diff --git a/.DS_Store b/.DS_Store
index 3cddd6b..6b8863d 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/Data/No_pharma_cov/core_table_droprace_filled.csv b/Data/No_pharma_cov/core_table_droprace_filled.csv
deleted file mode 100644
index 3245642..0000000
--- a/Data/No_pharma_cov/core_table_droprace_filled.csv
+++ /dev/null
@@ -1,30241 +0,0 @@
-year_month,age,patient_gender_code,race_code,PCV_combined,COVID_severity,count
-2020-04,0,F,A,PCV13_only,ICU_crit_care,3
-2020-04,0,F,A,PCV13_only,non_resp_severe,0
-2020-04,0,F,A,PCV13_only,non_severe,5
-2020-04,0,F,A,PCV13_only,resp_severe,5
-2020-04,0,F,A,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,F,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,F,A,PCV13_PPSV23_10yrs,non_severe,7
-2020-04,0,F,A,PCV13_PPSV23_10yrs,resp_severe,0
-2020-04,0,F,A,PPSV23_10yrs,ICU_crit_care,3
-2020-04,0,F,A,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,F,A,PPSV23_10yrs,non_severe,17
-2020-04,0,F,A,PPSV23_10yrs,resp_severe,7
-2020-04,0,F,A,U,ICU_crit_care,13
-2020-04,0,F,A,U,non_resp_severe,3
-2020-04,0,F,A,U,non_severe,127
-2020-04,0,F,A,U,resp_severe,82
-2020-04,0,F,B,PCV13_only,ICU_crit_care,13
-2020-04,0,F,B,PCV13_only,non_resp_severe,3
-2020-04,0,F,B,PCV13_only,non_severe,42
-2020-04,0,F,B,PCV13_only,resp_severe,30
-2020-04,0,F,B,PCV13_PPSV23_10yrs,ICU_crit_care,6
-2020-04,0,F,B,PCV13_PPSV23_10yrs,non_resp_severe,1
-2020-04,0,F,B,PCV13_PPSV23_10yrs,non_severe,25
-2020-04,0,F,B,PCV13_PPSV23_10yrs,resp_severe,15
-2020-04,0,F,B,PPSV23_10yrs,ICU_crit_care,16
-2020-04,0,F,B,PPSV23_10yrs,non_resp_severe,6
-2020-04,0,F,B,PPSV23_10yrs,non_severe,48
-2020-04,0,F,B,PPSV23_10yrs,resp_severe,60
-2020-04,0,F,B,U,ICU_crit_care,130
-2020-04,0,F,B,U,non_resp_severe,44
-2020-04,0,F,B,U,non_severe,569
-2020-04,0,F,B,U,resp_severe,509
-2020-04,0,F,H,PCV13_only,ICU_crit_care,9
-2020-04,0,F,H,PCV13_only,non_resp_severe,3
-2020-04,0,F,H,PCV13_only,non_severe,46
-2020-04,0,F,H,PCV13_only,resp_severe,37
-2020-04,0,F,H,PCV13_PPSV23_10yrs,ICU_crit_care,2
-2020-04,0,F,H,PCV13_PPSV23_10yrs,non_resp_severe,3
-2020-04,0,F,H,PCV13_PPSV23_10yrs,non_severe,27
-2020-04,0,F,H,PCV13_PPSV23_10yrs,resp_severe,27
-2020-04,0,F,H,PPSV23_10yrs,ICU_crit_care,18
-2020-04,0,F,H,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,F,H,PPSV23_10yrs,non_severe,63
-2020-04,0,F,H,PPSV23_10yrs,resp_severe,47
-2020-04,0,F,H,U,ICU_crit_care,91
-2020-04,0,F,H,U,non_resp_severe,24
-2020-04,0,F,H,U,non_severe,577
-2020-04,0,F,H,U,resp_severe,413
-2020-04,0,F,U,PCV13_only,ICU_crit_care,14
-2020-04,0,F,U,PCV13_only,non_resp_severe,5
-2020-04,0,F,U,PCV13_only,non_severe,59
-2020-04,0,F,U,PCV13_only,resp_severe,54
-2020-04,0,F,U,PCV13_PPSV23_10yrs,ICU_crit_care,12
-2020-04,0,F,U,PCV13_PPSV23_10yrs,non_resp_severe,1
-2020-04,0,F,U,PCV13_PPSV23_10yrs,non_severe,32
-2020-04,0,F,U,PCV13_PPSV23_10yrs,resp_severe,24
-2020-04,0,F,U,PPSV23_10yrs,ICU_crit_care,36
-2020-04,0,F,U,PPSV23_10yrs,non_resp_severe,6
-2020-04,0,F,U,PPSV23_10yrs,non_severe,115
-2020-04,0,F,U,PPSV23_10yrs,resp_severe,79
-2020-04,0,F,U,U,ICU_crit_care,295
-2020-04,0,F,U,U,non_resp_severe,108
-2020-04,0,F,U,U,non_severe,1375
-2020-04,0,F,U,U,resp_severe,1016
-2020-04,0,F,W,PCV13_only,ICU_crit_care,26
-2020-04,0,F,W,PCV13_only,non_resp_severe,5
-2020-04,0,F,W,PCV13_only,non_severe,124
-2020-04,0,F,W,PCV13_only,resp_severe,74
-2020-04,0,F,W,PCV13_PPSV23_10yrs,ICU_crit_care,15
-2020-04,0,F,W,PCV13_PPSV23_10yrs,non_resp_severe,2
-2020-04,0,F,W,PCV13_PPSV23_10yrs,non_severe,58
-2020-04,0,F,W,PCV13_PPSV23_10yrs,resp_severe,37
-2020-04,0,F,W,PPSV23_10yrs,ICU_crit_care,21
-2020-04,0,F,W,PPSV23_10yrs,non_resp_severe,12
-2020-04,0,F,W,PPSV23_10yrs,non_severe,145
-2020-04,0,F,W,PPSV23_10yrs,resp_severe,119
-2020-04,0,F,W,U,ICU_crit_care,223
-2020-04,0,F,W,U,non_resp_severe,78
-2020-04,0,F,W,U,non_severe,1814
-2020-04,0,F,W,U,resp_severe,1098
-2020-04,0,M,A,PCV13_only,ICU_crit_care,5
-2020-04,0,M,A,PCV13_only,non_resp_severe,1
-2020-04,0,M,A,PCV13_only,non_severe,7
-2020-04,0,M,A,PCV13_only,resp_severe,7
-2020-04,0,M,A,PCV13_PPSV23_10yrs,ICU_crit_care,2
-2020-04,0,M,A,PCV13_PPSV23_10yrs,non_resp_severe,2
-2020-04,0,M,A,PCV13_PPSV23_10yrs,non_severe,11
-2020-04,0,M,A,PCV13_PPSV23_10yrs,resp_severe,8
-2020-04,0,M,A,PPSV23_10yrs,ICU_crit_care,3
-2020-04,0,M,A,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,M,A,PPSV23_10yrs,non_severe,16
-2020-04,0,M,A,PPSV23_10yrs,resp_severe,12
-2020-04,0,M,A,U,ICU_crit_care,54
-2020-04,0,M,A,U,non_resp_severe,8
-2020-04,0,M,A,U,non_severe,134
-2020-04,0,M,A,U,resp_severe,111
-2020-04,0,M,B,PCV13_only,ICU_crit_care,9
-2020-04,0,M,B,PCV13_only,non_resp_severe,2
-2020-04,0,M,B,PCV13_only,non_severe,18
-2020-04,0,M,B,PCV13_only,resp_severe,21
-2020-04,0,M,B,PCV13_PPSV23_10yrs,ICU_crit_care,8
-2020-04,0,M,B,PCV13_PPSV23_10yrs,non_resp_severe,4
-2020-04,0,M,B,PCV13_PPSV23_10yrs,non_severe,16
-2020-04,0,M,B,PCV13_PPSV23_10yrs,resp_severe,12
-2020-04,0,M,B,PPSV23_10yrs,ICU_crit_care,17
-2020-04,0,M,B,PPSV23_10yrs,non_resp_severe,3
-2020-04,0,M,B,PPSV23_10yrs,non_severe,36
-2020-04,0,M,B,PPSV23_10yrs,resp_severe,37
-2020-04,0,M,B,U,ICU_crit_care,137
-2020-04,0,M,B,U,non_resp_severe,54
-2020-04,0,M,B,U,non_severe,376
-2020-04,0,M,B,U,resp_severe,376
-2020-04,0,M,H,PCV13_only,ICU_crit_care,9
-2020-04,0,M,H,PCV13_only,non_resp_severe,2
-2020-04,0,M,H,PCV13_only,non_severe,41
-2020-04,0,M,H,PCV13_only,resp_severe,28
-2020-04,0,M,H,PCV13_PPSV23_10yrs,ICU_crit_care,5
-2020-04,0,M,H,PCV13_PPSV23_10yrs,non_resp_severe,3
-2020-04,0,M,H,PCV13_PPSV23_10yrs,non_severe,14
-2020-04,0,M,H,PCV13_PPSV23_10yrs,resp_severe,15
-2020-04,0,M,H,PPSV23_10yrs,ICU_crit_care,20
-2020-04,0,M,H,PPSV23_10yrs,non_resp_severe,2
-2020-04,0,M,H,PPSV23_10yrs,non_severe,58
-2020-04,0,M,H,PPSV23_10yrs,resp_severe,54
-2020-04,0,M,H,U,ICU_crit_care,212
-2020-04,0,M,H,U,non_resp_severe,30
-2020-04,0,M,H,U,non_severe,499
-2020-04,0,M,H,U,resp_severe,485
-2020-04,0,M,U,PCV13_only,ICU_crit_care,21
-2020-04,0,M,U,PCV13_only,non_resp_severe,10
-2020-04,0,M,U,PCV13_only,non_severe,67
-2020-04,0,M,U,PCV13_only,resp_severe,74
-2020-04,0,M,U,PCV13_PPSV23_10yrs,ICU_crit_care,9
-2020-04,0,M,U,PCV13_PPSV23_10yrs,non_resp_severe,2
-2020-04,0,M,U,PCV13_PPSV23_10yrs,non_severe,25
-2020-04,0,M,U,PCV13_PPSV23_10yrs,resp_severe,22
-2020-04,0,M,U,PPSV23_10yrs,ICU_crit_care,44
-2020-04,0,M,U,PPSV23_10yrs,non_resp_severe,13
-2020-04,0,M,U,PPSV23_10yrs,non_severe,120
-2020-04,0,M,U,PPSV23_10yrs,resp_severe,82
-2020-04,0,M,U,U,ICU_crit_care,617
-2020-04,0,M,U,U,non_resp_severe,177
-2020-04,0,M,U,U,non_severe,1506
-2020-04,0,M,U,U,resp_severe,1442
-2020-04,0,M,W,PCV13_only,ICU_crit_care,37
-2020-04,0,M,W,PCV13_only,non_resp_severe,5
-2020-04,0,M,W,PCV13_only,non_severe,92
-2020-04,0,M,W,PCV13_only,resp_severe,71
-2020-04,0,M,W,PCV13_PPSV23_10yrs,ICU_crit_care,7
-2020-04,0,M,W,PCV13_PPSV23_10yrs,non_resp_severe,4
-2020-04,0,M,W,PCV13_PPSV23_10yrs,non_severe,53
-2020-04,0,M,W,PCV13_PPSV23_10yrs,resp_severe,41
-2020-04,0,M,W,PPSV23_10yrs,ICU_crit_care,34
-2020-04,0,M,W,PPSV23_10yrs,non_resp_severe,10
-2020-04,0,M,W,PPSV23_10yrs,non_severe,132
-2020-04,0,M,W,PPSV23_10yrs,resp_severe,111
-2020-04,0,M,W,U,ICU_crit_care,425
-2020-04,0,M,W,U,non_resp_severe,136
-2020-04,0,M,W,U,non_severe,1495
-2020-04,0,M,W,U,resp_severe,1199
-2020-04,0,U,A,PCV13_only,ICU_crit_care,0
-2020-04,0,U,A,PCV13_only,non_resp_severe,0
-2020-04,0,U,A,PCV13_only,non_severe,0
-2020-04,0,U,A,PCV13_only,resp_severe,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,non_severe,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,resp_severe,0
-2020-04,0,U,A,PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,A,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,A,PPSV23_10yrs,non_severe,0
-2020-04,0,U,A,PPSV23_10yrs,resp_severe,0
-2020-04,0,U,A,U,ICU_crit_care,0
