AD),"[55, 65)",0.06002388450843266,0.003193062071135675,0.05307912766846448,0.06642101102857004
-Non-hospitalised (no AD),"[65, 75)",0.05995463758703485,0.003193062071135675,0.05300988074706668,0.06635176410717222
-Non-hospitalised (no AD),"[75, 85)",0.05988066945971605,0.0031930620710924457,0.05293591261984191,0.06627779597976682
-Non-hospitalised (no AD),"[85, 120)",0.05978895111949516,0.003192267062301329,0.05284592338560672,0.06618448488259096
diff --git a/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group_SMR100.csv b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group_SMR100.csv
new file mode 100644
index 000000000..679fd35f5
--- /dev/null
+++ b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group_SMR100.csv
@@ -0,0 +1,41 @@
+hospitalisation,age_group,mean,sd,lower,upper
+Non-hospitalised,"[0, 12)",,,,
+Non-hospitalised,"[12, 18)",,,,
+Non-hospitalised,"[18, 25)",,,,
+Non-hospitalised,"[25, 35)",,,,
+Non-hospitalised,"[35, 45)",,,,
+Non-hospitalised,"[45, 55)",,,,
+Non-hospitalised,"[55, 65)",,,,
+Non-hospitalised,"[65, 75)",,,,
+Non-hospitalised,"[75, 85)",,,,
+Non-hospitalised,"[85, 120)",,,,
+Hospitalised (no IC),"[0, 12)",,,,
+Hospitalised (no IC),"[12, 18)",,,,
+Hospitalised (no IC),"[18, 25)",,,,
+Hospitalised (no IC),"[25, 35)",,,,
+Hospitalised (no IC),"[35, 45)",,,,
+Hospitalised (no IC),"[45, 55)",,,,
+Hospitalised (no IC),"[55, 65)",,,,
+Hospitalised (no IC),"[65, 75)",,,,
+Hospitalised (no IC),"[75, 85)",,,,
+Hospitalised (no IC),"[85, 120)",,,,
+Hospitalised (IC),"[0, 12)",,,,
+Hospitalised (IC),"[12, 18)",,,,
+Hospitalised (IC),"[18, 25)",,,,
+Hospitalised (IC),"[25, 35)",,,,
+Hospitalised (IC),"[35, 45)",,,,
+Hospitalised (IC),"[45, 55)",,,,
+Hospitalised (IC),"[55, 65)",,,,
+Hospitalised (IC),"[65, 75)",,,,
+Hospitalised (IC),"[75, 85)",,,,
+Hospitalised (IC),"[85, 120)",,,,
+Non-hospitalised (no AD),"[0, 12)",,,,
+Non-hospitalised (no AD),"[12, 18)",,,,
+Non-hospitalised (no AD),"[18, 25)",,,,
+Non-hospitalised (no AD),"[25, 35)",,,,
+Non-hospitalised (no AD),"[35, 45)",,,,
+Non-hospitalised (no AD),"[45, 55)",,,,
+Non-hospitalised (no AD),"[55, 65)",,,,
+Non-hospitalised (no AD),"[65, 75)",,,,
+Non-hospitalised (no AD),"[75, 85)",,,,
+Non-hospitalised (no AD),"[85, 120)",,,,
diff --git a/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group_SMR150.csv b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group_SMR150.csv
new file mode 100644
index 000000000..34f728f7e
--- /dev/null
+++ b/data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group_SMR150.csv
@@ -0,0 +1,41 @@
+hospitalisation,age_group,mean,sd,lower,upper
+Non-hospitalised,"[0, 12)",0.668462419110261,0.2582313828457331,0.2036324547925269,1.2266842634252282
+Non-hospitalised,"[12, 18)",0.5798944237428355,0.22187795956279144,0.18094233731503914,1.0596337319588378
+Non-hospitalised,"[18, 25)",0.5190581364867876,0.1968431158317833,0.16537131828772747,0.9447050484446313
+Non-hospitalised,"[25, 35)",0.44763489426351,0.16736730072686784,0.1471094089416646,0.8092417221722648
+Non-hospitalised,"[35, 45)",0.37120795187222455,0.13572307883109985,0.12759033988719307,0.6642768756212402
+Non-hospitalised,"[45, 55)",0.3002411506332599,0.10627491577506192,0.10947793400313126,0.5297938114190412
+Non-hospitalised,"[55, 65)",0.23622082675172743,0.07971458883081076,0.0929959256939798,0.4084045703816341
+Non-hospitalised,"[65, 75)",0.17618743525018343,0.05486694992256693,0.07783840424384207,0.29459863667090785
+Non-hospitalised,"[75, 85)",0.12206526711280687,0.03255206915706073,0.06381993641723875,0.1920038620032939
+Non-hospitalised,"[85, 120)",0.08103127670002823,0.015751336483312568,0.05284996746867399,0.1150524706145426
+Hospitalised (no IC),"[0, 12)",1.8980894392621346,0.7267827620017653,0.6207356011360611,3.4240096409038068
