-
Notifications
You must be signed in to change notification settings - Fork 1
/
build_vax_data.R
1665 lines (1462 loc) · 78.8 KB
/
build_vax_data.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# build_vax_data.R processes the COVID-19 immunisations linelist from
# Public Health England and generates schedules for covidm using assumptions on
# future vaccine rollout (i.e. vaccine supply, uptake and distribution of
# vaccine products to different age groups)
############################## CHANGES START HERE ##############################
# specify folder and file names here
path_to_data <- "~/Documents/uk_covid_data_sensitive/"
foldername <- "vaccinations"
filedate <- "20220504" # change this to match the date the data was built
ref_date <- '2022-05-04' # this should be in date format and match filedate
sebsfile <- "vaccination-2022-05-04.rds"
path_to_newcovidvax <- "."
uk_covid_data_path <- paste0(path_to_newcovidvax, "/fitting_data/")
cm_path <- paste0(path_to_newcovidvax, "/covidm_for_fitting/")
# required packages
library(data.table)
library(stringr)
library(lubridate)
library(dplyr)
library(ggplot2)
# turn off scientific notation
options(scipen=999)
date_fitting <- as.character(today())
############################## CHANGES ~END~ HERE ##############################
# load summary vaccination data built from PHE immunisations linelist by Seb
sv <- readRDS(paste0(path_to_data, foldername, '/', sebsfile))
# checks on the summary vaccination data (optional)
checks <- function(vaxdata){
print(paste0('Total vaccines administered: ', formatC(sum(vaxdata$vaccinated), big.mark=",")))
print(paste0('Earliest date: ', min(vaxdata$vaccination_date)))
print(paste0('Latest date: ', max(vaxdata$vaccination_date)))
print(paste0('Total dates: ', length(unique(vaxdata$vaccination_date))))
print(paste0(length(vaxdata$vaccination_date[vaxdata$vaccination_date < '2020-12-08']), ' entries with a date before 8th December 2020 (start of rollout)'))
print(paste0('Comprising ', 100*length(vaxdata$vaccination_date[vaxdata$vaccination_date < '2020-12-08'])/dim(vaxdata)[1], '% of entries'))
print(paste0(sum(vaxdata$vaccinated[vaxdata$vaccination_date < '2020-12-08']), ' vaccinated on dates before 8th December 2020 (start of rollout)'))
print(paste0('Comprising ', 100*sum(vaxdata$vaccinated[vaxdata$vaccination_date < '2020-12-08'])/sum(vaxdata$vaccinated), '% of all vaccinations'))
print(paste0(length(unique(vaxdata$age_group)), ' age groups: ', paste0(unique(vaxdata$age_group), collapse = ", ")))
print(paste0(length(unique(na.omit(vaxdata$age_group))), ' non-NA age groups (in order): ', paste0(sort(unique(vaxdata$age_group), by = levels(vaxdata$age_group)), collapse = ", ")))
print(paste0(sum(vaxdata$vaccinated[is.na(vaxdata$age_group) == TRUE]), ' vaccinations wih NA age group'))
print(paste0('Comprising ', 100*sum(vaxdata$vaccinated[is.na(vaxdata$age_group) == TRUE])/sum(vaxdata$vaccinated), '% of all vaccinations'))
print(paste0('Dose numbers from ', min(vaxdata$dose_number), ' to ', max(vaxdata$dose_number)))
print(paste0('Vaccine products: ', paste0(unique(vaxdata$product), collapse = ", ")))
print(paste0(sum(vaxdata$vaccinated[is.na(vaxdata$product) == TRUE]), ' vaccinations wih NA product'))
print(paste0('Comprising ', 100*sum(vaxdata$vaccinated[is.na(vaxdata$product) == TRUE])/sum(vaxdata$vaccinated), '% of all vaccinations'))
print(paste0(sum(vaxdata$vaccinated[vaxdata$product == 'Other'], na.rm = TRUE), ' vaccinations wih `Other` product'))
print(paste0('Comprising ', 100*sum(vaxdata$vaccinated[vaxdata$product == 'Other'], na.rm = TRUE)/sum(vaxdata$vaccinated), '% of all vaccinations'))
print(paste0(length(unique(vaxdata$region_of_residence)), ' regions: ', paste0(unique(vaxdata$region_of_residence), collapse = ", ")))
print(paste0(sum(vaxdata$vaccinated[is.na(vaxdata$region_of_residence) == TRUE]), ' vaccinations wih NA regions'))
print(paste0('Comprising ', 100*sum(vaxdata$vaccinated[is.na(vaxdata$region_of_residence) == TRUE])/sum(vaxdata$vaccinated), '% of all vaccinations'))
p1 = hist(vaxdata$dose_number)
p1
p2 = qplot(vaxdata$region_of_residence) + theme(axis.text.x = element_text(angle = 90))
p2
p3 = qplot(vaxdata$product) + theme(axis.text.x = element_text(angle = 90))
p3
plots = list(p1 = p1, p2 = p2, p3 = p3)
return(plots)
}
oldsv = sv
# keep first doses only
sv = sv[sv$dose_number == 1,]
plots = checks(oldsv)
plots$p1
plots$p2
plots$p3
plots2 = checks(sv)
plots2$p1
plots2$p2
plots2$p3
# recode erroneous dates.... (assume all vaccinations were really delivered, but dates were recorded incorrectly)
# firstly recode dates occurring before 2020 as 2020 dates
year(sv$vaccination_date[year(sv$vaccination_date) < 2020]) = 2020
# secondly recode dates occurring after this year as this year's dates
year(sv$vaccination_date[year(sv$vaccination_date) > year(today())]) = year(today())