-2020-04,0,U,A,U,non_resp_severe,0
-2020-04,0,U,A,U,non_severe,1
-2020-04,0,U,A,U,resp_severe,0
-2020-04,0,U,B,PCV13_only,ICU_crit_care,0
-2020-04,0,U,B,PCV13_only,non_resp_severe,0
-2020-04,0,U,B,PCV13_only,non_severe,0
-2020-04,0,U,B,PCV13_only,resp_severe,0
-2020-04,0,U,B,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,B,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,B,PCV13_PPSV23_10yrs,non_severe,0
-2020-04,0,U,B,PCV13_PPSV23_10yrs,resp_severe,0
-2020-04,0,U,B,PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,B,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,B,PPSV23_10yrs,non_severe,0
-2020-04,0,U,B,PPSV23_10yrs,resp_severe,0
-2020-04,0,U,B,U,ICU_crit_care,3
-2020-04,0,U,B,U,non_resp_severe,0
-2020-04,0,U,B,U,non_severe,3
-2020-04,0,U,B,U,resp_severe,4
-2020-04,0,U,H,PCV13_only,ICU_crit_care,0
-2020-04,0,U,H,PCV13_only,non_resp_severe,1
-2020-04,0,U,H,PCV13_only,non_severe,0
-2020-04,0,U,H,PCV13_only,resp_severe,0
-2020-04,0,U,H,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,H,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,H,PCV13_PPSV23_10yrs,non_severe,0
-2020-04,0,U,H,PCV13_PPSV23_10yrs,resp_severe,0
-2020-04,0,U,H,PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,H,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,H,PPSV23_10yrs,non_severe,0
-2020-04,0,U,H,PPSV23_10yrs,resp_severe,0
-2020-04,0,U,H,U,ICU_crit_care,0
-2020-04,0,U,H,U,non_resp_severe,2
-2020-04,0,U,H,U,non_severe,4
-2020-04,0,U,H,U,resp_severe,1
-2020-04,0,U,U,PCV13_only,ICU_crit_care,0
-2020-04,0,U,U,PCV13_only,non_resp_severe,0
-2020-04,0,U,U,PCV13_only,non_severe,0
-2020-04,0,U,U,PCV13_only,resp_severe,2
-2020-04,0,U,U,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,U,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,U,PCV13_PPSV23_10yrs,non_severe,0
-2020-04,0,U,U,PCV13_PPSV23_10yrs,resp_severe,0
-2020-04,0,U,U,PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,U,PPSV23_10yrs,non_resp_severe,3
-2020-04,0,U,U,PPSV23_10yrs,non_severe,0
-2020-04,0,U,U,PPSV23_10yrs,resp_severe,0
-2020-04,0,U,U,U,ICU_crit_care,0
-2020-04,0,U,U,U,non_resp_severe,0
-2020-04,0,U,U,U,non_severe,6
-2020-04,0,U,U,U,resp_severe,5
-2020-04,0,U,W,PCV13_only,ICU_crit_care,0
-2020-04,0,U,W,PCV13_only,non_resp_severe,0
-2020-04,0,U,W,PCV13_only,non_severe,1
-2020-04,0,U,W,PCV13_only,resp_severe,2
-2020-04,0,U,W,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,W,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,W,PCV13_PPSV23_10yrs,non_severe,2
-2020-04,0,U,W,PCV13_PPSV23_10yrs,resp_severe,0
-2020-04,0,U,W,PPSV23_10yrs,ICU_crit_care,3
-2020-04,0,U,W,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,W,PPSV23_10yrs,non_severe,3
-2020-04,0,U,W,PPSV23_10yrs,resp_severe,0
-2020-04,0,U,W,U,ICU_crit_care,1
-2020-04,0,U,W,U,non_resp_severe,0
-2020-04,0,U,W,U,non_severe,3
-2020-04,0,U,W,U,resp_severe,5
-2020-04,1,F,A,PCV13_only,ICU_crit_care,2
-2020-04,1,F,A,PCV13_only,non_resp_severe,0
-2020-04,1,F,A,PCV13_only,non_severe,6
-2020-04,1,F,A,PCV13_only,resp_severe,9
-2020-04,1,F,A,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,1,F,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,1,F,A,PCV13_PPSV23_10yrs,non_severe,6
-2020-04,1,F,A,PCV13_PPSV23_10yrs,resp_severe,7
-2020-04,1,F,A,PPSV23_10yrs,ICU_crit_care,2
-2020-04,1,F,A,PPSV23_10yrs,non_resp_severe,0
-2020-04,1,F,A,PPSV23_10yrs,non_severe,11
-2020-04,1,F,A,PPSV23_10yrs,resp_severe,5
-2020-04,1,F,A,U,ICU_crit_care,18
-2020-04,1,F,A,U,non_resp_severe,1
-2020-04,1,F,A,U,non_severe,64
-2020-04,1,F,A,U,resp_severe,44
-2020-04,1,F,B,PCV13_only,ICU_crit_care,9
-2020-04,1,F,B,PCV13_only,non_resp_severe,5
-2020-04,1,F,B,PCV13_only,non_severe,37
-2020-04,1,F,B,PCV13_only,resp_severe,34
-2020-04,1,F,B,PCV13_PPSV23_10yrs,ICU_crit_care,8
-2020-04,1,F,B,PCV13_PPSV23_10yrs,non_resp_severe,6
-2020-04,1,F,B,PCV13_PPSV23_10yrs,non_severe,24
-2020-04,1,F,B,PCV13_PPSV23_10yrs,resp_severe,37
-2020-04,1,F,B,PPSV23_10yrs,ICU_crit_care,13
-2020-04,1,F,B,PPSV23_10yrs,non_resp_severe,8
-2020-04,1,F,B,PPSV23_10yrs,non_severe,37
-2020-04,1,F,B,PPSV23_10yrs,resp_severe,45
-2020-04,1,F,B,U,ICU_crit_care,96
-2020-04,1,F,B,U,non_resp_severe,50
-2020-04,1,F,B,U,non_severe,304
-2020-04,1,F,B,U,resp_severe,363
-2020-04,1,F,H,PCV13_only,ICU_crit_care,11
-2020-04,1,F,H,PCV13_only,non_resp_severe,1
-2020-04,1,F,H,PCV13_only,non_severe,45
-2020-04,1,F,H,PCV13_only,resp_severe,46
-2020-04,1,F,H,PCV13_PPSV23_10yrs,ICU_crit_care,4
-2020-04,1,F,H,PCV13_PPSV23_10yrs,non_resp_severe,3
-2020-04,1,F,H,PCV13_PPSV23_10yrs,non_severe,28
-2020-04,1,F,H,PCV13_PPSV23_10yrs,resp_severe,27
-2020-04,1,F,H,PPSV23_10yrs,ICU_crit_care,18
-2020-04,1,F,H,PPSV23_10yrs,non_resp_severe,2
-2020-04,1,F,H,PPSV23_10yrs,non_severe,38
-2020-04,1,F,H,PPSV23_10yrs,resp_severe,34
-2020-04,1,F,H,U,ICU_crit_care,78
-2020-04,1,F,H,U,non_resp_severe,18
-2020-04,1,F,H,U,non_severe,345
-2020-04,1,F,H,U,resp_severe,298
-2020-04,1,F,U,PCV13_only,ICU_crit_care,22
-2020-04,1,F,U,PCV13_only,non_resp_severe,6
-2020-04,1,F,U,PCV13_only,non_severe,67
-2020-04,1,F,U,PCV13_only,resp_severe,74
-2020-04,1,F,U,PCV13_PPSV23_10yrs,ICU_crit_care,9
-2020-04,1,F,U,PCV13_PPSV23_10yrs,non_resp_severe,3
-2020-04,1,F,U,PCV13_PPSV23_10yrs,non_severe,38
-2020-04,1,F,U,PCV13_PPSV23_10yrs,resp_severe,24
-2020-04,1,F,U,PPSV23_10yrs,ICU_crit_care,32
-2020-04,1,F,U,PPSV23_10yrs,non_resp_severe,9
-2020-04,1,F,U,PPSV23_10yrs,non_severe,81
-2020-04,1,F,U,PPSV23_10yrs,resp_severe,65
-2020-04,1,F,U,U,ICU_crit_care,276
-2020-04,1,F,U,U,non_resp_severe,116
-2020-04,1,F,U,U,non_severe,930
-2020-04,1,F,U,U,resp_severe,848
-2020-04,1,F,W,PCV13_only,ICU_crit_care,29
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diff --git a/Data/No_pharma_cov/core_table_filled.csv b/Data/No_pharma_cov/core_table_filled.csv
deleted file mode 100644
index 3245642..0000000
--- a/Data/No_pharma_cov/core_table_filled.csv
+++ /dev/null
@@ -1,30241 +0,0 @@
-year_month,age,patient_gender_code,race_code,PCV_combined,COVID_severity,count
-2020-04,0,F,A,PCV13_only,ICU_crit_care,3
-2020-04,0,F,A,PCV13_only,non_resp_severe,0
-2020-04,0,F,A,PCV13_only,non_severe,5
-2020-04,0,F,A,PCV13_only,resp_severe,5
-2020-04,0,F,A,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,F,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,F,A,PCV13_PPSV23_10yrs,non_severe,7
-2020-04,0,F,A,PCV13_PPSV23_10yrs,resp_severe,0
-2020-04,0,F,A,PPSV23_10yrs,ICU_crit_care,3
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diff --git a/Data/No_pharma_cov/suppl_6mo_table_filled.csv b/Data/No_pharma_cov/suppl_6mo_table_filled.csv
deleted file mode 100644
index 723fb5e..0000000
--- a/Data/No_pharma_cov/suppl_6mo_table_filled.csv
+++ /dev/null
@@ -1,368641 +0,0 @@
-six_mo,age,patient_gender_code,race_code,flu_vacc,zoster_vacc,bmi_30_plus,comorbidities,income_est_mod,PCV_combined,COVID_severity,count
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-4,5,U,W,TRUE,TRUE,TRUE,FALSE,2,PCV13_PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,2,PCV13_PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,2,PPSV23_10yrs,ICU_crit_care,0
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-4,5,U,W,TRUE,TRUE,TRUE,FALSE,2,U,ICU_crit_care,0
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-4,5,U,W,TRUE,TRUE,TRUE,FALSE,2,U,resp_severe,0
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-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,PCV13_only,non_severe,0
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-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,PCV13_PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,PCV13_PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,PCV13_PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,PCV13_PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,U,ICU_crit_care,0