+Hospitalised (no IC),"[12, 18)",1.6478459932072107,0.6300813814747115,0.546874783644301,2.9726944049100523
+Hospitalised (no IC),"[18, 25)",1.472820461853593,0.5617587044599202,0.49489406166793193,2.654800869057853
+Hospitalised (no IC),"[25, 35)",1.2634184870428975,0.47914425110921366,0.4321870750253125,2.272532748393311
+Hospitalised (no IC),"[35, 45)",1.0346805795675365,0.3878601694223998,0.3623500214659087,1.8514667391139488
+Hospitalised (no IC),"[45, 55)",0.8193694351258769,0.30129304663868833,0.2966104797693478,1.4529560947554594
+Hospitalised (no IC),"[55, 65)",0.6252626513069367,0.22328391548470894,0.23728421113389478,1.0937858217247964
+Hospitalised (no IC),"[65, 75)",0.44562604115188553,0.15161325700772058,0.18080980173221772,0.7631334746039069
+Hospitalised (no IC),"[75, 85)",0.28685503228783793,0.08900307321463005,0.13044941001272536,0.4732435233307565
+Hospitalised (no IC),"[85, 120)",0.16891493919738063,0.04328019853546922,0.09158887797754195,0.2612073485364238
+Hospitalised (IC),"[0, 12)",2.9237322020738303,0.9793705841671974,1.2107236313271368,4.999388921316399
+Hospitalised (IC),"[12, 18)",2.531638312858393,0.8497821429058505,1.0519529833728634,4.331883645375432
+Hospitalised (IC),"[18, 25)",2.257562381291156,0.757962284073033,0.9411960051611258,3.8628058096578908
+Hospitalised (IC),"[25, 35)",1.9298543316512762,0.6466155391861154,0.8090419079356348,3.2987897263962442
+Hospitalised (IC),"[35, 45)",1.5721185906351458,0.5232154765316691,0.6651014247066298,2.679412531598891
+Hospitalised (IC),"[45, 55)",1.2355267693612049,0.40597563326945363,0.5298650819811873,2.094388339582217
+Hospitalised (IC),"[55, 65)",0.9320835477121102,0.3003429071567427,0.40793600939386565,1.56709365027751
+Hospitalised (IC),"[65, 75)",0.6511568673099988,0.2034594772850809,0.2948885959200651,1.0807438310262218
+Hospitalised (IC),"[75, 85)",0.4027184979804148,0.1189921363368849,0.1936167990895866,0.6530541829319151
+Hospitalised (IC),"[85, 120)",0.21808592502322638,0.057238589088626377,0.11680411982667387,0.33711143404710353
+Non-hospitalised (no AD),"[0, 12)",0.06014137527980275,0.002961992425985527,0.05453426633193338,0.06582908835826626
+Non-hospitalised (no AD),"[12, 18)",0.06011715444004468,0.002961992425985527,0.054510045492175314,0.0658048675185082
+Non-hospitalised (no AD),"[18, 25)",0.06009237832769276,0.0029619924259855273,0.05448526937982339,0.06578009140615626
+Non-hospitalised (no AD),"[25, 35)",0.06005474395604065,0.0029619924259855278,0.054447635008171284,0.06574245703450415
+Non-hospitalised (no AD),"[35, 45)",0.06000375519059451,0.002961992425985528,0.054396646242725145,0.06569146826905803
+Non-hospitalised (no AD),"[45, 55)",0.059945480395673406,0.002961992425985528,0.05433837144780404,0.06563319347413692
+Non-hospitalised (no AD),"[55, 65)",0.05988345587040076,0.002961992425985525,0.054276346922531395,0.06557116894886428
+Non-hospitalised (no AD),"[65, 75)",0.05981520268326211,0.0029619924259855247,0.054208093735392746,0.06550291576172562
+Non-hospitalised (no AD),"[75, 85)",0.05974245826449364,0.0029619924191818985,0.05413534932950368,0.06543017132989262
+Non-hospitalised (no AD),"[85, 120)",0.05963967771162379,0.0029605970042578026,0.05403521032407272,0.06532471125642572
diff --git a/data/covid19_DTM/interim/sciensano/vacc_incidence_national.pkl b/data/covid19_DTM/interim/sciensano/vacc_incidence_national.pkl
index bc0b8f29a..55f095e20 100644
Binary files a/data/covid19_DTM/interim/sciensano/vacc_incidence_national.pkl and b/data/covid19_DTM/interim/sciensano/vacc_incidence_national.pkl differ
diff --git a/notebooks/analysis/woldmuyn_long_COVID.py b/notebooks/analysis/woldmuyn_long_COVID.py
index 1346733de..311e5c635 100644
--- a/notebooks/analysis/woldmuyn_long_COVID.py
+++ b/notebooks/analysis/woldmuyn_long_COVID.py
@@ -38,8 +38,6 @@
     ## Define simulation settings ##
     ################################
 
