Revise QALY chapter PhD (#390)

data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group_SMR100_r3.csv

@@ -0,0 +1,41 @@
+hospitalisation,age_group,mean,sd,lower,upper
+Non-hospitalised,"[0, 12)",0.3305614846604638,0.11919868809265946,0.10851097562367687,0.5658184180399422
+Non-hospitalised,"[12, 18)",0.31145636477251554,0.1113799430362558,0.10437726943689853,0.5305694735704607
+Non-hospitalised,"[18, 25)",0.29630662504384564,0.10516697272923886,0.10108594908880274,0.5033235764924822
+Non-hospitalised,"[25, 35)",0.2762995369162831,0.09693623121612849,0.09671862432752007,0.46724224100834366
+Non-hospitalised,"[35, 45)",0.251938498516607,0.08686610287215077,0.09136713421768743,0.4231386240758023
+Non-hospitalised,"[45, 55)",0.2255363828129091,0.07589188263500844,0.08552935210393163,0.37515569811887844
+Non-hospitalised,"[55, 65)",0.19690569687471937,0.06394960700769411,0.07914718059533314,0.32297415751938907
+Non-hospitalised,"[65, 75)",0.16339932997284856,0.04997099277884874,0.07167572645084565,0.2618944456420483
+Non-hospitalised,"[75, 85)",0.12507355711644985,0.03402251959859311,0.06297415972398501,0.1925632721560043
+Non-hospitalised,"[85, 120)",0.08865965850234227,0.01895275236878103,0.05363349807534285,0.12612544861137343
+Hospitalised (no IC),"[0, 12)",0.8832845775704176,0.3667542374707597,0.24834719946371747,1.6010117363689964
+Hospitalised (no IC),"[12, 18)",0.8350553370303702,0.34667736075680583,0.23556070560461503,1.5156136957070334
+Hospitalised (no IC),"[18, 25)",0.7957888521573001,0.33010600618632585,0.2260532259392908,1.445249538046481
+Hospitalised (no IC),"[25, 35)",0.7422051380680859,0.30709922987713795,0.21334830411677447,1.3478204924815753
+Hospitalised (no IC),"[35, 45)",0.6740361017477915,0.27715614693587187,0.19753740254699703,1.2215598683105011
+Hospitalised (no IC),"[45, 55)",0.5967586767223034,0.24243677912980746,0.17997204882791695,1.0758564152430778
+Hospitalised (no IC),"[55, 65)",0.5107166912307866,0.20328325684705903,0.16060728708832192,0.9120062314183928
+Hospitalised (no IC),"[65, 75)",0.4098777370494498,0.15736732748090046,0.13789774194935206,0.7198761680263338
+Hospitalised (no IC),"[75, 85)",0.29644054455405416,0.10614307865000118,0.11191847155023543,0.5050904000406272
+Hospitalised (no IC),"[85, 120)",0.1911535539171515,0.059257407859640426,0.08854283062650693,0.30724006154291217
+Hospitalised (IC),"[0, 12)",1.3461227527343875,0.4267899908033585,0.6137361179274297,2.175143263865365
+Hospitalised (IC),"[12, 18)",1.2694394479365863,0.4033292935836578,0.5789481865094063,2.047050309726796
+Hospitalised (IC),"[18, 25)",1.2071238522702425,0.38398123878685525,0.5504407668346681,1.9436448038918315
+Hospitalised (IC),"[25, 35)",1.1222764174615594,0.3571317406783741,0.5119787470010881,1.80676368014914
+Hospitalised (IC),"[35, 45)",1.0146401130107683,0.32219053218017746,0.4642554602971365,1.6335061542907594
+Hospitalised (IC),"[45, 55)",0.8929591291230691,0.28166648748512496,0.4114974564445142,1.4341913561922361
+Hospitalised (IC),"[55, 65)",0.757688935932757,0.235958710095915,0.35360168477418186,1.2104366711921228
+Hospitalised (IC),"[65, 75)",0.5991661199060311,0.18235633407819005,0.2858012239419936,0.948084181684375
+Hospitalised (IC),"[75, 85)",0.4206589603473456,0.12254148128982942,0.2092165123792232,0.65475299798298
+Hospitalised (IC),"[85, 120)",0.25476009896544793,0.06770274607258217,0.14106545475220286,0.384031925939427
+Non-hospitalised (no AD),"[0, 12)",0.059617292175113995,0.0029204948972637902,0.05386841701244646,0.06543471450848262
+Non-hospitalised (no AD),"[12, 18)",0.059593505897304624,0.0029204948972637902,0.0538446307346371,0.06541092823067327
+Non-hospitalised (no AD),"[18, 25)",0.059569178855336656,0.00292049489726379,0.05382030369266913,0.0653866011887053
+Non-hospitalised (no AD),"[25, 35)",0.05953223147331912,0.0029204948972637907,0.053783356310651585,0.06534965380668775
+Non-hospitalised (no AD),"[35, 45)",0.05948217929722228,0.0029204948972637902,0.05373330413455476,0.06529960163059093
+Non-hospitalised (no AD),"[45, 55)",0.05942498073650866,0.0029204948972637902,0.05367610557384113,0.0652424030698773
+Non-hospitalised (no AD),"[55, 65)",0.05936410666998854,0.00292049489726379,0.05361523150732101,0.06518152900335718
+Non-hospitalised (no AD),"[65, 75)",0.059297124052350283,0.0029204948972637907,0.05354824888968275,0.06511454638571892
+Non-hospitalised (no AD),"[75, 85)",0.05922573817725154,0.0029204948972319307,0.05347686301464672,0.06504316051055674
+Non-hospitalised (no AD),"[85, 120)",0.059137177003986074,0.002919776200075173,0.05338971656743944,0.06495316774263428