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-4,5,U,W,TRUE,TRUE,TRUE,FALSE,U,U,non_severe,0
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-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PCV13_only,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PCV13_only,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PCV13_only,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PCV13_PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PCV13_PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PCV13_PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PCV13_PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,U,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,U,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,0,U,non_severe,0
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-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,PCV13_only,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,PCV13_PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,PCV13_PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,PCV13_PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,PCV13_PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,U,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,U,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,U,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,1,U,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PCV13_only,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PCV13_only,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PCV13_only,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PCV13_only,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PCV13_PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PCV13_PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PCV13_PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PCV13_PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,U,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,U,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,2,U,non_severe,0
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-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PCV13_only,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PCV13_only,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PCV13_only,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PCV13_only,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PCV13_PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PCV13_PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PCV13_PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PCV13_PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PPSV23_10yrs,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PPSV23_10yrs,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PPSV23_10yrs,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PPSV23_10yrs,resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,ICU_crit_care,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,resp_severe,0
diff --git a/Data/core_table_droprace_filled.csv b/Data/core_table_droprace_filled.csv
deleted file mode 100644
index 1dbe6aa..0000000
--- a/Data/core_table_droprace_filled.csv
+++ /dev/null
@@ -1,30241 +0,0 @@
-year_month,age,patient_gender_code,race_code,PCV_combined,COVID_severity,count
-2020-04,0,F,A,PCV13_only,ICU_crit_care,0
-2020-04,0,F,A,PCV13_only,non_resp_severe,0
-2020-04,0,F,A,PCV13_only,non_severe,5
-2020-04,0,F,A,PCV13_only,resp_severe,6
-2020-04,0,F,A,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,F,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,F,A,PCV13_PPSV23_10yrs,non_severe,6
-2020-04,0,F,A,PCV13_PPSV23_10yrs,resp_severe,3
-2020-04,0,F,A,PPSV23_10yrs,ICU_crit_care,3
-2020-04,0,F,A,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,F,A,PPSV23_10yrs,non_severe,9
-2020-04,0,F,A,PPSV23_10yrs,resp_severe,2
-2020-04,0,F,A,U,ICU_crit_care,21
-2020-04,0,F,A,U,non_resp_severe,3
-2020-04,0,F,A,U,non_severe,88
-2020-04,0,F,A,U,resp_severe,69
-2020-04,0,F,B,PCV13_only,ICU_crit_care,17
-2020-04,0,F,B,PCV13_only,non_resp_severe,3
-2020-04,0,F,B,PCV13_only,non_severe,41
-2020-04,0,F,B,PCV13_only,resp_severe,30
-2020-04,0,F,B,PCV13_PPSV23_10yrs,ICU_crit_care,4
-2020-04,0,F,B,PCV13_PPSV23_10yrs,non_resp_severe,1
-2020-04,0,F,B,PCV13_PPSV23_10yrs,non_severe,24
-2020-04,0,F,B,PCV13_PPSV23_10yrs,resp_severe,19
-2020-04,0,F,B,PPSV23_10yrs,ICU_crit_care,11
-2020-04,0,F,B,PPSV23_10yrs,non_resp_severe,5
-2020-04,0,F,B,PPSV23_10yrs,non_severe,28
-2020-04,0,F,B,PPSV23_10yrs,resp_severe,29
-2020-04,0,F,B,U,ICU_crit_care,98
-2020-04,0,F,B,U,non_resp_severe,33
-2020-04,0,F,B,U,non_severe,441
-2020-04,0,F,B,U,resp_severe,450
-2020-04,0,F,H,PCV13_only,ICU_crit_care,10
-2020-04,0,F,H,PCV13_only,non_resp_severe,3
-2020-04,0,F,H,PCV13_only,non_severe,52
-2020-04,0,F,H,PCV13_only,resp_severe,51
-2020-04,0,F,H,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,F,H,PCV13_PPSV23_10yrs,non_resp_severe,2
-2020-04,0,F,H,PCV13_PPSV23_10yrs,non_severe,17
-2020-04,0,F,H,PCV13_PPSV23_10yrs,resp_severe,19
-2020-04,0,F,H,PPSV23_10yrs,ICU_crit_care,21
-2020-04,0,F,H,PPSV23_10yrs,non_resp_severe,2
-2020-04,0,F,H,PPSV23_10yrs,non_severe,44
-2020-04,0,F,H,PPSV23_10yrs,resp_severe,27
-2020-04,0,F,H,U,ICU_crit_care,82
-2020-04,0,F,H,U,non_resp_severe,17
-2020-04,0,F,H,U,non_severe,467
-2020-04,0,F,H,U,resp_severe,391
-2020-04,0,F,U,PCV13_only,ICU_crit_care,25
-2020-04,0,F,U,PCV13_only,non_resp_severe,4
-2020-04,0,F,U,PCV13_only,non_severe,67
-2020-04,0,F,U,PCV13_only,resp_severe,54
-2020-04,0,F,U,PCV13_PPSV23_10yrs,ICU_crit_care,9
-2020-04,0,F,U,PCV13_PPSV23_10yrs,non_resp_severe,4
-2020-04,0,F,U,PCV13_PPSV23_10yrs,non_severe,22
-2020-04,0,F,U,PCV13_PPSV23_10yrs,resp_severe,20
-2020-04,0,F,U,PPSV23_10yrs,ICU_crit_care,25
-2020-04,0,F,U,PPSV23_10yrs,non_resp_severe,1
-2020-04,0,F,U,PPSV23_10yrs,non_severe,71
-2020-04,0,F,U,PPSV23_10yrs,resp_severe,53
-2020-04,0,F,U,U,ICU_crit_care,307
-2020-04,0,F,U,U,non_resp_severe,100
-2020-04,0,F,U,U,non_severe,1173
-2020-04,0,F,U,U,resp_severe,943
-2020-04,0,F,W,PCV13_only,ICU_crit_care,29
-2020-04,0,F,W,PCV13_only,non_resp_severe,9
-2020-04,0,F,W,PCV13_only,non_severe,124
-2020-04,0,F,W,PCV13_only,resp_severe,84
-2020-04,0,F,W,PCV13_PPSV23_10yrs,ICU_crit_care,14
-2020-04,0,F,W,PCV13_PPSV23_10yrs,non_resp_severe,2
-2020-04,0,F,W,PCV13_PPSV23_10yrs,non_severe,46
-2020-04,0,F,W,PCV13_PPSV23_10yrs,resp_severe,42
-2020-04,0,F,W,PPSV23_10yrs,ICU_crit_care,10
-2020-04,0,F,W,PPSV23_10yrs,non_resp_severe,5
-2020-04,0,F,W,PPSV23_10yrs,non_severe,73
-2020-04,0,F,W,PPSV23_10yrs,resp_severe,68
-2020-04,0,F,W,U,ICU_crit_care,217
-2020-04,0,F,W,U,non_resp_severe,88
-2020-04,0,F,W,U,non_severe,1421
-2020-04,0,F,W,U,resp_severe,936
-2020-04,0,M,A,PCV13_only,ICU_crit_care,4
-2020-04,0,M,A,PCV13_only,non_resp_severe,1
-2020-04,0,M,A,PCV13_only,non_severe,6
-2020-04,0,M,A,PCV13_only,resp_severe,8
-2020-04,0,M,A,PCV13_PPSV23_10yrs,ICU_crit_care,2
-2020-04,0,M,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,M,A,PCV13_PPSV23_10yrs,non_severe,2
-2020-04,0,M,A,PCV13_PPSV23_10yrs,resp_severe,10
-2020-04,0,M,A,PPSV23_10yrs,ICU_crit_care,5
-2020-04,0,M,A,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,M,A,PPSV23_10yrs,non_severe,6
-2020-04,0,M,A,PPSV23_10yrs,resp_severe,5
-2020-04,0,M,A,U,ICU_crit_care,52
-2020-04,0,M,A,U,non_resp_severe,10
-2020-04,0,M,A,U,non_severe,84
-2020-04,0,M,A,U,resp_severe,93
-2020-04,0,M,B,PCV13_only,ICU_crit_care,7
-2020-04,0,M,B,PCV13_only,non_resp_severe,4
-2020-04,0,M,B,PCV13_only,non_severe,26
-2020-04,0,M,B,PCV13_only,resp_severe,32
-2020-04,0,M,B,PCV13_PPSV23_10yrs,ICU_crit_care,4
-2020-04,0,M,B,PCV13_PPSV23_10yrs,non_resp_severe,3
-2020-04,0,M,B,PCV13_PPSV23_10yrs,non_severe,14
-2020-04,0,M,B,PCV13_PPSV23_10yrs,resp_severe,10
-2020-04,0,M,B,PPSV23_10yrs,ICU_crit_care,14