-    # Number of simulations
-    #N=50
     # Number of neg. binomial draws/ simulation
     K=10
     # Number of cpu's
@@ -47,7 +45,7 @@
     # Number of age groups
     age_stratification_size=10
     # End of simulation
-    end_sim='2021-12-31'
+    end_sim='2021-07-01'
     # Confidence level used to visualise model fit
     conf_int=0.05
 
@@ -116,14 +114,12 @@
       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])
+      ax.set_ylim([0,6600])
     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()
+    fig.savefig(result_folder+'QALY_losses_per_age_group.pdf')
 
     # Wolf's code starts here
     label_font = font_manager.FontProperties(family='CMU Sans Serif',
@@ -206,6 +202,6 @@
         lower = np.quantile(total,0.025)
         upper = np.quantile(total,0.975)
         
-        QALY_table[total_label]['Total'] = f'{mean:.0f}\n({lower:.0f};{upper:.0f})'
+        QALY_table[total_label]['Total'] = f'{mean:.0f}\n({lower:.0f}; {upper:.0f})'
 
     QALY_table.to_csv(os.path.join(result_folder,f'Long_COVID_summary_SMR{SMR*100:.0f}.csv'))
\ No newline at end of file
diff --git a/notebooks/preprocessing/woldmuyn_long_covid_average_QALY_loss.py b/notebooks/preprocessing/woldmuyn_long_covid_average_QALY_loss.py
index a9109227d..47411bffc 100644
--- a/notebooks/preprocessing/woldmuyn_long_covid_average_QALY_loss.py
+++ b/notebooks/preprocessing/woldmuyn_long_covid_average_QALY_loss.py
@@ -97,14 +97,6 @@
 # results #
 # ------- #
 
-# latex font
-label_font = font_manager.FontProperties(family='CMU Sans Serif',
-                                   style='normal', 
-                                   size=10)
-legend_font = font_manager.FontProperties(family='CMU Sans Serif',
-                                   style='normal', 
-                                   size=8)
-
 # result folders
 data_result_folder = '../../data/QALY_model/interim/long_COVID/'
 fig_result_folder = '../../results/QALY_model/direct_QALYs/prepocessing/'
@@ -247,7 +239,7 @@ def log_probability(theta, tau, x, y, bounds):
     )
     samplers.update({hospitalisation:sampler})
     # run sampler
-    sampler.run_mcmc(pos, 25000, progress=True)
+    sampler.run_mcmc(pos, 50000, progress=True)
     # extract chains
     flat_samples = sampler.get_chain(discard=5000, thin=100, flat=True)
     # print results
@@ -256,6 +248,8 @@ def log_probability(theta, tau, x, y, bounds):
     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)
 