# recode 2020 dates occurring before 8th December 2020 as 2021 dates
year(sv$vaccination_date[sv$vaccination_date < '2020-12-08']) = 2021
# recode dates occurring after reference_date as dates from the previous year
year(sv$vaccination_date[sv$vaccination_date > ref_date]) = year(sv$vaccination_date[sv$vaccination_date > ref_date])-1
# finally, remove any dates which are still before 8th December 2020 (e.g. this could happen when reference_date is < 8th December 2021)
sv = sv[!(sv$vaccination_date < '2020-12-08'),]
# check dates again
print(paste0('Earliest date: ', min(sv$vaccination_date)))
print(paste0('Latest date: ', max(sv$vaccination_date)))
# set up covidm model to get covidm age groups
datapath = function(x) paste0(uk_covid_data_path, x)
cm_force_rebuild = F
cm_build_verbose = T
cm_version = 3
source(paste0(cm_path, "/R/covidm.R"))
popUK = readRDS(datapath("popNHS.rds"))
matricesUK = readRDS(datapath("matricesNHS.rds"))
cm_populations = rbind(cm_populations[name != "United Kingdom"], popUK)
cm_matrices = c(cm_matrices, matricesUK)
nhs_regions = popUK[, unique(name)]
# NUMBER OF REGIONS TO FIT
N_REG = 12;
# Build parameters for NHS regions ###
params = cm_parameters_SEI3R(nhs_regions[1:N_REG], deterministic = T,
date_start = "2020-01-01",
date_end = date_fitting,
dE = cm_delay_gamma(2.5, 2.5, t_max = 15, t_step = 0.25)$p,
dIp = cm_delay_gamma(2.5, 4.0, t_max = 15, t_step = 0.25)$p,
dIs = cm_delay_gamma(2.5, 4.0, t_max = 15, t_step = 0.25)$p,
dIa = cm_delay_gamma(5.0, 4.0, t_max = 15, t_step = 0.25)$p)
params = cm_split_matrices_ex_in(params, 15)
agegroups <- params$pop[[1]]$group_names
min_ages <- c(0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75)
oldest_age <- 115
max_ages <- c(4,9,14,19,24,29,34,39,44,49,54,59,64,69,74,oldest_age)
ages <- data.frame(groups = agegroups, min_ages = min_ages, max_ages = max_ages)
# build existing vaccination data (sv) into format suitable for covidm
# get sequence of vaccination dates, from first to last recorded in vax
dates <- seq(min(sv$vaccination_date), max(sv$vaccination_date), by = 1)
num_dates <- length(dates)
# get list of NHS England (and Unknown!) regions of residence in vax
regions <- unique(sv$region_of_residence)
# get list of vaccines in vax -> keep Pfizer, AstraZeneca and Moderna for model purposes
vaccines <- unique(sv$product)
print(vaccines)
`%!in%` <- Negate(`%in%`)
nonvaccs <- vaccines[vaccines %!in% c('PF', 'AZ', 'MD')]
vaccines <- vaccines[vaccines %in% c('PF', 'AZ', 'MD')]
# redistribute doses with vaccine type in 'nonvaccs' (see above) according to
# the measured split between PF, AZ and MD vaccine products
unknown_vax <- sv[sv$product %in% nonvaccs,]
num_to_correct <- dim(unknown_vax)[1]
# calculate existing split of AZ / PF / MD
total = sum(sv$vaccinated[sv$product %in% vaccines])
aztot = sum(sv$vaccinated[sv$product %in% c('AZ')])
pftot = sum(sv$vaccinated[sv$product %in% c('PF')])
mdtot = sum(sv$vaccinated[sv$product %in% c('MD')])
vax_split = c(pftot/total, aztot/total, mdtot/total)
if (sum(vax_split)!=1){
stop("Sum of split vector should equal 1")
}
to_distribute = round(vax_split*num_to_correct)
pfsamp = sample(num_to_correct, size = to_distribute[1], replace = FALSE)
remaining = seq(1,num_to_correct, by = 1)
remaining = remaining[!remaining %in% pfsamp]
azsamp = sample(remaining, size = to_distribute[2], replace = FALSE)
product_type_vector = rep('MD', num_to_correct)
product_type_vector[pfsamp] <- "PF"
product_type_vector[azsamp] <- "AZ"
unknown_vax$product <- product_type_vector
# remove unknown vax from main data frame
sv <- sv[!(sv$product %in% nonvaccs),]
# bind unknown vax entries with imputed vaccine product type to main data frame
sv <- rbind(sv, unknown_vax)
# get list of possible doses
doses <- unique(sv$dose_number)
# recode all entries with "" (i.e. unknown) dose number as first doses
if ("" %in% doses){
num_unknown_doses <- sum(sv$dose_number == "")
# get unknown doses in separate data frame
unknown_doses <- sv[sv$dose_number == "",]
unknown_doses$dose_number <- rep(1, dim(unknown_doses)[1])
# remove unknown doses from main data frame
sv <- sv[!(sv$dose_number == ""),]
# bind updated entries back to main data frame
sv <- rbind(sv, unknown_doses)
}
# remove "" entry from doses
doses <- doses[!(doses == "")]
# do the same for any NA doses
if (sum(is.na(doses)) > 0){
num_unknown_doses <- sum(is.na(sv$dose_number) == TRUE)
# get unknown doses in separate data frame
unknown_doses <- sv[is.na(sv$dose_number) == TRUE,]
unknown_doses$dose_number <- rep(1, dim(unknown_doses)[1])
# remove unknown doses from main data frame
sv <- sv[!(is.na(sv$dose_number) == TRUE),]
# bind updated entries back to main data frame
sv <- rbind(sv, unknown_doses)
}
# remove "" entry from doses
doses <- doses[!is.na(doses) == TRUE]
# initialise dataframe to store vaccine data for covidm
vax_df <- data.frame(region = NULL, age.group = NULL, ages = NULL,
vaccine = NULL, dose = NULL, date = NULL, number = NULL)
# loop through regions
for (i in 1:length(regions)){