-2020-04,0,M,B,PPSV23_10yrs,non_resp_severe,5
-2020-04,0,M,B,PPSV23_10yrs,non_severe,25
-2020-04,0,M,B,PPSV23_10yrs,resp_severe,32
-2020-04,0,M,B,U,ICU_crit_care,125
-2020-04,0,M,B,U,non_resp_severe,52
-2020-04,0,M,B,U,non_severe,320
-2020-04,0,M,B,U,resp_severe,345
-2020-04,0,M,H,PCV13_only,ICU_crit_care,13
-2020-04,0,M,H,PCV13_only,non_resp_severe,2
-2020-04,0,M,H,PCV13_only,non_severe,34
-2020-04,0,M,H,PCV13_only,resp_severe,31
-2020-04,0,M,H,PCV13_PPSV23_10yrs,ICU_crit_care,4
-2020-04,0,M,H,PCV13_PPSV23_10yrs,non_resp_severe,1
-2020-04,0,M,H,PCV13_PPSV23_10yrs,non_severe,10
-2020-04,0,M,H,PCV13_PPSV23_10yrs,resp_severe,13
-2020-04,0,M,H,PPSV23_10yrs,ICU_crit_care,13
-2020-04,0,M,H,PPSV23_10yrs,non_resp_severe,3
-2020-04,0,M,H,PPSV23_10yrs,non_severe,44
-2020-04,0,M,H,PPSV23_10yrs,resp_severe,39
-2020-04,0,M,H,U,ICU_crit_care,186
-2020-04,0,M,H,U,non_resp_severe,29
-2020-04,0,M,H,U,non_severe,435
-2020-04,0,M,H,U,resp_severe,414
-2020-04,0,M,U,PCV13_only,ICU_crit_care,36
-2020-04,0,M,U,PCV13_only,non_resp_severe,9
-2020-04,0,M,U,PCV13_only,non_severe,69
-2020-04,0,M,U,PCV13_only,resp_severe,77
-2020-04,0,M,U,PCV13_PPSV23_10yrs,ICU_crit_care,7
-2020-04,0,M,U,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,M,U,PCV13_PPSV23_10yrs,non_severe,24
-2020-04,0,M,U,PCV13_PPSV23_10yrs,resp_severe,26
-2020-04,0,M,U,PPSV23_10yrs,ICU_crit_care,46
-2020-04,0,M,U,PPSV23_10yrs,non_resp_severe,10
-2020-04,0,M,U,PPSV23_10yrs,non_severe,80
-2020-04,0,M,U,PPSV23_10yrs,resp_severe,75
-2020-04,0,M,U,U,ICU_crit_care,596
-2020-04,0,M,U,U,non_resp_severe,174
-2020-04,0,M,U,U,non_severe,1331
-2020-04,0,M,U,U,resp_severe,1362
-2020-04,0,M,W,PCV13_only,ICU_crit_care,34
-2020-04,0,M,W,PCV13_only,non_resp_severe,7
-2020-04,0,M,W,PCV13_only,non_severe,113
-2020-04,0,M,W,PCV13_only,resp_severe,77
-2020-04,0,M,W,PCV13_PPSV23_10yrs,ICU_crit_care,11
-2020-04,0,M,W,PCV13_PPSV23_10yrs,non_resp_severe,3
-2020-04,0,M,W,PCV13_PPSV23_10yrs,non_severe,38
-2020-04,0,M,W,PCV13_PPSV23_10yrs,resp_severe,36
-2020-04,0,M,W,PPSV23_10yrs,ICU_crit_care,24
-2020-04,0,M,W,PPSV23_10yrs,non_resp_severe,7
-2020-04,0,M,W,PPSV23_10yrs,non_severe,72
-2020-04,0,M,W,PPSV23_10yrs,resp_severe,59
-2020-04,0,M,W,U,ICU_crit_care,394
-2020-04,0,M,W,U,non_resp_severe,120
-2020-04,0,M,W,U,non_severe,1279
-2020-04,0,M,W,U,resp_severe,1080
-2020-04,0,U,A,PCV13_only,ICU_crit_care,0
-2020-04,0,U,A,PCV13_only,non_resp_severe,0
-2020-04,0,U,A,PCV13_only,non_severe,0
-2020-04,0,U,A,PCV13_only,resp_severe,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,non_severe,0
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-2021-12,5,M,H,U,non_severe,90
-2021-12,5,M,H,U,resp_severe,22
-2021-12,5,M,U,PCV13_only,ICU_crit_care,10
-2021-12,5,M,U,PCV13_only,non_resp_severe,12
-2021-12,5,M,U,PCV13_only,non_severe,58
-2021-12,5,M,U,PCV13_only,resp_severe,26
-2021-12,5,M,U,PCV13_PPSV23_10yrs,ICU_crit_care,5
-2021-12,5,M,U,PCV13_PPSV23_10yrs,non_resp_severe,2
-2021-12,5,M,U,PCV13_PPSV23_10yrs,non_severe,19
-2021-12,5,M,U,PCV13_PPSV23_10yrs,resp_severe,7
-2021-12,5,M,U,PPSV23_10yrs,ICU_crit_care,6
-2021-12,5,M,U,PPSV23_10yrs,non_resp_severe,9
-2021-12,5,M,U,PPSV23_10yrs,non_severe,44
-2021-12,5,M,U,PPSV23_10yrs,resp_severe,26
-2021-12,5,M,U,U,ICU_crit_care,133
-2021-12,5,M,U,U,non_resp_severe,250
-2021-12,5,M,U,U,non_severe,1161
-2021-12,5,M,U,U,resp_severe,408
-2021-12,5,M,W,PCV13_only,ICU_crit_care,8
-2021-12,5,M,W,PCV13_only,non_resp_severe,11
-2021-12,5,M,W,PCV13_only,non_severe,50
-2021-12,5,M,W,PCV13_only,resp_severe,18
-2021-12,5,M,W,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,M,W,PCV13_PPSV23_10yrs,non_resp_severe,1
-2021-12,5,M,W,PCV13_PPSV23_10yrs,non_severe,7
-2021-12,5,M,W,PCV13_PPSV23_10yrs,resp_severe,4
-2021-12,5,M,W,PPSV23_10yrs,ICU_crit_care,1
-2021-12,5,M,W,PPSV23_10yrs,non_resp_severe,4
-2021-12,5,M,W,PPSV23_10yrs,non_severe,21
-2021-12,5,M,W,PPSV23_10yrs,resp_severe,7
-2021-12,5,M,W,U,ICU_crit_care,59
-2021-12,5,M,W,U,non_resp_severe,104
-2021-12,5,M,W,U,non_severe,549
-2021-12,5,M,W,U,resp_severe,193
-2021-12,5,U,A,PCV13_only,ICU_crit_care,0
-2021-12,5,U,A,PCV13_only,non_resp_severe,0
-2021-12,5,U,A,PCV13_only,non_severe,0
-2021-12,5,U,A,PCV13_only,resp_severe,0
-2021-12,5,U,A,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,A,PCV13_PPSV23_10yrs,non_severe,0
-2021-12,5,U,A,PCV13_PPSV23_10yrs,resp_severe,0
-2021-12,5,U,A,PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,A,PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,A,PPSV23_10yrs,non_severe,0
-2021-12,5,U,A,PPSV23_10yrs,resp_severe,0
-2021-12,5,U,A,U,ICU_crit_care,0
-2021-12,5,U,A,U,non_resp_severe,0
-2021-12,5,U,A,U,non_severe,1
-2021-12,5,U,A,U,resp_severe,0
-2021-12,5,U,B,PCV13_only,ICU_crit_care,0
-2021-12,5,U,B,PCV13_only,non_resp_severe,0
-2021-12,5,U,B,PCV13_only,non_severe,0
-2021-12,5,U,B,PCV13_only,resp_severe,0
-2021-12,5,U,B,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,B,PCV13_PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,B,PCV13_PPSV23_10yrs,non_severe,0
-2021-12,5,U,B,PCV13_PPSV23_10yrs,resp_severe,0
-2021-12,5,U,B,PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,B,PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,B,PPSV23_10yrs,non_severe,0
-2021-12,5,U,B,PPSV23_10yrs,resp_severe,0
-2021-12,5,U,B,U,ICU_crit_care,0
-2021-12,5,U,B,U,non_resp_severe,0
-2021-12,5,U,B,U,non_severe,0
-2021-12,5,U,B,U,resp_severe,0
-2021-12,5,U,H,PCV13_only,ICU_crit_care,0
-2021-12,5,U,H,PCV13_only,non_resp_severe,0
-2021-12,5,U,H,PCV13_only,non_severe,0
-2021-12,5,U,H,PCV13_only,resp_severe,0
-2021-12,5,U,H,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,H,PCV13_PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,H,PCV13_PPSV23_10yrs,non_severe,0
-2021-12,5,U,H,PCV13_PPSV23_10yrs,resp_severe,0
-2021-12,5,U,H,PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,H,PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,H,PPSV23_10yrs,non_severe,0
-2021-12,5,U,H,PPSV23_10yrs,resp_severe,3
-2021-12,5,U,H,U,ICU_crit_care,0
-2021-12,5,U,H,U,non_resp_severe,0
-2021-12,5,U,H,U,non_severe,1
-2021-12,5,U,H,U,resp_severe,0
-2021-12,5,U,U,PCV13_only,ICU_crit_care,0
-2021-12,5,U,U,PCV13_only,non_resp_severe,0
-2021-12,5,U,U,PCV13_only,non_severe,3
-2021-12,5,U,U,PCV13_only,resp_severe,0
-2021-12,5,U,U,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,U,PCV13_PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,U,PCV13_PPSV23_10yrs,non_severe,0
-2021-12,5,U,U,PCV13_PPSV23_10yrs,resp_severe,0
-2021-12,5,U,U,PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,U,PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,U,PPSV23_10yrs,non_severe,0
-2021-12,5,U,U,PPSV23_10yrs,resp_severe,0
-2021-12,5,U,U,U,ICU_crit_care,3
-2021-12,5,U,U,U,non_resp_severe,0
-2021-12,5,U,U,U,non_severe,6
-2021-12,5,U,U,U,resp_severe,3
-2021-12,5,U,W,PCV13_only,ICU_crit_care,0
-2021-12,5,U,W,PCV13_only,non_resp_severe,0
-2021-12,5,U,W,PCV13_only,non_severe,0
-2021-12,5,U,W,PCV13_only,resp_severe,2
-2021-12,5,U,W,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,W,PCV13_PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,W,PCV13_PPSV23_10yrs,non_severe,0
-2021-12,5,U,W,PCV13_PPSV23_10yrs,resp_severe,0
-2021-12,5,U,W,PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,W,PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,W,PPSV23_10yrs,non_severe,0
-2021-12,5,U,W,PPSV23_10yrs,resp_severe,0
-2021-12,5,U,W,U,ICU_crit_care,0
-2021-12,5,U,W,U,non_resp_severe,0
-2021-12,5,U,W,U,non_severe,3
-2021-12,5,U,W,U,resp_severe,0
diff --git a/Data/core_table_filled.csv b/Data/core_table_filled.csv
deleted file mode 100644
index 1dbe6aa..0000000
--- a/Data/core_table_filled.csv
+++ /dev/null
@@ -1,30241 +0,0 @@
-year_month,age,patient_gender_code,race_code,PCV_combined,COVID_severity,count
-2020-04,0,F,A,PCV13_only,ICU_crit_care,0