+print(p_AD_summary)
+
 # visualise MCMC results    
 fig,axes = plt.subplots(nrows=1,ncols=3,figsize=(8.3,0.25*11.7),sharey=True)
 
@@ -305,8 +299,8 @@ def log_probability(theta, tau, x, y, bounds):
 ax.set_xlabel('Age (years)',size=10)
 ax.set_ylabel('QoL (-)',size=10)
 ax.set_ylim([0.5, 0.9])
-ax.grid(False)
 ax.tick_params(axis='both', which='major', labelsize=10)
+ax.grid(False)
 plt.tight_layout()
 fig.savefig(os.path.join(abs_dir,fig_result_folder,'QoL_Belgium_fit.pdf'))
 
@@ -323,7 +317,7 @@ 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 = 1000
+draws = 500
 
 # 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'])
@@ -351,8 +345,8 @@ 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 (OPM: kan ook discreet) 
     QALY_loss = quad(QALY_loss_func,0,LE,args=(tau,p_AD,age,QoL_after))[0]/12 
-    average_QALY_losses.iloc[idx] = QALY_loss
-    
+    average_QALY_losses_per_age.iloc[idx] = QALY_loss
+
 print('\n(4.2) Bin average QALY loss per age to age groups\n')
 
 # bin data
@@ -394,13 +388,12 @@ def get_sd(x):
 
 print('\n(6) Visualise results\n')
 
-# Visualise results (TWA)
+# QALY loss per age group
 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']
+    mean = average_QALY_losses_per_age_summary.loc[hospitalisation]['mean']
+    lower = average_QALY_losses_per_age_summary.loc[hospitalisation]['lower']
+    upper = average_QALY_losses_per_age_summary.loc[hospitalisation]['upper']
     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='-')
@@ -413,42 +406,24 @@ def get_sd(x):
 plt.tight_layout()
 fig.savefig(os.path.join(abs_dir,fig_result_folder,'average_QALY_losses_per_age.pdf'))
 
-# Visualise results (WD)
-fig,axs = plt.subplots(1,3,figsize=(6,2.5),sharey=True,sharex=True)
-for ax,hospitalisation in zip(axs,hospitalisation_groups):
-    mean = average_QALY_losses_per_age_summary.loc[hospitalisation]['mean']
-    lower = average_QALY_losses_per_age_summary.loc[hospitalisation]['lower']
-    upper = average_QALY_losses_per_age_summary.loc[hospitalisation]['upper']
-    ax.plot(LE_table.index.values,mean,color=palette_colors[color_dict[hospitalisation]],linestyle='--',label=f'{hospitalisation}',linewidth=1)
-    ax.fill_between(LE_table.index.values,lower,upper,alpha=0.20, color=palette_colors[color_dict[hospitalisation]])
-    
-    ax.grid(False)
-    ax.set_xlabel('Age when infected (years)',font=label_font)
-    ax.tick_params(axis='both', which='major', labelsize=8)
-    ax.set_title(hospitalisation,font=label_font)
-
-axs[0].set_ylabel('Average QALY loss',font=label_font)
-fig.tight_layout()
-fig.savefig(os.path.join(abs_dir,fig_result_folder,f'average_QALY_losses_per_age_SMR{SMR*100:.0f}.png'),dpi=600,bbox_inches='tight')
-
 # QALY losses due COVID death
 fig,ax = plt.subplots(figsize=(5,3))
-ax.plot(Life_table.compute_QALY_D_x(r=0,SMR=SMR),color=palette_colors['black'],label=r'$r=0\%$',linewidth=2)
-ax.plot(Life_table.compute_QALY_D_x(r=0.03,SMR=SMR),color=palette_colors['black'],linestyle=':',label=r'$r=3\%$',linewidth=2)
+ax.plot(Life_table.compute_QALY_D_x(r=0,SMR=SMR),color='black',label=r'$r=0\%$',linewidth=2)
+ax.plot(Life_table.compute_QALY_D_x(r=0.03,SMR=SMR),color='black',linestyle=':',label=r'$r=3\%$',linewidth=2)
 ax.grid(False)
-ax.set_xlabel('Age (years)',font=label_font)
-ax.set_ylabel(r'$QALY_D$',font=label_font)
+ax.set_xlabel('Age (years)')
+ax.set_ylabel(r'$QALY_D$')
 ax.tick_params(axis='both', which='major', labelsize=8)
-ax.legend(prop=legend_font)
+ax.legend()
 fig.tight_layout()
-fig.savefig(os.path.join(abs_dir,fig_result_folder,f'QALY_D_SMR{SMR*100:.0f}.png'),dpi=600,bbox_inches='tight')
+fig.savefig(os.path.join(abs_dir,fig_result_folder,f'QALY_D_SMR{SMR*100:.0f}.pdf'),dpi=600,bbox_inches='tight')
 