data/QALY_model/interim/long_COVID/average_QALY_losses_per_age_group_SMR150_r3.csv

@@ -0,0 +1,41 @@
+hospitalisation,age_group,mean,sd,lower,upper
+Non-hospitalised,"[0, 12)",0.3346063379336184,0.12109259111225079,0.09492116970436534,0.5702149476451104
+Non-hospitalised,"[12, 18)",0.3146885853212113,0.11285124009349479,0.09124400620358904,0.5336221221959858
+Non-hospitalised,"[18, 25)",0.29877175474701845,0.10625997037175196,0.0882727478131342,0.5044972104163242
+Non-hospitalised,"[25, 35)",0.2774990831868067,0.0974386608638956,0.08432754236843863,0.4656985068459262
+Non-hospitalised,"[35, 45)",0.25107404990923887,0.08645732605959154,0.07947020083082093,0.41741485825986646
+Non-hospitalised,"[45, 55)",0.22178826718538297,0.07425928964266687,0.07417303735071187,0.36438758554651945
+Non-hospitalised,"[55, 65)",0.1898015630383642,0.06092057818523529,0.06875804137520232,0.306724372423187
+Non-hospitalised,"[65, 75)",0.15331040950701832,0.045708646346628704,0.06273607063895473,0.24088077767656518
+Non-hospitalised,"[75, 85)",0.11370767607446111,0.02922824667047981,0.0562120611332052,0.16951938429562183
+Non-hospitalised,"[85, 120)",0.07892878870699377,0.014830918132018785,0.04961203898155524,0.10706309902191954
+Hospitalised (no IC),"[0, 12)",0.8570009852879483,0.3408766576331888,0.26391027719405624,1.5617449070899652
+Hospitalised (no IC),"[12, 18)",0.8081332294990626,0.32122556828316406,0.25136501403637757,1.4771610927369048
+Hospitalised (no IC),"[18, 25)",0.7680565338706365,0.30487789204841986,0.24113402585590862,1.4063589806390613
+Hospitalised (no IC),"[25, 35)",0.7128072522442771,0.2819505647650179,0.22554248412078579,1.306452643914053
+Hospitalised (no IC),"[35, 45)",0.6414303691448003,0.25169134955553824,0.20573860815351802,1.173756060909175
+Hospitalised (no IC),"[45, 55)",0.5594227646088907,0.2162573298545412,0.1841785265262356,1.017609070190077
+Hospitalised (no IC),"[55, 65)",0.4683144487597797,0.17654583434503004,0.16057523563988751,0.8422514281721868
+Hospitalised (no IC),"[65, 75)",0.3647291213941632,0.13148125270307992,0.1344978121466269,0.6433048998426287
+Hospitalised (no IC),"[75, 85)",0.2540797477722758,0.08377969144058438,0.10730923130339316,0.431578234182271
+Hospitalised (no IC),"[85, 120)",0.15877580367068933,0.04333957055421197,0.0798310996684462,0.24981778809821872
+Hospitalised (IC),"[0, 12)",1.3581312171653273,0.4371215927404079,0.6120481304784425,2.1903389609520576
+Hospitalised (IC),"[12, 18)",1.2779833781040089,0.4121174931115049,0.5748181411614312,2.059246119057844
+Hospitalised (IC),"[18, 25)",1.2122234950217792,0.39126712599402125,0.5447861021852428,1.9516351696085426
+Hospitalised (IC),"[25, 35)",1.1215163385729991,0.3619400906727364,0.5042046614559457,1.8050530994139753
+Hospitalised (IC),"[35, 45)",1.0042482271927164,0.32309584233043304,0.4530706647771798,1.6146492761010358
+Hospitalised (IC),"[45, 55)",0.8694283303115776,0.27746660289313535,0.3956350629588998,1.3935350815279648
+Hospitalised (IC),"[55, 65)",0.7196012930453836,0.22625782932307945,0.33249603262257754,1.1466107594310122
+Hospitalised (IC),"[65, 75)",0.5492643376084241,0.16815417009589884,0.2605580197024796,0.8661746098328296
+Hospitalised (IC),"[75, 85)",0.36736087255078237,0.10667806413534164,0.18431444173479233,0.5680162877775026
+Hospitalised (IC),"[85, 120)",0.2107654170452775,0.05443878893770784,0.11669182083459281,0.3150601936735091
+Non-hospitalised (no AD),"[0, 12)",0.059654337301126924,0.0030930255103472186,0.053567373520776916,0.06543825585845722
+Non-hospitalised (no AD),"[12, 18)",0.059630551023317754,0.0030930255103472186,0.05354358724296774,0.06541446958064805
+Non-hospitalised (no AD),"[18, 25)",0.059606223981349786,0.0030930255103472186,0.05351926020099978,0.06539014253868007
+Non-hospitalised (no AD),"[25, 35)",0.05956927659933224,0.003093025510347219,0.05348231281898223,0.06535319515666253
+Non-hospitalised (no AD),"[35, 45)",0.05951922442323542,0.0030930255103472195,0.05343226064288541,0.0653031429805657
+Non-hospitalised (no AD),"[45, 55)",0.05946202586252179,0.0030930255103472186,0.053375062082171784,0.06524594441985208
+Non-hospitalised (no AD),"[55, 65)",0.05940115179600168,0.003093025510347219,0.053314188015651666,0.06518507035333197
+Non-hospitalised (no AD),"[65, 75)",0.059334169178363406,0.003093025510347218,0.05324720539801339,0.0651180877356937
+Non-hospitalised (no AD),"[75, 85)",0.05926278320510238,0.0030930255043479077,0.053175819436558795,0.06504670175121403
+Non-hospitalised (no AD),"[85, 120)",0.05916245938576747,0.0030916140892978234,0.05307827323165357,0.06494373860359912