this_region <- regions[i]
print(paste0('Commencing region ', i, ' of ', length(regions), ': ', this_region))
if (is.na(this_region) == TRUE){
this_region_vax <- sv[is.na(sv$region_of_residence) == TRUE,]
} else {
this_region_vax <- sv[sv$region_of_residence == this_region,]
}
# loop through age groups + 1 (one extra group for NA age entries)
for (j in 1:(length(agegroups)+1)){
print(paste0('Commencing age group ', j, ' of ', length(agegroups)+1))
if (j == length(agegroups)+1){
this_age_group <- NA
this_age_group_vax <- this_region_vax[is.na(this_region_vax$age_group) == T,]
print('Age group is NA ages')
} else {
this_age_group <- agegroups[j]
this_age_group_vax <- this_region_vax[this_region_vax$age_group == this_age_group,]
print(paste0('Age group is ', this_age_group))
}
# loop through vaccines
for (k in 1:length(vaccines)){
this_vaccine <- vaccines[k]
print(paste0('Commencing vaccine product ', k, ' of ', length(vaccines), ': ', this_vaccine))
this_vaccine_vax <- this_age_group_vax[this_age_group_vax$product == this_vaccine,]
# loop through doses
for (l in 1:length(doses)){
print(paste0('Commencing dose number ', l, ' of ', length(doses)))
this_dose <- doses[l]
this_dose_vax <- this_vaccine_vax[this_vaccine_vax$dose_number == this_dose,]
# initialise vector to store number of vaccines delivered on each date
deliveries <- rep(0, length(dates))
# loop through dates
for (m in 1:length(dates)){
# calculate number of doses delivered for this region, age group,
# vaccine, dose and date
this_date <- dates[m]
this_date_vax <- this_dose_vax[this_dose_vax$vaccination_date == this_date,]
if (dim(this_date_vax)[1] > 1){
deliveries[m] <- sum(this_date_vax$vaccinated)
} else if (dim(this_date_vax)[1] == 0){
deliveries[m] <- 0
} else {
deliveries[m] <- this_date_vax$vaccinated
}
rm(this_date_vax)
}
# populate dataframe for this region, age group, vaccine, dose and date
this_df <- data.frame(region = rep(this_region, num_dates),
age.group = rep(j, num_dates),
ages = rep(this_age_group, num_dates),
vaccine = rep(this_vaccine, num_dates),
dose = rep(this_dose, num_dates),
date = dates,
number = deliveries)
rm(this_dose, this_dose_vax, deliveries)
# bind dataframe to master dataframe
vax_df <- rbind(vax_df, this_df)
rm(this_df)
}
rm(this_vaccine, this_vaccine_vax)
}
rm(this_age_group, this_age_group_vax)
rm(lower_age, upper_age)
}
rm(this_region, this_region_vax)
}
# check that total doses in vax_df matches total doses in vax
if (sum(sv$vaccinated) != sum(vax_df$number)){
warning("Total doses in sv does not match those in processed dataframe")
}
# save processed linelist output as .csv
datetime <- str_replace_all(Sys.time(), "[ :GMTBST-]", "")
write.csv(vax_df, file = paste0(path_to_data, foldername, "/", filedate, "-",
datetime, "-processedvaccinedata.csv"),
row.names = FALSE)
if (0){
vax_df = read.csv('~/Documents/uk_covid_data_sensitive/vaccinations/20220504-20220505174155-processedvaccinedata.csv')
}
# redistribute vaccination entries with 'NA' region and 'NA' age (or both)
# into appropriate NHS England regions and covidm age groups
if (0){ # testing code on small subset of data
# sample at random 5000 rows from data frame
idxs = round(runif(5000, 0, dim(vax_df)[1]))
vax_df = vax_df[idxs,]
}
# rename regions from codes to names
vax_df = as.data.table(vax_df)
vax_df[region == 'E40000010', new_region := 'North West']
vax_df[region == 'E40000008', new_region := 'Midlands']
vax_df[region == 'E40000009', new_region := 'North East and Yorkshire']
vax_df[region == 'E40000007', new_region := 'East of England']
vax_df[region == 'E40000003', new_region := 'London']
vax_df[region == 'E40000006', new_region := 'South West']
vax_df[region == 'E40000005', new_region := 'South East']
vax_df[region == 'E40000010', region := 'North West']
vax_df[region == 'E40000008', region := 'Midlands']
vax_df[region == 'E40000009', region := 'North East and Yorkshire']
vax_df[region == 'E40000007', region := 'East of England']
vax_df[region == 'E40000003', region := 'London']
vax_df[region == 'E40000006', region := 'South West']
vax_df[region == 'E40000005', region := 'South East']
vax_df[is.na(region) == TRUE, new_region := NA]
# next: calculate distribution of vaccines into age groups
age_dist = data.table(age_group = NULL, total = NULL)
for (i in 1:(length(agegroups))){
group = agegroups[i]
total = sum(vax_df$number[vax_df$age.group == i])
new_entry = data.table(age_group = group,
total = total)
age_dist = rbind(age_dist, new_entry)
}
age_dist$prop = age_dist$total / sum(age_dist$total)
age_dist$age_group = factor(age_dist$age_group, levels = levels(sv$age_group))
age_dist$cumprobs <- cumsum(age_dist$prop)
qplot(as.factor(age_dist$age_group), age_dist$prop) + theme(axis.text.x = element_text(angle = 90))
# remove any entries with NA number
vax_df = vax_df[!(is.na(vax_df$number) == TRUE),]
counter = 0
if (1){