 # Life expectancy
 fig,ax = plt.subplots(figsize=(3,3))
 ax.plot(LE_table,'black',linewidth=1.5)
 ax.grid(False)
-ax.set_ylabel('Life expectancy (years)',font=label_font)
-ax.set_xlabel('Age (years)',font=label_font)
+ax.set_ylabel('Life expectancy (years)')
+ax.set_xlabel('Age (years)')
 ax.tick_params(axis='both', which='major', labelsize=8)
 fig.tight_layout()
-fig.savefig(os.path.join(abs_dir,fig_result_folder,f'LE_SMR{SMR*100:.0f}.png'),dpi=600,bbox_inches='tight')
\ No newline at end of file
+fig.savefig(os.path.join(abs_dir,fig_result_folder,f'LE_SMR{SMR*100:.0f}.pdf'),dpi=600,bbox_inches='tight')
\ No newline at end of file
diff --git a/results/QALY_model/direct_QALYs/analysis/Long_COVID_summary.csv b/results/QALY_model/direct_QALYs/analysis/Long_COVID_summary.csv
deleted file mode 100644
index b3acab8e9..000000000
--- a/results/QALY_model/direct_QALYs/analysis/Long_COVID_summary.csv
+++ /dev/null
@@ -1,89 +0,0 @@
-,Non-hospitalised (no AD),Non-hospitalised (AD),Cohort,ICU,Deaths,Total (no AD),Total (AD)
-0-12,"13423
-(12015;14930)","157452
-(49854;274172)","3873
-(894;7006)","166
-(63;312)","0
-(0;0)","17462
-(13893;21468)","161491
-(53118;278452)"
-12-18,"6751
-(6057;7499)","68913
-(22364;119343)","1487
-(348;2690)","305
-(131;521)","502
-(245;796)","9045
-(7620;10774)","71208
-(24414;121733)"
-18-25,"5288
-(4711;5898)","48488
-(16000;83893)","1420
-(332;2589)","244
-(104;405)","0
-(0;0)","6951
-(5643;8433)","50151
-(17101;85632)"
-25-35,"9130
-(8144;10203)","72651
-(24379;126004)","3566
-(846;6414)","531
-(217;913)","2276
-(1328;3406)","15502
-(12285;19277)","79023
-(30135;132496)"
-35-45,"11919
-(10635;13240)","79551
-(27815;136155)","4253
-(1053;7658)","1025
-(420;1744)","4989
-(3533;6750)","22185
-(18003;26470)","89817
-(37157;146527)"
-45-55,"11049
-(9846;12345)","60648
-(22096;103103)","6004
-(1562;10787)","2016
-(863;3403)","11183
-(8247;14077)","30253
-(24484;36381)","79852
-(40741;123085)"
-55-65,"7155
-(6351;8005)","31563
-(12213;52503)","5368
-(1427;9478)","2487
-(1105;4157)","21750
-(18206;25285)","36760
-(31298;43337)","61168
-(40315;82328)"
-65-75,"2705
-(2399;3047)","9111
-(3847;14696)","5946
-(1784;10449)","3162
-(1449;5088)","45420
-(40089;50999)","57233
-(49661;64739)","63640
-(54108;73470)"
-75-85,"926
-(812;1052)","2208
-(1072;3427)","4248
-(1491;7195)","1382
-(637;2239)","52241
-(47264;57450)","58796
-(52750;64975)","60079
-(53991;66113)"
-85+,"462
-(405;525)","735
-(434;1053)","1421
-(597;2318)","99
-(49;164)","19288
-(17760;20972)","21270
-(19371;23250)","21543
-(19687;23538)"
-Total,"68807
-(61355;76777)","531321
-(180293;914343)","37586
-(10347;66245)","11416
-(5105;18499)","157649
-(146532;170026)","275458
-(240383;313122)","737971
-(381697;1120947)"
diff --git a/results/QALY_model/direct_QALYs/analysis/Long_COVID_summary_SMR100.csv b/results/QALY_model/direct_QALYs/analysis/Long_COVID_summary_SMR100.csv
new file mode 100644
index 000000000..e98cae007
--- /dev/null
+++ b/results/QALY_model/direct_QALYs/analysis/Long_COVID_summary_SMR100.csv
@@ -0,0 +1,89 @@
+,Non-hospitalised (no AD),Non-hospitalised (AD),Cohort,ICU,Deaths,Total (no AD),Total (AD)