notebooks/analysis/woldmuyn_long_COVID.py

@@ -4,8 +4,8 @@
 
 """ 
 
-__author__      = "Wolf Demuynck"
-__copyright__   = "Copyright (c) 2022 by W. Demuynck, BIOMATH, Ghent University. All Rights Reserved."
+__author__      = "Wolf Demuynck, Tijs Alleman"
+__copyright__   = "Copyright (c) 2024 by T. Alleman, BIOMATH, Ghent University. All Rights Reserved."
 
 import argparse
 from covid19_DTM.models.utils import initialize_COVID19_SEIQRD_hybrid_vacc
@@ -19,11 +19,14 @@
 import multiprocessing as mp
 import pandas as pd
 
+# parse arguments
 parser = argparse.ArgumentParser()
+parser.add_argument("-r", "--discounting", help="Discounting", default=0.03)
 parser.add_argument("-s", "--SMR", help="SMR", default=1)
 parser.add_argument("-n", "--N", help="DTM simulations", default=50)
 args = parser.parse_args()
-
+# format arguments
+r = float(args.discounting)
 SMR = float(args.SMR)
 N = int(args.N)
 
@@ -75,8 +78,8 @@
     #######################
 
     print('\n3) Calculating QALYs')
-    out_AD = lost_QALYs(out,AD_non_hospitalised=True,SMR=SMR)
-    out_no_AD = lost_QALYs(out,AD_non_hospitalised=False,SMR=SMR)
+    out_AD = lost_QALYs(out, AD_non_hospitalised=True, SMR=SMR, r=r)
+    out_no_AD = lost_QALYs(out, AD_non_hospitalised=False, SMR=SMR, r=r)
 
     ####################
     ## Visualisations ##
@@ -114,7 +117,7 @@
       ax.grid(False)
       ax.tick_params(axis='both', which='major', labelsize=10)
       ax.tick_params(axis='x', which='major', rotation=30)
-      ax.set_ylim([0,3200])
+      ax.set_ylim([0,2500])
     axes[0].legend(loc=2, framealpha=1, fontsize=10)
     axes[0].set_ylabel('QALYs lost per 100K inhab.', size=10)
 
@@ -204,4 +207,4 @@
 
         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'))
+    QALY_table.to_csv(os.path.join(result_folder,f'Long_COVID_summary_SMR{SMR*100:.0f}_r{r*100:.0f}.csv'))