-2020-04,0,F,A,PCV13_only,non_resp_severe,0
-2020-04,0,F,A,PCV13_only,non_severe,5
-2020-04,0,F,A,PCV13_only,resp_severe,6
-2020-04,0,F,A,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,F,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,F,A,PCV13_PPSV23_10yrs,non_severe,6
-2020-04,0,F,A,PCV13_PPSV23_10yrs,resp_severe,3
-2020-04,0,F,A,PPSV23_10yrs,ICU_crit_care,3
-2020-04,0,F,A,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,F,A,PPSV23_10yrs,non_severe,9
-2020-04,0,F,A,PPSV23_10yrs,resp_severe,2
-2020-04,0,F,A,U,ICU_crit_care,21
-2020-04,0,F,A,U,non_resp_severe,3
-2020-04,0,F,A,U,non_severe,88
-2020-04,0,F,A,U,resp_severe,69
-2020-04,0,F,B,PCV13_only,ICU_crit_care,17
-2020-04,0,F,B,PCV13_only,non_resp_severe,3
-2020-04,0,F,B,PCV13_only,non_severe,41
-2020-04,0,F,B,PCV13_only,resp_severe,30
-2020-04,0,F,B,PCV13_PPSV23_10yrs,ICU_crit_care,4
-2020-04,0,F,B,PCV13_PPSV23_10yrs,non_resp_severe,1
-2020-04,0,F,B,PCV13_PPSV23_10yrs,non_severe,24
-2020-04,0,F,B,PCV13_PPSV23_10yrs,resp_severe,19
-2020-04,0,F,B,PPSV23_10yrs,ICU_crit_care,11
-2020-04,0,F,B,PPSV23_10yrs,non_resp_severe,5
-2020-04,0,F,B,PPSV23_10yrs,non_severe,28
-2020-04,0,F,B,PPSV23_10yrs,resp_severe,29
-2020-04,0,F,B,U,ICU_crit_care,98
-2020-04,0,F,B,U,non_resp_severe,33
-2020-04,0,F,B,U,non_severe,441
-2020-04,0,F,B,U,resp_severe,450
-2020-04,0,F,H,PCV13_only,ICU_crit_care,10
-2020-04,0,F,H,PCV13_only,non_resp_severe,3
-2020-04,0,F,H,PCV13_only,non_severe,52
-2020-04,0,F,H,PCV13_only,resp_severe,51
-2020-04,0,F,H,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,F,H,PCV13_PPSV23_10yrs,non_resp_severe,2
-2020-04,0,F,H,PCV13_PPSV23_10yrs,non_severe,17
-2020-04,0,F,H,PCV13_PPSV23_10yrs,resp_severe,19
-2020-04,0,F,H,PPSV23_10yrs,ICU_crit_care,21
-2020-04,0,F,H,PPSV23_10yrs,non_resp_severe,2
-2020-04,0,F,H,PPSV23_10yrs,non_severe,44
-2020-04,0,F,H,PPSV23_10yrs,resp_severe,27
-2020-04,0,F,H,U,ICU_crit_care,82
-2020-04,0,F,H,U,non_resp_severe,17
-2020-04,0,F,H,U,non_severe,467
-2020-04,0,F,H,U,resp_severe,391
-2020-04,0,F,U,PCV13_only,ICU_crit_care,25
-2020-04,0,F,U,PCV13_only,non_resp_severe,4
-2020-04,0,F,U,PCV13_only,non_severe,67
-2020-04,0,F,U,PCV13_only,resp_severe,54
-2020-04,0,F,U,PCV13_PPSV23_10yrs,ICU_crit_care,9
-2020-04,0,F,U,PCV13_PPSV23_10yrs,non_resp_severe,4
-2020-04,0,F,U,PCV13_PPSV23_10yrs,non_severe,22
-2020-04,0,F,U,PCV13_PPSV23_10yrs,resp_severe,20
-2020-04,0,F,U,PPSV23_10yrs,ICU_crit_care,25
-2020-04,0,F,U,PPSV23_10yrs,non_resp_severe,1
-2020-04,0,F,U,PPSV23_10yrs,non_severe,71
-2020-04,0,F,U,PPSV23_10yrs,resp_severe,53
-2020-04,0,F,U,U,ICU_crit_care,307
-2020-04,0,F,U,U,non_resp_severe,100
-2020-04,0,F,U,U,non_severe,1173
-2020-04,0,F,U,U,resp_severe,943
-2020-04,0,F,W,PCV13_only,ICU_crit_care,29
-2020-04,0,F,W,PCV13_only,non_resp_severe,9
-2020-04,0,F,W,PCV13_only,non_severe,124
-2020-04,0,F,W,PCV13_only,resp_severe,84
-2020-04,0,F,W,PCV13_PPSV23_10yrs,ICU_crit_care,14
-2020-04,0,F,W,PCV13_PPSV23_10yrs,non_resp_severe,2
-2020-04,0,F,W,PCV13_PPSV23_10yrs,non_severe,46
-2020-04,0,F,W,PCV13_PPSV23_10yrs,resp_severe,42
-2020-04,0,F,W,PPSV23_10yrs,ICU_crit_care,10
-2020-04,0,F,W,PPSV23_10yrs,non_resp_severe,5
-2020-04,0,F,W,PPSV23_10yrs,non_severe,73
-2020-04,0,F,W,PPSV23_10yrs,resp_severe,68
-2020-04,0,F,W,U,ICU_crit_care,217
-2020-04,0,F,W,U,non_resp_severe,88
-2020-04,0,F,W,U,non_severe,1421
-2020-04,0,F,W,U,resp_severe,936
-2020-04,0,M,A,PCV13_only,ICU_crit_care,4
-2020-04,0,M,A,PCV13_only,non_resp_severe,1
-2020-04,0,M,A,PCV13_only,non_severe,6
-2020-04,0,M,A,PCV13_only,resp_severe,8
-2020-04,0,M,A,PCV13_PPSV23_10yrs,ICU_crit_care,2
-2020-04,0,M,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,M,A,PCV13_PPSV23_10yrs,non_severe,2
-2020-04,0,M,A,PCV13_PPSV23_10yrs,resp_severe,10
-2020-04,0,M,A,PPSV23_10yrs,ICU_crit_care,5
-2020-04,0,M,A,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,M,A,PPSV23_10yrs,non_severe,6
-2020-04,0,M,A,PPSV23_10yrs,resp_severe,5
-2020-04,0,M,A,U,ICU_crit_care,52
-2020-04,0,M,A,U,non_resp_severe,10
-2020-04,0,M,A,U,non_severe,84
-2020-04,0,M,A,U,resp_severe,93
-2020-04,0,M,B,PCV13_only,ICU_crit_care,7
-2020-04,0,M,B,PCV13_only,non_resp_severe,4
-2020-04,0,M,B,PCV13_only,non_severe,26
-2020-04,0,M,B,PCV13_only,resp_severe,32
-2020-04,0,M,B,PCV13_PPSV23_10yrs,ICU_crit_care,4
-2020-04,0,M,B,PCV13_PPSV23_10yrs,non_resp_severe,3
-2020-04,0,M,B,PCV13_PPSV23_10yrs,non_severe,14
-2020-04,0,M,B,PCV13_PPSV23_10yrs,resp_severe,10
-2020-04,0,M,B,PPSV23_10yrs,ICU_crit_care,14
-2020-04,0,M,B,PPSV23_10yrs,non_resp_severe,5
-2020-04,0,M,B,PPSV23_10yrs,non_severe,25
-2020-04,0,M,B,PPSV23_10yrs,resp_severe,32
-2020-04,0,M,B,U,ICU_crit_care,125
-2020-04,0,M,B,U,non_resp_severe,52
-2020-04,0,M,B,U,non_severe,320
-2020-04,0,M,B,U,resp_severe,345
-2020-04,0,M,H,PCV13_only,ICU_crit_care,13
-2020-04,0,M,H,PCV13_only,non_resp_severe,2
-2020-04,0,M,H,PCV13_only,non_severe,34
-2020-04,0,M,H,PCV13_only,resp_severe,31
-2020-04,0,M,H,PCV13_PPSV23_10yrs,ICU_crit_care,4
-2020-04,0,M,H,PCV13_PPSV23_10yrs,non_resp_severe,1
-2020-04,0,M,H,PCV13_PPSV23_10yrs,non_severe,10
-2020-04,0,M,H,PCV13_PPSV23_10yrs,resp_severe,13
-2020-04,0,M,H,PPSV23_10yrs,ICU_crit_care,13
-2020-04,0,M,H,PPSV23_10yrs,non_resp_severe,3
-2020-04,0,M,H,PPSV23_10yrs,non_severe,44
-2020-04,0,M,H,PPSV23_10yrs,resp_severe,39
-2020-04,0,M,H,U,ICU_crit_care,186
-2020-04,0,M,H,U,non_resp_severe,29
-2020-04,0,M,H,U,non_severe,435
-2020-04,0,M,H,U,resp_severe,414
-2020-04,0,M,U,PCV13_only,ICU_crit_care,36
-2020-04,0,M,U,PCV13_only,non_resp_severe,9
-2020-04,0,M,U,PCV13_only,non_severe,69
-2020-04,0,M,U,PCV13_only,resp_severe,77
-2020-04,0,M,U,PCV13_PPSV23_10yrs,ICU_crit_care,7
-2020-04,0,M,U,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,M,U,PCV13_PPSV23_10yrs,non_severe,24
-2020-04,0,M,U,PCV13_PPSV23_10yrs,resp_severe,26
-2020-04,0,M,U,PPSV23_10yrs,ICU_crit_care,46
-2020-04,0,M,U,PPSV23_10yrs,non_resp_severe,10
-2020-04,0,M,U,PPSV23_10yrs,non_severe,80
-2020-04,0,M,U,PPSV23_10yrs,resp_severe,75
-2020-04,0,M,U,U,ICU_crit_care,596
-2020-04,0,M,U,U,non_resp_severe,174
-2020-04,0,M,U,U,non_severe,1331
-2020-04,0,M,U,U,resp_severe,1362
-2020-04,0,M,W,PCV13_only,ICU_crit_care,34
-2020-04,0,M,W,PCV13_only,non_resp_severe,7
-2020-04,0,M,W,PCV13_only,non_severe,113
-2020-04,0,M,W,PCV13_only,resp_severe,77
-2020-04,0,M,W,PCV13_PPSV23_10yrs,ICU_crit_care,11
-2020-04,0,M,W,PCV13_PPSV23_10yrs,non_resp_severe,3
-2020-04,0,M,W,PCV13_PPSV23_10yrs,non_severe,38
-2020-04,0,M,W,PCV13_PPSV23_10yrs,resp_severe,36
-2020-04,0,M,W,PPSV23_10yrs,ICU_crit_care,24
-2020-04,0,M,W,PPSV23_10yrs,non_resp_severe,7
-2020-04,0,M,W,PPSV23_10yrs,non_severe,72
-2020-04,0,M,W,PPSV23_10yrs,resp_severe,59
-2020-04,0,M,W,U,ICU_crit_care,394
-2020-04,0,M,W,U,non_resp_severe,120
-2020-04,0,M,W,U,non_severe,1279
-2020-04,0,M,W,U,resp_severe,1080
-2020-04,0,U,A,PCV13_only,ICU_crit_care,0
-2020-04,0,U,A,PCV13_only,non_resp_severe,0
-2020-04,0,U,A,PCV13_only,non_severe,0
-2020-04,0,U,A,PCV13_only,resp_severe,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,non_severe,0
-2020-04,0,U,A,PCV13_PPSV23_10yrs,resp_severe,0
-2020-04,0,U,A,PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,A,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,A,PPSV23_10yrs,non_severe,0
-2020-04,0,U,A,PPSV23_10yrs,resp_severe,0
-2020-04,0,U,A,U,ICU_crit_care,0
-2020-04,0,U,A,U,non_resp_severe,0
-2020-04,0,U,A,U,non_severe,0
-2020-04,0,U,A,U,resp_severe,3
-2020-04,0,U,B,PCV13_only,ICU_crit_care,0
-2020-04,0,U,B,PCV13_only,non_resp_severe,0
-2020-04,0,U,B,PCV13_only,non_severe,0
-2020-04,0,U,B,PCV13_only,resp_severe,0
-2020-04,0,U,B,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,B,PCV13_PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,B,PCV13_PPSV23_10yrs,non_severe,0
-2020-04,0,U,B,PCV13_PPSV23_10yrs,resp_severe,0
-2020-04,0,U,B,PPSV23_10yrs,ICU_crit_care,0