+0-12,"8308
+(7215;9355)","95784
+(22162;176321)","1809
+(412;3191)","76
+(23;151)","0
+(0;0)","10193
+(8370;11783)","97628
+(23766;179274)"
+12-18,"4621
+(4014;5190)","46361
+(11042;84891)","804
+(185;1417)","164
+(59;297)","276
+(137;488)","5865
+(4997;6633)","47583
+(12200;86664)"
+18-25,"3757
+(3185;4296)","33873
+(8215;61977)","877
+(204;1554)","153
+(51;274)","0
+(0;0)","4787
+(3891;5629)","34879
+(9154;63328)"
+25-35,"6624
+(5627;7564)","51835
+(13067;94287)","2262
+(532;3944)","338
+(122;592)","1474
+(705;2314)","10698
+(8604;12568)","55851
+(16701;99231)"
+35-45,"8521
+(7358;9644)","55925
+(14930;100931)","2716
+(669;4743)","662
+(229;1169)","3036
+(2217;3870)","14935
+(12332;17318)","62262
+(20907;107712)"
+45-55,"8223
+(7006;9394)","44403
+(12645;79253)","4006
+(1020;6876)","1351
+(470;2344)","7550
+(5781;9224)","21131
+(17319;24750)","57185
+(24322;93054)"
+55-65,"4806
+(3960;5570)","20871
+(6446;36750)","3326
+(931;5678)","1554
+(605;2636)","13606
+(11656;15580)","23292
+(19967;26568)","39240
+(23523;55949)"
+65-75,"1796
+(1398;2145)","5964
+(2059;10296)","3760
+(1144;6285)","1970
+(738;3282)","29139
+(25712;33036)","36665
+(31782;41731)","40704
+(33922;47521)"
+75-85,"512
+(361;662)","1205
+(477;2037)","2261
+(815;3735)","738
+(314;1239)","28582
+(24175;31180)","32093
+(27607;35359)","32726
+(27980;36010)"
+85+,"281
+(199;365)","442
+(216;690)","742
+(334;1141)","52
+(25;86)","10232
+(8738;11191)","11307
+(9693;12454)","11457
+(9711;12570)"
+Total,"47449
+(40499;54165)","356664
+(91264;647881)","22563
+(6251;38517)","7057
+(2635;11902)","93895
+(82929;101095)","170965
+(148203;191242)","479515
+(209211;779571)"
diff --git a/results/QALY_model/direct_QALYs/analysis/Long_COVID_summary_SMR150.csv b/results/QALY_model/direct_QALYs/analysis/Long_COVID_summary_SMR150.csv
new file mode 100644
index 000000000..377564f61
--- /dev/null
+++ b/results/QALY_model/direct_QALYs/analysis/Long_COVID_summary_SMR150.csv
@@ -0,0 +1,89 @@
+,Non-hospitalised (no AD),Non-hospitalised (AD),Cohort,ICU,Deaths,Total (no AD),Total (AD)
+0-12,"8270
+(7280;9352)","90236
+(14582;157711)","1794
+(432;3082)","73
+(20;140)","0
+(0;0)","10136
+(8429;11889)","92079
+(16423;159442)"
+12-18,"4599
+(4054;5177)","43563
+(7403;75963)","792
+(191;1351)","158
+(43;277)","254
+(125;416)","5804
+(4968;6680)","44756
+(8526;77025)"
+18-25,"3739
+(3248;4278)","31716
+(5667;54786)","861
+(209;1480)","147
+(40;256)","0
+(0;0)","4748
+(3934;5613)","32712
+(6479;55825)"
+25-35,"6593
+(5730;7541)","48277
+(9210;82875)","2210
+(539;3785)","319
+(83;546)","1358
+(841;1959)","10480
+(8406;12473)","52132
+(12573;86823)"
+35-45,"8483
+(7444;9686)","51582
+(10766;87745)","2620
+(666;4434)","606
+(164;1020)","2981
+(2129;3902)","14689
+(12072;16967)","57748
+(16660;93621)"
+45-55,"8186
+(7102;9419)","40321
+(9461;67807)","3804
+(1002;6433)","1231
+(356;2044)","6784
+(4865;8396)","20005
+(16666;23280)","52078
+(20886;79521)"
+55-65,"4783
+(4088;5562)","18573
+(4998;30583)","3069
+(866;5172)","1398
+(411;2330)","12027
+(10026;14919)","21277
+(17736;24985)","35015
+(21034;48394)"