# distribute vaccines with known region and unknown age
krua_vaxp <- vax_df[is.na(vax_df$ages) == TRUE,]
known_regions <- regions[is.na(regions) == FALSE]
for (i in 1:length(known_regions)){
this_region = known_regions[i]
krua_vaxp_tr <- krua_vaxp[krua_vaxp$new_region == this_region,]
# get non-zero vaccine entries
krua_vaxp_tr_n0 <- krua_vaxp_tr[is.na(krua_vaxp_tr$number) == FALSE,]
krua_vaxp_tr_n0 <- krua_vaxp_tr_n0[krua_vaxp_tr_n0$number > 0,]
if (dim(krua_vaxp_tr_n0)[1] > 0){
for (o in 1:dim(krua_vaxp_tr_n0)[1]){
num_to_distribute = krua_vaxp_tr_n0$number[o]
counter = counter + num_to_distribute
print(paste0('Known region unknown age counter: ', counter))
# loop through number to distribute
rand <- runif(num_to_distribute,0,1)
idx <- rep(1,num_to_distribute)
# recoded_ages <- rep(NA,num_to_distribute)
for (m in 1:num_to_distribute){
for (l in 1:(length(age_dist$cumprobs)-1)){
if (rand[m] > age_dist$cumprobs[l]){
idx[m] <- idx[m] + 1
}
}
# now we need to add krua_vaxp_tr_n0$number[m] to the correct age group in the master df
vax_df$number[vax_df$new_region == this_region &&
vax_df$age.group == idx[m] &&
vax_df$vaccine == krua_vaxp_tr_n0$vaccine[m] &&
vax_df$dose == krua_vaxp_tr_n0$dose[m] &&
vax_df$date == krua_vaxp_tr_n0$date[m]] =
vax_df$number[vax_df$new_region == this_region &&
vax_df$age.group == idx[m] &&
vax_df$vaccine == krua_vaxp_tr_n0$vaccine[m] &&
vax_df$dose == krua_vaxp_tr_n0$dose[m] &&
vax_df$date == krua_vaxp_tr_n0$date[m]] + 1
}
}
}
}
# distribute vaccines with unknown region and known age
urka_vaxp <- vax_df[is.na(vax_df$region) == TRUE,]
urka_vaxp <- urka_vaxp[is.na(urka_vaxp$ages) == FALSE,]
urka_vaxp_n0 <- urka_vaxp[!(is.na(urka_vaxp$number) == TRUE),]
urka_vaxp_n0 <- urka_vaxp_n0[urka_vaxp_n0$number > 0,]
# loop through age groups recorded in urka_vaxp_n0
age_groups_to_loop <- unique(urka_vaxp_n0$ages)
known_regions = unique(vax_df$new_region)
known_regions = known_regions[!(is.na(known_regions))]
if (length(age_groups_to_loop) == 0){
break
} else if (length(age_groups_to_loop) >= 1){
for (i in 1:length(age_groups_to_loop)){
this_age_group <- age_groups_to_loop[i]
# calculate population sizes for this age group across NHS England regions
pop_vec <- NULL
if (this_age_group == "75+"){
print(paste0("Unknown region, known age group: ", this_age_group))
all_pops <- popUK[popUK$age %in% c("75-79","80-84","85-89","90+"),]
for (j in 1:length(known_regions)){
these_pops <- all_pops[all_pops$name == known_regions[j],]
these_pops$all <- these_pops$f + these_pops$m
pop_vec[j] <- sum(these_pops$all)
}
} else {
print(paste0('Unknown region, known age group: ', this_age_group))
all_pops <- popUK[popUK$age == this_age_group,]
for (j in 1:length(known_regions)){
these_pops <- all_pops[all_pops$name == known_regions[j],]
pop_vec[j] <- these_pops$f + these_pops$m
}
}
# print(pop_vec)
# normalise population size vector and calculate cumulative sum for allocation
cs_norm_pop <- cumsum(pop_vec / sum(pop_vec))
# get vaccines to distribute for this age group
to_distribute <- urka_vaxp_n0[urka_vaxp_n0$ages == this_age_group,]
# loop through number of vaccine dates with vaccines to distribute
for (j in 1:length(to_distribute$date)){
this_vaccine <- to_distribute$vaccine[j]
this_dose <- to_distribute$dose[j]
this_date <- to_distribute$date[j]
num_to_distribute <- to_distribute$number[j]
counter = counter + num_to_distribute
print(paste0('Unknown region, known age counter: ', counter))
# print(counter)
# loop through each vaccine of this category that needs to be distributed
for (k in 1:num_to_distribute){
# each dose needs to be randomly allocated to a region
rand <- runif(1,0,1)
idx <- 1
for (l in 1:(length(cs_norm_pop)-1)){
if (rand > cs_norm_pop[l]){
idx <- idx + 1
}
}
region_to_go <- known_regions[idx]
# allocate dose to correct region, age group, vaccine, dose, date
row_idx <- which(vax_df$new_region == region_to_go &
vax_df$ages == this_age_group &
vax_df$vaccine == this_vaccine &
vax_df$dose == this_dose &
vax_df$date == this_date)
if (length(row_idx) > 1){
stop("More than one location for this vaccine to go: code fix required")
}
vax_df$number[row_idx] <- vax_df$number[row_idx] + 1
}
}
}
}
# distribute vaccines with unknown region and unknown age
agegroups <- ages$groups
urua_vaxp <- vax_df[is.na(vax_df$region) == TRUE,]
urua_vaxp <- urua_vaxp[is.na(urua_vaxp$ages) == TRUE,]
urua_vaxp_n0 <- urua_vaxp[urua_vaxp$number > 0,]
# pre calculate probabilities for regions by age group
cs_norm_pop_allages <- list()
cumprobs = age_dist$cumprobs
for (i in 1:length(agegroups)){
this_age_group <- agegroups[i]
# calculate population sizes for this age group across NHS England regions
pop_vec <- NULL
if (this_age_group == "75+"){
all_pops <- popUK[popUK$age %in% c("75-79","80-84","85-89","90+"),]
for (j in 1:length(known_regions)){
these_pops <- all_pops[all_pops$name == known_regions[j],]
these_pops$all <- these_pops$f + these_pops$m
pop_vec[j] <- sum(these_pops$all)
}
} else {