-2020-04,0,U,B,PPSV23_10yrs,non_resp_severe,0
-2020-04,0,U,B,PPSV23_10yrs,non_severe,0
-2020-04,0,U,B,PPSV23_10yrs,resp_severe,0
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-2021-12,5,M,U,PPSV23_10yrs,non_severe,44
-2021-12,5,M,U,PPSV23_10yrs,resp_severe,26
-2021-12,5,M,U,U,ICU_crit_care,133
-2021-12,5,M,U,U,non_resp_severe,250
-2021-12,5,M,U,U,non_severe,1161
-2021-12,5,M,U,U,resp_severe,408
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-2021-12,5,M,W,PCV13_only,non_severe,50
-2021-12,5,M,W,PCV13_only,resp_severe,18
-2021-12,5,M,W,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,M,W,PCV13_PPSV23_10yrs,non_resp_severe,1
-2021-12,5,M,W,PCV13_PPSV23_10yrs,non_severe,7
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-2021-12,5,M,W,PPSV23_10yrs,ICU_crit_care,1
-2021-12,5,M,W,PPSV23_10yrs,non_resp_severe,4
-2021-12,5,M,W,PPSV23_10yrs,non_severe,21
-2021-12,5,M,W,PPSV23_10yrs,resp_severe,7
-2021-12,5,M,W,U,ICU_crit_care,59
-2021-12,5,M,W,U,non_resp_severe,104
-2021-12,5,M,W,U,non_severe,549
-2021-12,5,M,W,U,resp_severe,193
-2021-12,5,U,A,PCV13_only,ICU_crit_care,0
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-2021-12,5,U,A,PCV13_PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,A,PCV13_PPSV23_10yrs,non_severe,0
-2021-12,5,U,A,PCV13_PPSV23_10yrs,resp_severe,0
-2021-12,5,U,A,PPSV23_10yrs,ICU_crit_care,0
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-2021-12,5,U,A,U,non_severe,1
-2021-12,5,U,A,U,resp_severe,0
-2021-12,5,U,B,PCV13_only,ICU_crit_care,0
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-2021-12,5,U,B,PCV13_PPSV23_10yrs,ICU_crit_care,0
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-2021-12,5,U,B,PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,B,PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,B,PPSV23_10yrs,non_severe,0
-2021-12,5,U,B,PPSV23_10yrs,resp_severe,0
-2021-12,5,U,B,U,ICU_crit_care,0
-2021-12,5,U,B,U,non_resp_severe,0
-2021-12,5,U,B,U,non_severe,0
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-2021-12,5,U,H,PCV13_only,ICU_crit_care,0
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-2021-12,5,U,H,PCV13_only,non_severe,0
-2021-12,5,U,H,PCV13_only,resp_severe,0
-2021-12,5,U,H,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,H,PCV13_PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,H,PCV13_PPSV23_10yrs,non_severe,0
-2021-12,5,U,H,PCV13_PPSV23_10yrs,resp_severe,0
-2021-12,5,U,H,PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,H,PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,H,PPSV23_10yrs,non_severe,0
-2021-12,5,U,H,PPSV23_10yrs,resp_severe,3
-2021-12,5,U,H,U,ICU_crit_care,0
-2021-12,5,U,H,U,non_resp_severe,0
-2021-12,5,U,H,U,non_severe,1
-2021-12,5,U,H,U,resp_severe,0
-2021-12,5,U,U,PCV13_only,ICU_crit_care,0
-2021-12,5,U,U,PCV13_only,non_resp_severe,0
-2021-12,5,U,U,PCV13_only,non_severe,3
-2021-12,5,U,U,PCV13_only,resp_severe,0
-2021-12,5,U,U,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,U,PCV13_PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,U,PCV13_PPSV23_10yrs,non_severe,0
-2021-12,5,U,U,PCV13_PPSV23_10yrs,resp_severe,0
-2021-12,5,U,U,PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,U,PPSV23_10yrs,non_resp_severe,0
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-2021-12,5,U,U,PPSV23_10yrs,resp_severe,0
-2021-12,5,U,U,U,ICU_crit_care,3
-2021-12,5,U,U,U,non_resp_severe,0
-2021-12,5,U,U,U,non_severe,6
-2021-12,5,U,U,U,resp_severe,3
-2021-12,5,U,W,PCV13_only,ICU_crit_care,0
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-2021-12,5,U,W,PCV13_only,non_severe,0
-2021-12,5,U,W,PCV13_only,resp_severe,2
-2021-12,5,U,W,PCV13_PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,W,PCV13_PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,W,PCV13_PPSV23_10yrs,non_severe,0
-2021-12,5,U,W,PCV13_PPSV23_10yrs,resp_severe,0
-2021-12,5,U,W,PPSV23_10yrs,ICU_crit_care,0
-2021-12,5,U,W,PPSV23_10yrs,non_resp_severe,0
-2021-12,5,U,W,PPSV23_10yrs,non_severe,0
-2021-12,5,U,W,PPSV23_10yrs,resp_severe,0
-2021-12,5,U,W,U,ICU_crit_care,0
-2021-12,5,U,W,U,non_resp_severe,0
-2021-12,5,U,W,U,non_severe,3
-2021-12,5,U,W,U,resp_severe,0
diff --git a/Data/suppl_6mo_table_filled.csv b/Data/suppl_6mo_table_filled.csv
deleted file mode 100644
index e9aed36..0000000
--- a/Data/suppl_6mo_table_filled.csv
+++ /dev/null
@@ -1,368641 +0,0 @@
-six_mo,age,patient_gender_code,race_code,flu_vacc,zoster_vacc,bmi_30_plus,comorbidities,income_est_mod,PCV_combined,COVID_severity,count
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-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,PCV13_only,non_severe,3
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,PCV13_only,resp_severe,3
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,PCV13_PPSV23_10yrs,ICU_crit_care,0
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-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,PCV13_PPSV23_10yrs,non_severe,0
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,PCV13_PPSV23_10yrs,resp_severe,0
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,PPSV23_10yrs,ICU_crit_care,0
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,PPSV23_10yrs,non_resp_severe,0
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-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,PPSV23_10yrs,resp_severe,0
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,U,ICU_crit_care,6
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,U,non_resp_severe,2
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,U,non_severe,62
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,0,U,resp_severe,27
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,PCV13_only,ICU_crit_care,0
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,PCV13_only,non_resp_severe,0
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-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,PCV13_only,resp_severe,2
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,PCV13_PPSV23_10yrs,ICU_crit_care,0
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-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,PCV13_PPSV23_10yrs,resp_severe,0
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,PPSV23_10yrs,ICU_crit_care,0
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-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,U,ICU_crit_care,12
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,U,non_resp_severe,2
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,U,non_severe,95
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,1,U,resp_severe,24
-1,0,F,A,FALSE,FALSE,FALSE,FALSE,2,PCV13_only,ICU_crit_care,0
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-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,non_resp_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,non_severe,0
-4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,resp_severe,0
diff --git a/Figures/Figure1.png b/Figures/Figure1.png
new file mode 100644
index 0000000..33d8fda
Binary files /dev/null and b/Figures/Figure1.png differ
diff --git a/PCV_analysis.Rmd b/PCV_analysis.Rmd
deleted file mode 100644
index a7d2ab3..0000000
--- a/PCV_analysis.Rmd
+++ /dev/null
@@ -1,205 +0,0 @@
----
-title: "PCV_analysis"
-output: html_document
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-```
-
-## Load required R packages
-```{r}
-library(table1)
-library(dplyr)
-```
-
-```{r}
-### Load the data: 
-core_table <- read.csv("./Data/core_table_filled.csv")
-core_table_norace <- read.csv("./Data/core_table_droprace_filled.csv")
-supp_table_filled <- read.csv("./Data/suppl_table_filled.csv")
-supp_table_6mo_filled <- read.csv("./Data/suppl_6mo_table_filled.csv")
-```
-
-## First step is to create another columnn of no response
-```{r}
-### Prepare the data and do some exploration