+65-75,"1786
+(1486;2126)","5186
+(1670;8394)","3366
+(1100;5565)","1738
+(575;2832)","24676
+(22406;27343)","31566
+(27673;35399)","34910
+(29820;40108)"
+75-85,"507
+(392;630)","1024
+(417;1650)","1976
+(764;3156)","610
+(223;993)","22990
+(20883;25499)","26084
+(23459;28960)","26575
+(23632;29661)"
+85+,"278
+(215;346)","375
+(206;556)","621
+(301;956)","41
+(18;71)","7839
+(7043;8729)","8779
+(7932;9755)","8870
+(7957;9836)"
+Total,"47225
+(41067;54111)","330853
+(64709;566318)","21113
+(6043;35368)","6321
+(1913;10391)","78909
+(72366;85375)","153568
+(134389; 173180)","436876
+(166479; 673679)"
diff --git a/results/QALY_model/direct_QALYs/analysis/QALY_losses_per_age_group.pdf b/results/QALY_model/direct_QALYs/analysis/QALY_losses_per_age_group.pdf
new file mode 100644
index 000000000..33e3197d6
Binary files /dev/null and b/results/QALY_model/direct_QALYs/analysis/QALY_losses_per_age_group.pdf differ
diff --git a/results/QALY_model/direct_QALYs/analysis/QALY_losses_per_age_group_AD.png b/results/QALY_model/direct_QALYs/analysis/QALY_losses_per_age_group_AD.png
deleted file mode 100644
index 42e82b9d1..000000000
Binary files a/results/QALY_model/direct_QALYs/analysis/QALY_losses_per_age_group_AD.png and /dev/null differ
diff --git a/results/QALY_model/direct_QALYs/analysis/QALY_losses_per_age_group_no_AD.png b/results/QALY_model/direct_QALYs/analysis/QALY_losses_per_age_group_no_AD.png
deleted file mode 100644
index 99ac6a302..000000000
Binary files a/results/QALY_model/direct_QALYs/analysis/QALY_losses_per_age_group_no_AD.png and /dev/null differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/LE_SMR100.pdf b/results/QALY_model/direct_QALYs/prepocessing/LE_SMR100.pdf
new file mode 100644
index 000000000..51bffd25d
Binary files /dev/null and b/results/QALY_model/direct_QALYs/prepocessing/LE_SMR100.pdf differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/LE_SMR150.pdf b/results/QALY_model/direct_QALYs/prepocessing/LE_SMR150.pdf
new file mode 100644
index 000000000..1569cb7af
Binary files /dev/null and b/results/QALY_model/direct_QALYs/prepocessing/LE_SMR150.pdf differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/QALY_D_SMR100.pdf b/results/QALY_model/direct_QALYs/prepocessing/QALY_D_SMR100.pdf
new file mode 100644
index 000000000..44146505b
Binary files /dev/null and b/results/QALY_model/direct_QALYs/prepocessing/QALY_D_SMR100.pdf differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/QALY_D_SMR150.pdf b/results/QALY_model/direct_QALYs/prepocessing/QALY_D_SMR150.pdf
new file mode 100644
index 000000000..454e27fde
Binary files /dev/null and b/results/QALY_model/direct_QALYs/prepocessing/QALY_D_SMR150.pdf differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/QoL_Belgium_fit.pdf b/results/QALY_model/direct_QALYs/prepocessing/QoL_Belgium_fit.pdf
new file mode 100644
index 000000000..f8afb1c07
Binary files /dev/null and b/results/QALY_model/direct_QALYs/prepocessing/QoL_Belgium_fit.pdf differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/QoL_Belgium_fit.png b/results/QALY_model/direct_QALYs/prepocessing/QoL_Belgium_fit.png