all_pops <- popUK[popUK$age == this_age_group,]
for (j in 1:length(known_regions)){
these_pops <- all_pops[all_pops$name == known_regions[j],]
pop_vec[j] <- these_pops$f + these_pops$m
}
}
# normalise population size vector and calculate cumulative sum for allocation
cs_norm_pop <- cumsum(pop_vec / sum(pop_vec))
# store vector in list at appropriate index
cs_norm_pop_allages[[i]] <- cs_norm_pop
}
# loop through number of entries recorded in urua_vaxp_n0
for (i in 1:dim(urua_vaxp_n0)[1]){
this_vaccine <- urua_vaxp_n0$vaccine[i]
this_dose <- urua_vaxp_n0$dose[i]
this_date <- urua_vaxp_n0$date[i]
num_to_allocate <- urua_vaxp_n0$number[i]
counter = counter + num_to_allocate
# loop through each dose that needs allocating
print(paste0('Unknown region, unknown age counter: ', counter))
for (j in 1:num_to_allocate){
# randomly select an age group for this dose to be allocated to
rand <- runif(1,0,1)
idx <- 1
for (k in 1:(length(cumprobs))){
if (rand > cumprobs[k]){
idx <- idx + 1
}
}
age_group_to_go <- agegroups[idx]
cs_norm_pop <- cs_norm_pop_allages[[idx]]
# select a region for this dose to be allocated to
rand <- runif(1,0,1)
idx <- 1
for (l in 1:(length(cs_norm_pop)-1)){
if (rand > cs_norm_pop[l]){
idx <- idx + 1
}
}
region_to_go <- known_regions[idx]
# allocate dose to correct region, age group, vaccine, dose, date
row_idx <- which(vax_df$new_region == region_to_go &
vax_df$ages == age_group_to_go &
vax_df$vaccine == this_vaccine &
vax_df$dose == this_dose &
vax_df$date == this_date)
if (length(row_idx) > 1){
stop("More than one location for this vaccine to go: code fix required")
}
vax_df$number[row_idx] <- vax_df$number[row_idx] + 1
}
}
# finally, remove entries within vaxp that have region = "Unknown" and age = NA
final_vaxp <- vax_df[!(is.na(vax_df$region) == TRUE),]
final_vaxp <- final_vaxp[!((is.na(final_vaxp$ages))==TRUE),]
# vax_df = final_vaxp
# save processed dataframe as .csv
datetime <- str_replace_all(Sys.time(), "[ :GMTBST-]", "")
write.csv(final_vaxp, file = paste0(path_to_data,
foldername, "/", filedate, "-", datetime,
"-processedvaccinedata-reallocated.csv"),
row.names = FALSE)
}
if(0){
final_vaxp = read.csv('~/Documents/uk_covid_data_sensitive/vaccinations/20220504-20220505195127-processedvaccinedata-reallocated.csv')
}
vax_df_old_copy = vax_df
vax_df = final_vaxp
# calculate actual uptake across England by age
source('./vax_funcs.R')
actuals = calculate_uptake(vax_df, popUK)
first_dose_coverage = actuals[actuals$dose == 1,]
print(first_dose_coverage)
# results below calculated on 5th May 2022 (see coverage column)
# age.group dose date cum_doses age pop coverage
# 1: 1 1 2022-05-02 168 0-4 3239447 0.0000518607
# 2: 2 1 2022-05-02 167035 5-9 3539458 0.0471922537
# 3: 3 1 2022-05-02 1481081 10-14 3435579 0.4311008421
# 4: 4 1 2022-05-02 2604922 15-19 3115871 0.8360172806
# 5: 5 1 2022-05-02 2928173 20-24 3472522 0.8432410219
# 6: 6 1 2022-05-02 3185664 25-29 3771493 0.8446692066
# 7: 7 1 2022-05-02 3453348 30-34 3824652 0.9029182263
# 8: 8 1 2022-05-02 3420699 35-39 3738209 0.9150636040
# 9: 9 1 2022-05-02 3310558 40-44 3476303 0.9523214749
# 10: 10 1 2022-05-02 3399008 45-49 3638639 0.9341426836
# 11: 11 1 2022-05-02 3760555 50-54 3875351 0.9703779090
# 12: 12 1 2022-05-02 3688624 55-59 3761782 0.9805523021
# 13: 13 1 2022-05-02 3183170 60-64 3196813 0.9957323121
# 14: 14 1 2022-05-02 2718777 65-69 2784300 0.9764669755
# 15: 15 1 2022-05-02 2757870 70-74 2814128 0.9800087274
# 16: 16 1 2022-05-02 4734340 75+ 4865591 0.9730246541
# assume existing first dose uptake/coverage limit for individuals aged 15+
uptake = rep(0, 16)
uptake[4:16] = pmin(first_dose_coverage$coverage[4:16],1) # setting existing uptake for ages 15+ (>80% for all age groups)
# assume 80% uptake limit in individuals aged 5-14
uptake[2:3] = 0.8
uptake
# calculate vaccine supply (first doses) by week over time
vax_supply = first_dose_supply(vax_df)
vax_supply$rollmean = zoo::rollmean(vax_supply$V1, 7, fill = NA)
# plot rolling 7-day average number of first doses
plot(as.Date(vax_supply$date), vax_supply$rollmean, type = 'l')
# calculate mean number of vaccines delivered (PER WEEK) in 2022
supp22 = mean(vax_supply$rollmean[vax_supply$date > '2021-12-31'], na.rm = TRUE)*7
print(supp22)
# calculate done on 5th May
# [1] 69465.87
# calculate vector containing future dose supply: each element corresponds to
# the total number of vaccine doses for *one week* in England
# assume 150000 first doses available per week for 35 weeks (takes us from 2nd May 2022 to 2nd January 2023)
num_weeks = 35
doses = c(rep(150000, num_weeks))
dates = seq(as.Date(max(vax_df$date))+1, as.Date(max(vax_df$date))+1+((num_weeks*7)-1), by = 1)
ddoses <- NULL
for (i in 1:(length(doses))){
ddoses[(1+(i-1)*7):(i*7)] <- doses[i]/7
}
last_delivery <- as.Date(max(vax_df$date))
idx <- which (dates == last_delivery)
if (length(idx) > 0){
dates <- dates[-(1:idx)]
ddoses <- ddoses[-(1:idx)]
}
final_doses <- NULL