-#baseline demographic characteristics (Table 1)
-supp_table_filled$age <- as.factor(core_table$age)
-supp_table_filled$patient_gender_code <- as.factor(core_table$patient_gender_code)
-data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","resp_severe"),]
-data1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",]
-data2 <- supp_table_filled[supp_table_filled$COVID_severity =="resp_severe",]
-data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod"))
-```
-
-```{r}
-## Exploratory analysis of the dataset: 
-print(paste0("Total number of individuals: ",sum(data$count)))
-print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count)))
-print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count)))
-print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count)))
-print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count)))
-print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count)))
-print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count)))
-```
-
-## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe outcomes
-```{r}
-## Factor categorical variable and change reference level for race (set White as the ref level)
-col_names <- c("patient_gender_code","race_code","income_est_mod","PCV_combined")
-data_comb[col_names] <- lapply(data_comb[col_names], factor)
-data_comb$race_code <- relevel(data_comb$race_code,ref="W")
-data_comb$PCV_combined <- relevel(data_comb$PCV_combined,ref="U")
-```
-
-```{r}
-m1<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit"))
-#summary(m1)
-m1.ci<-confint(m1)
-```
-```{r}
-m1.table <- cbind(coef(m1),m1.ci)
-colnames(m1.table) <- c("estimate","lower","upper")
-m1.table <- exp(m1.table)
-View(m1.table)
-```
-
-
-## Second part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19
-```{r}
-### Prepare the data and do some exploration
-#baseline demographic characteristics (Table 1)
-supp_table_filled$age <- as.factor(core_table$age)
-supp_table_filled$patient_gender_code <- as.factor(core_table$patient_gender_code)
-data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","non_resp_severe"),]
-data1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",]
-data2 <- supp_table_filled[supp_table_filled$COVID_severity =="non_resp_severe",]
-data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod"))
-```
-
-```{r}
-## Exploratory analysis of the dataset: 
-print(paste0("Total number of individuals: ",sum(data$count)))
-print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count)))
-print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count)))
-print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count)))
-print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count)))
-print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count)))
-print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count)))
-```
-
-## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe respiratory outcome
-```{r}
-## Factor categorical variable and change reference level for race (set White as the ref level)
-col_names <- c("patient_gender_code","race_code","income_est_mod","PCV_combined")
-data_comb[col_names] <- lapply(data_comb[col_names], factor)
-data_comb$race_code <- relevel(data_comb$race_code,ref="W")
-data_comb$PCV_combined <- relevel(data_comb$PCV_combined,ref="U")
-```
-
-```{r}
-m2<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit"))
-#summary(m2)
-m2.ci<-confint(m2)
-```
-
-```{r}
-m2.table <- cbind(coef(m2),m2.ci)
-colnames(m2.table) <- c("estimate","lower","upper")
-m2.table <- exp(m2.table)
-View(m2.table)
-```
-
-## Second part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19
-```{r}
-### Prepare the data and do some exploration
-#baseline demographic characteristics (Table 1)
-supp_table_filled$age <- as.factor(core_table$age)
-supp_table_filled$patient_gender_code <- as.factor(core_table$patient_gender_code)
-data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","ICU_crit_care"),]
-data1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",]
-data2 <- supp_table_filled[supp_table_filled$COVID_severity =="ICU_crit_care",]
-data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod"))
-```
-
-```{r}
-## Exploratory analysis of the dataset: 
-print(paste0("Total number of individuals: ",sum(data$count)))
-print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count)))
-print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count)))
-print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count)))
-print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count)))
-print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count)))
-print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count)))
-```
-
-## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe respiratory outcome
-```{r}
-## Factor categorical variable and change reference level for race (set White as the ref level)
-col_names <- c("patient_gender_code","race_code","income_est_mod","PCV_combined")
-data_comb[col_names] <- lapply(data_comb[col_names], factor)
-data_comb$race_code <- relevel(data_comb$race_code,ref="W")
-data_comb$PCV_combined <- relevel(data_comb$PCV_combined,ref="U")
-```
-
-```{r}
-m3<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit"))
-#summary(m3)
-m3.ci<-confint(m3)
-```
-
-```{r}
-m3.table <- cbind(coef(m3),m3.ci)
-colnames(m3.table) <- c("estimate","lower","upper")
-m3.table <- exp(m3.table)
-View(m3.table)
-```
-
-## Fourth part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19
-```{r}
-### Prepare the data and do some exploration
-#baseline demographic characteristics (Table 1)
-supp_table_filled$age <- as.factor(core_table$age)
-supp_table_filled$patient_gender_code <- as.factor(core_table$patient_gender_code)
-data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("ICU_crit_care","resp_severe"),]
-data1 <- supp_table_filled[supp_table_filled$COVID_severity == "ICU_crit_care",]
-data2 <- supp_table_filled[supp_table_filled$COVID_severity =="resp_severe",]
-data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod"))
-```
-
-```{r}
-## Exploratory analysis of the dataset: 
-print(paste0("Total number of individuals: ",sum(data$count)))
-print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count)))
-print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count)))
-print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count)))
-print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count)))
-print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count)))
-print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count)))
-```
-
-## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe respiratory outcome
-```{r}
-## Factor categorical variable and change reference level for race (set White as the ref level)
-col_names <- c("patient_gender_code","race_code","income_est_mod","PCV_combined")
-data_comb[col_names] <- lapply(data_comb[col_names], factor)
-data_comb$race_code <- relevel(data_comb$race_code,ref="W")
-data_comb$PCV_combined <- relevel(data_comb$PCV_combined,ref="U")
-```
-
-```{r}
-m4<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit"))
-#summary(m4)
-m4.ci<-confint(m4)
-```
-
-```{r}
-m4.table <- cbind(coef(m4),m4.ci)
-colnames(m4.table) <- c("estimate","lower","upper")
-m4.table <- exp(m4.table)
-View(m4.table)
-```
-
-
-
diff --git a/PCV_analysis_with_pharma_dataset1.Rmd b/PCV_analysis_with_pharma_dataset1.Rmd
deleted file mode 100644
index 949e902..0000000
--- a/PCV_analysis_with_pharma_dataset1.Rmd
+++ /dev/null
@@ -1,86 +0,0 @@
----
-title: "PCV_analysis -- Dataset 1 and 2"
-output: html_document
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-```
-
-## Load required R packages
-```{r}
-library(table1)
-library(dplyr)
-library(ggplot2)
-#library(GGally)
-library(car)
-source('./R/functions.R')