deleted file mode 100644
index 101a0d63a..000000000
Binary files a/results/QALY_model/direct_QALYs/prepocessing/QoL_Belgium_fit.png and /dev/null differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/average_QALY_losses_per_age.pdf b/results/QALY_model/direct_QALYs/prepocessing/average_QALY_losses_per_age.pdf
new file mode 100644
index 000000000..640384aa9
Binary files /dev/null and b/results/QALY_model/direct_QALYs/prepocessing/average_QALY_losses_per_age.pdf differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/prevalence_MCMC_fit.pdf b/results/QALY_model/direct_QALYs/prepocessing/prevalence_MCMC_fit.pdf
new file mode 100644
index 000000000..45b6dd0c3
Binary files /dev/null and b/results/QALY_model/direct_QALYs/prepocessing/prevalence_MCMC_fit.pdf differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/prevalence_MCMC_fit.png b/results/QALY_model/direct_QALYs/prepocessing/prevalence_MCMC_fit.png
deleted file mode 100644
index dd86d0bcf..000000000
Binary files a/results/QALY_model/direct_QALYs/prepocessing/prevalence_MCMC_fit.png and /dev/null differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/prevalence_first_fit.pdf b/results/QALY_model/direct_QALYs/prepocessing/prevalence_first_fit.pdf
new file mode 100644
index 000000000..a4fb090e1
Binary files /dev/null and b/results/QALY_model/direct_QALYs/prepocessing/prevalence_first_fit.pdf differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/prevalence_first_fit_AD.png b/results/QALY_model/direct_QALYs/prepocessing/prevalence_first_fit_AD.png
deleted file mode 100644
index f2dd5be7e..000000000
Binary files a/results/QALY_model/direct_QALYs/prepocessing/prevalence_first_fit_AD.png and /dev/null differ
diff --git a/results/QALY_model/direct_QALYs/prepocessing/prevalence_first_fit_no_AD.png b/results/QALY_model/direct_QALYs/prepocessing/prevalence_first_fit_no_AD.png
deleted file mode 100644
index 84c10f03b..000000000
Binary files a/results/QALY_model/direct_QALYs/prepocessing/prevalence_first_fit_no_AD.png and /dev/null differ
diff --git a/src/QALY_model/__pycache__/direct_QALYs.cpython-310.pyc b/src/QALY_model/__pycache__/direct_QALYs.cpython-310.pyc
index e621651be..bac8351df 100644
Binary files a/src/QALY_model/__pycache__/direct_QALYs.cpython-310.pyc and b/src/QALY_model/__pycache__/direct_QALYs.cpython-310.pyc differ
diff --git a/src/QALY_model/direct_QALYs.py b/src/QALY_model/direct_QALYs.py
index 55ae4f1ec..b2cbaaa7c 100644
--- a/src/QALY_model/direct_QALYs.py
+++ b/src/QALY_model/direct_QALYs.py
@@ -467,13 +467,13 @@ def lost_QALYs(out,AD_non_hospitalised=False,SMR=1,draws=500):
         out_enlarged = xr.concat([out]*int(np.ceil(500/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__)
-    rel_dir = '../../../data/covid19_DTM/interim/QALY_model/long_COVID/'
+    rel_dir = '../../data/QALY_model/interim/long_COVID/'
     file_name = f'average_QALY_losses_per_age_group_SMR{SMR*100:.0f}.csv'
     average_QALY_losses = pd.read_csv(os.path.join(abs_dir,rel_dir,file_name),index_col=[0,1])
     age_groups = average_QALY_losses.index.get_level_values('age_group').unique()