for (i in 1:ceiling(length(ddoses)/7)){
if (i == ceiling(length(ddoses)/7)){
final_doses[i] = sum(ddoses[(1+(i-1)*7):length(ddoses)])
} else {
final_doses[i] = sum(ddoses[(1+(i-1)*7):(i*7)])
}
}
# calculate data frame containing the proportion of vaccine product types to be
# administered by age group in covidm (currently AstraZeneca, Pfizer, Moderna)
# calculate proportion of product type delivered so far (for all doses and for first doses only)
props <- products_delivered(vax_df, age_group_limit = 11) # age_group_limit = 11 corresponds to ages 50+ only
prop_AZ <- rep(0,16) # no AZ for individuals <40 years old
prop_AZ[9:16] <- 0.6 # 60% AZ for individuals aged 40+
prop_PZ <- rep(0.75,16) # 75% Pfizer for individuals <40 years old
prop_PZ[9:16] <- 0.3 # 30% Pfizer for individuals aged 40+
prop_MD <- rep(0.25,16) # 25% Moderna for individuals <40 years old
prop_MD[9:16] <- 0.1 # 10% Moderna for individuals aged 40+
products <- data.frame(ages = agegroups, AZ = prop_AZ, PZ = prop_PZ, MD = prop_MD)
# define second_doses as TRUE if doses vector contains both first and second
# doses and as FALSE if doses vector contains first doses only
second_doses <- FALSE
# generate default schedule
# check input values are suitable
if (length(uptake) != 16){
stop('Uptake vector should contain 16 values for 16 age groups')
} else if (max(uptake) > 1){
stop('Values in uptake vector must not exceed 1')
} else if (min(uptake) < 0){
stop('Values in uptake vector must be non-negative')
} else if (min(doses) < 0){
stop('Values in doses vector must be non-negative')
} else if (dim(products)[1] != 16){
stop('Products data frame should contain 16 rows for all age groups')
}
# calculate start and end date for future vaccine schedule
start_date <- as.Date(max(vax_df$date))+1
end_date <- start_date + 7*(length(doses))
# make sure dates in vax_df are Date format
vax_df$date = as.Date(vax_df$date)
# remove 'new_region column header from vax_df before next section
vax_df$new_region = NULL
if (second_doses == TRUE){
print('Second doses = TRUE')
stop('Need code for second_doses == TRUE -> see vax_funcs.R')
} else if (second_doses == FALSE){
print('Second doses = FALSE')
# calculate list of dates and number of doses *per day*
vpdates <- dates
vpdoses <- ddoses
# calculate normalised population size vector for NHS England regions
regions <- unique(vax_df$region)
pop_vec <- NULL
for (i in 1:length(regions)){
this_region <- regions[i]
all_pops <- popUK[popUK$name == this_region,]
pop_vec[i] <- sum(all_pops$f) + sum(all_pops$m)
}
pop_vec <- pop_vec / sum(pop_vec)
# calculate total doses administered by age group, add to vax_df
# calculate number of individuals left to dose in each age group,
# vaccine group and dose group
age_groups <- unique(vax_df$ages)
final_df = data.frame(region = NULL, age.group = NULL, ages = NULL,
vaccine = NULL, dose = NULL, date = NULL,
number = NULL, cum_doses = NULL, pop_size = NULL,
max_uptake = NULL, left_to_dose = NULL,
left_to_dose_ut = NULL)
vaccines <- unique(vax_df$vaccine)
doses <- unique(vax_df$dose)
for (i in 1:length(regions)){
this_region <- regions[i]
this_region_data <- vax_df[vax_df$region == this_region,]
this_region_pop <- popUK[popUK$name == this_region,]
for (j in 1:length(age_groups)){
this_age_group <- age_groups[j]
this_data <- this_region_data[this_region_data$ages == this_age_group,]
# note that covidm population sizes are listed in 1000's
if (this_age_group == "75+"){
all_pops <- this_region_pop[this_region_pop$age %in% c("75-79","80-84","85-89","90+"),]
pop_size <- (sum(all_pops$f) + sum(all_pops$m)) * 1000
} else {
this_region_popage <- this_region_pop[this_region_pop$age == this_age_group,]
pop_size <- (this_region_popage$f + this_region_popage$m) * 1000
}
inner_df = data.frame(region = NULL, age.group = NULL,
ages = NULL, vaccine = NULL, dose = NULL,
date = NULL, number = NULL,
cum_doses = NULL, pop_size = NULL,
max_uptake = NULL, left_to_dose = NULL,
left_to_dose_ut = NULL)
# loop through vaccine type and dose number to calculate cumulative doses
for (k in 1:length(vaccines)){
this_vax <- vaccines[k]
this_data_v <- this_data[this_data$vaccine == this_vax,]
for (l in 1:length(doses)){
this_dose <- doses[l]
this_data_vd <- this_data_v[this_data_v$dose == this_dose,]
this_data_vd$cum_doses <- cumsum(this_data_vd$number)
this_data_vd$pop_size <- rep(round(pop_size), length(this_data_vd$cum_doses))
this_data_vd$max_uptake <- round(this_data_vd$pop_size*uptake[j])
inner_df <- rbind(inner_df, this_data_vd)
}
}
# calculate the number of people left to dose in each age group ACROSS
# vaccine products (i.e. AZ+PZ+Moderna), for each dose (first and second)
inner2_df = data.frame(region = NULL, age.group = NULL, ages = NULL,
vaccine = NULL, dose = NULL, date = NULL,
number = NULL, cum_doses = NULL, pop_size = NULL,
max_uptake = NULL, left_to_dose = NULL,
left_to_dose_ut = NULL)
for (k in 1:length(doses)){
this_dose <- doses[k]
this_dose_data <- inner_df[inner_df$dose == this_dose,]
dates_to_loop <- unique(this_dose_data$date)
left_to_dose <- NULL
left_to_dose_ut <- NULL
for (l in 1:length(dates_to_loop)){
this_date <- dates_to_loop[l]
this_date_data <- this_dose_data[this_dose_data$date == this_date,]
left_to_dose[l] <- this_date_data$pop_size[1] - sum(this_date_data$cum_doses)
left_to_dose_ut[l] <- this_date_data$max_uptake[1] - sum(this_date_data$cum_doses)
}
final_this_dose_date <- cbind(this_dose_data,
left_to_dose = rep(left_to_dose, length(vaccines)),
left_to_dose_ut = rep(left_to_dose_ut, length(vaccines)))
inner2_df <- rbind(inner2_df, final_this_dose_date)
}
final_df <- rbind(final_df, inner2_df)
}
}
# remove dose > 1 information in data frame (not required)
final_df <- final_df[final_df$dose == 1,]
final_df$date = as.Date(final_df$date)
# calculate age distribution of vaccines delivered
# get list of age groups in covidm (for England)
# oldest_age <- max(vax$age, na.rm = TRUE)
# min_ages <- c(0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75)
# max_ages <- c(4,9,14,19,24,29,34,39,44,49,54,59,64,69,74,oldest_age)
# ages <- data.frame(groups = agegroups, min_ages = min_ages, max_ages = max_ages)
# breaks = c(0,4.5,9.5,14.5,19.5,24.5,29.5,34.5,39.5,44.5,49.5,54.5,59.5,64.5,69.5,74.5,oldest_age)
# hist <- hist(vax_df$age, freq = FALSE, breaks)
# nbins <- length(breaks) - 1
# binwidths <- diff(breaks)
agegroup_probs <- age_dist$prop
# loop through future dates to add
leftover_doses <- 0
for (i in 1:length(vpdates)){
date_today <- vpdates[i]
date_yesterday <- date_today - 1
doses_today <- vpdoses[i]
doses_per_region <- doses_today * pop_vec
print(date_today)
# now distribute doses per region to each region
for (j in 1:length(regions)){
this_region <- regions[j]
print(this_region)
these_doses <- doses_per_region[j]
# distribute these_doses into age groups for this_region
# IMPORTANT: we assume that the age distribution of vaccines to be delivered
# follows the existing age distribution in the vaccines delivered already,
# starting with the oldest age group to the youngest. Any leftover doses
# get carried over to doses for the next age group down, the
# next region along, or the following date along, or are otherwise
# recorded as a leftover dose
doses_per_group <- round(these_doses * agegroup_probs)
AZ_doses <- round(doses_per_group*products$AZ)
PZ_doses <- round(doses_per_group*products$PZ)
MD_doses <- round(doses_per_group*products$MD)
# get data for this region only
dfr <- final_df[final_df$region == this_region,]
# check if these doses *can* be allocated (cannot exceed either 100% of
# population sizes or vaccine uptake limits e.g. 95% of population size)
# looping from oldest age group to youngest
for (k in length(doses_per_group):1){
# get data for this age group only
this_age_group <- age_groups[k]
print(this_age_group)
dfra <- dfr[dfr$ages == this_age_group,]
# get data from last date of vaccines already delivered
first_dose_data <- dfra[dfra$dose == 1,]
fdAZ_data <- first_dose_data[first_dose_data$vaccine == "AZ",]
fdAZ_data_lastday <- fdAZ_data[fdAZ_data$date == date_yesterday,]
fdPZ_data <- first_dose_data[first_dose_data$vaccine == "PF",]
fdPZ_data_lastday <- fdPZ_data[fdPZ_data$date == date_yesterday,]
fdMD_data <- first_dose_data[first_dose_data$vaccine == "MD",]
fdMD_data_lastday <- fdMD_data[fdMD_data$date == date_yesterday,]
AZ_doses_to_deliver <- max(AZ_doses[k],0)
PZ_doses_to_deliver <- max(PZ_doses[k],0)
MD_doses_to_deliver <- max(MD_doses[k],0)
# check if product types need redistributing here (e.g. AZ vaccines from older
# groups can get carried over down to younger groups, but younger groups do not
# receive AZ vaccines in the real world. We assume vaccine supply is fixed, but
# we can switch across product types without constraints on supply)
product_split_this_age_group = products[k,c(2:4)]
all_doses_to_deliver = sum(AZ_doses_to_deliver,
PZ_doses_to_deliver,
MD_doses_to_deliver)
AZ_doses_to_deliver = round(all_doses_to_deliver*product_split_this_age_group$AZ)
PZ_doses_to_deliver = round(all_doses_to_deliver*product_split_this_age_group$PZ)
MD_doses_to_deliver = round(all_doses_to_deliver*product_split_this_age_group$MD)
# calculate first and second doses to (attempt to) deliver
fdtd_AZ <- AZ_doses_to_deliver
fdtd_PZ <- PZ_doses_to_deliver
fdtd_MD <- MD_doses_to_deliver
# limit doses to the uptake threshold of each age group
# deliver as many as possible first doses for AZ, Pfizer, and Moderna,
# limiting the number of doses to uptake threshold
if (fdAZ_data_lastday$left_to_dose_ut > 0){
if (fdtd_AZ + fdtd_PZ + fdtd_MD > fdAZ_data_lastday$left_to_dose_ut){
# split left_to_dose_ut into proportions of AZ, Pfizer and Moderna
final_fdtd_AZ <- round(products$AZ[k] * fdAZ_data_lastday$left_to_dose_ut)
final_fdtd_PZ <- round(products$PZ[k] * fdAZ_data_lastday$left_to_dose_ut)
final_fdtd_MD <- round(products$MD[k] * fdAZ_data_lastday$left_to_dose_ut)
# shift all remaining second doses to...
if (k > 1){
# next age group down
AZ_doses[k-1] <- AZ_doses[k-1] + (fdtd_AZ - final_fdtd_AZ)
PZ_doses[k-1] <- PZ_doses[k-1] + (fdtd_PZ - final_fdtd_PZ)
MD_doses[k-1] <- MD_doses[k-1] + (fdtd_MD - final_fdtd_MD)
} else if (j < length(regions)){
# next region along
doses_per_region[j+1] <- doses_per_region[j+1] +
(fdtd_AZ - final_fdtd_AZ) +
(fdtd_PZ - final_fdtd_PZ) +
(fdtd_MD - final_fdtd_MD)
} else if (i < length(vpdates)){
# next date along
vpdoses[i+1] <- vpdoses[i+1] +
(fdtd_AZ - final_fdtd_AZ) +
(fdtd_PZ - final_fdtd_PZ) +
(fdtd_MD - final_fdtd_MD)
} else {
# or record leftover doses
leftover_doses <- leftover_doses +
(fdtd_AZ - final_fdtd_AZ) +
(fdtd_PZ - final_fdtd_PZ) +
(fdtd_MD - final_fdtd_MD)
}
# deliver final first doses for AZ, Pfizer and Moderna
new_entries <- data.frame(region = rep(this_region, 3),
age.group = rep(k, 3),
ages = rep(this_age_group, 3),
vaccine = c("AZ", "PF", "MD"),
dose = rep(1, 3),
date = rep(as.Date(date_today), 3),
number = c(final_fdtd_AZ, final_fdtd_PZ, final_fdtd_MD),
cum_doses = c(fdAZ_data_lastday$cum_doses + final_fdtd_AZ,
fdPZ_data_lastday$cum_doses + final_fdtd_PZ,
fdMD_data_lastday$cum_doses + final_fdtd_MD),
pop_size = rep(fdAZ_data_lastday$pop_size, 3),
max_uptake = rep(fdAZ_data_lastday$max_uptake, 3),
left_to_dose = rep(fdAZ_data_lastday$left_to_dose - fdAZ_data_lastday$left_to_dose_ut, 3),
left_to_dose_ut = rep(0, 3))
# bind new entries to final data frame
final_df <- rbind(final_df, new_entries)
} else { # fdtd_AZ + fdtd_PZ + fdtd_MD <= fdAZ_data_lastday$left_to_dose_ut
# deliver first doses for AZ, Pfizer and Moderna
new_entries <- data.frame(region = rep(this_region, 3),
age.group = rep(k, 3),
ages = rep(this_age_group, 3),
vaccine = c("AZ", "PF", "MD"),
dose = rep(1, 3),
date = rep(as.Date(date_today), 3),
number = c(fdtd_AZ, fdtd_PZ, fdtd_MD),
cum_doses = c(fdAZ_data_lastday$cum_doses + fdtd_AZ,
fdPZ_data_lastday$cum_doses + fdtd_PZ,
fdMD_data_lastday$cum_doses + fdtd_MD),
pop_size = rep(fdAZ_data_lastday$pop_size, 3),
max_uptake = rep(fdAZ_data_lastday$max_uptake, 3),
left_to_dose = rep(fdAZ_data_lastday$left_to_dose - (fdtd_AZ+fdtd_PZ+fdtd_MD), 3),
left_to_dose_ut = rep(fdAZ_data_lastday$left_to_dose_ut - (fdtd_AZ+fdtd_PZ+fdtd_MD), 3))
# bind new entries to final data frame
final_df <- rbind(final_df, new_entries)
}
} else {
# fdAZ_data_lastday$left_to_dose_ut <= 0
# record zero first doses getting delivered for AZ, Pfizer and Moderna
new_entries <- data.frame(region = rep(this_region, 3),
age.group = rep(k, 3),
ages = rep(this_age_group, 3),
vaccine = c("AZ", "PF", "MD"),
dose = rep(1, 3),
date = rep(as.Date(date_today), 3),
number = rep(0, 3),
cum_doses = c(fdAZ_data_lastday$cum_doses,
fdPZ_data_lastday$cum_doses,
fdMD_data_lastday$cum_doses),
pop_size = rep(fdAZ_data_lastday$pop_size, 3),
max_uptake = rep(fdAZ_data_lastday$max_uptake, 3),
left_to_dose = rep(fdAZ_data_lastday$left_to_dose, 3),
left_to_dose_ut = rep(fdAZ_data_lastday$left_to_dose_ut, 3))
# bind new entries to final data frame
final_df <- rbind(final_df, new_entries)
# shift all remaining second doses to...
if (k > 1){
# next age group down
AZ_doses[k-1] <- AZ_doses[k-1] + fdtd_AZ
PZ_doses[k-1] <- PZ_doses[k-1] + fdtd_PZ
MD_doses[k-1] <- MD_doses[k-1] + fdtd_MD
} else if (j < length(regions)){
# next region along
doses_per_region[j+1] <- doses_per_region[j+1] + fdtd_AZ + fdtd_PZ + fdtd_MD
} else if (i < length(vpdates)){
# next date along
vpdoses[i+1] <- vpdoses[i+1] + fdtd_AZ + fdtd_PZ + fdtd_MD
} else {
# or record leftover doses
leftover_doses <- leftover_doses + fdtd_AZ + fdtd_PZ + fdtd_MD
}
}
}
}
}
# print leftover doses
print(leftover_doses)
# sort final_df data frame rows by region, age group, vaccine, date
vax_schedule <- final_df[order(final_df$region, final_df$age.group,
final_df$vaccine, final_df$date),]