-```
-
-```{r}
-### Load the data: 
-core_table <- read.csv("./Data/core_table_filled.csv")
-core_table_norace <- read.csv("./Data/core_table_droprace_filled.csv")
-```
-
-## Check that individuals are the same across dataframes
-```{r}
-print(paste0("Total number of individuals: "))
-sum(core_table$count)
-print("\n")
-sum(core_table_norace$count)
-```
-## Define covariates
-```{r}
-list_cov <- c("age","patient_gender_code","race_code","PCV_combined")
-```
-
-
-## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe outcomes
-```{r}
-list_out <- c("resp_severe","non_severe")
-m1<-run_model(core_table,list_cov,list_out)
-View(m1$m.table)
-```
-## Check collinearity among the covariates using variance inflation factor (VIF): VIF answers the question: "How well is one of my covariates x_i explained by all other covariates x jointly?".
-The VIF is defined as 1/(1-R_squared); this means that for VIF>=10, this specific covariate is explained by all other covariates to a large extent (~90%). There could be a problem of collinearity.
-```{r}
-m1$vif
-```
-## Second part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19
-```{r}
-list_out <- c("non_resp_severe","non_severe")
-m2<-run_model(core_table,list_cov,list_out)
-View(m2$m.table)
-```
-##Check collinearity
-```{r}
-m2$vif
-```
-
-## Third part of the analysis: restrict to people with ICU critical care vs non-severe outcomes for Covid-19
-```{r}
-list_out <- c("ICU_crit_care","non_severe")
-m3<-run_model(core_table,list_cov,list_out)
-View(m3$m.table)
-```
-
-##Check collinearity
-```{r}
-m3$vif
-```
-
-## Fourth part of the analysis: restrict to people with ICU critical care vs respiratory severe outcomes for Covid-19
-
-```{r}
-list_out <- c("ICU_crit_care","resp_severe")
-m4<-run_model(core_table,list_cov,list_out)
-View(m4$m.table)
-```
-
-##Check collinearity
-```{r}
-m4$vif
-```
-
-
diff --git a/PCV_analysis_with_pharma_dataset2.Rmd b/PCV_analysis_with_pharma_dataset2.Rmd
deleted file mode 100644
index c662e0f..0000000
--- a/PCV_analysis_with_pharma_dataset2.Rmd
+++ /dev/null
@@ -1,41 +0,0 @@
----
-title: "PCV_analysis -- Dataset 3"
-output: html_document
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-```
-
-## Load required R packages
-```{r}
-library(table1)
-library(dplyr)
-library(ggplot2)
-#library(GGally)
-library(car)
-```
-
-```{r}
-### Load the data: 
-supp_table <- read.csv("~/Documents/Ottavia/Data_pharma/suppl_table_filled.csv")
-```
-
-## Check that individuals are the same across dataframes
-```{r}
-print(paste0("Total number of individuals: "))
-sum(supp_table$count)
-```
-## Define covariates
-```{r}
-list_cov <- c("year_month","age","patient_gender_code","race_code","PCV_combined") #removed "income_est_mod"
-## drop covariates
-drop.cols <- c("bmi_30_plus","income_est_mod")
-df_table <- supp_table %>% select(-all_of(drop.cols))
-```
-## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe outcomes
-```{r}
-list_out <- c("non_severe","resp_severe")
-m1<-run_model(df_table,list_cov,list_out)
-View(m1$m.table)
-```
\ No newline at end of file
diff --git a/PCV_analysis_with_pharma_dataset3.Rmd b/PCV_analysis_with_pharma_dataset3.Rmd
deleted file mode 100644
index 0e9a882..0000000
--- a/PCV_analysis_with_pharma_dataset3.Rmd
+++ /dev/null
@@ -1,216 +0,0 @@
----
-title: "PCV_analysis -- Dataset 1 and 2"
-output: html_document
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-```
-
-## Load required R packages
-```{r}
-library(table1)
-library(dplyr)
-library(ggplot2)
-#library(GGally)
-library(car)
-```
-
-```{r}
-### Load the data: 
-core_table <- read.csv("./Data/core_table_filled.csv")
-core_table_norace <- read.csv("./Data/core_table_droprace_filled.csv")
-```
-
-## Check that individuals are the same across dataframes
-```{r}
-print(paste0("Total number of individuals: "))
-sum(core_table$count)
-print("\n")
-sum(core_table_norace$count)
-```
-## Factor variables in the data
-```{r}
-## Factor categorical variable and change reference level for race (set White as the ref level)
-col_names <- c("age","patient_gender_code","race_code","income_est_mod","PCV_combined")
-supp_table_filled[col_names] <- lapply(supp_table_filled[col_names], factor)
-supp_table_filled$race_code <- relevel(supp_table_filled$race_code,ref="W")
-supp_table_filled$PCV_combined <- relevel(supp_table_filled$PCV_combined,ref="U")
-```
-
-## First step is to create another columnn of no response
-```{r}
-### Prepare the data and do some exploration
-#baseline demographic characteristics (Table 1)
-data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","resp_severe"),]
-data1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",]
-data2 <- supp_table_filled[supp_table_filled$COVID_severity =="resp_severe",]
-data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod"))
-```
-```{r}
-d <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","resp_severe"),]
-d1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",]
-d2 <- supp_table_filled[supp_table_filled$COVID_severity =="resp_severe",]
-d_comb<-merge(d1,d2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod"))
-
-```
-
-
-
-
-```{r}
-## Exploratory analysis of the dataset: 
-print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count)))
-print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count)))
-print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count)))
-print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count)))
-print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count)))
-print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count)))
-```
-
-## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe outcomes
-```{r}
-m1<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit"))
-#summary(m1)
-m1.ci<-confint(m1)
-```
-
-```{r}
-m1.table <- cbind(coef(m1),m1.ci)
-colnames(m1.table) <- c("estimate","lower","upper")
-m1.table <- exp(m1.table)
-View(m1.table)
-```
-
-##Check collinearity among the covariates using variance inflation factor (VIF): VIF answers the question: "How well is one of my covariates x_i explained by all other covariates x jointly?".
-The VIF is defined as 1/(1-R_squared); this means that for VIF>=10, this specific covariate is explained by all other covariates to a large extent (~90%). There could be a problem of collinearity.
-
-```{r}
-#X<-data_comb[,2:10]
-#ggpairs(X)
-vif(m1)
-```
-
-
-## Second part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19
-```{r}
-### Prepare the data and do some exploration
-#baseline demographic characteristics (Table 1)
-data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","non_resp_severe"),]
-data1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",]
-data2 <- supp_table_filled[supp_table_filled$COVID_severity =="non_resp_severe",]
-data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod"))
-```
-
-```{r}
-## Exploratory analysis of the dataset: 
-print(paste0("Total number of individuals: ",sum(data$count)))
-print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count)))
-print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count)))
-print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count)))
-print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count)))
-print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count)))
-print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count)))
-```
-
-## Logistic regression
-```{r}
-m2<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit"))
-#summary(m2)
-m2.ci<-confint(m2)
-```
-
-##Check collinearity
-```{r}
-vif(m2)
-```
-
-
-```{r}
-m2.table <- cbind(coef(m2),m2.ci)
-colnames(m2.table) <- c("estimate","lower","upper")
-m2.table <- exp(m2.table)
-View(m2.table)
-```
-
-## Third part of the analysis: restrict to people with ICU critical care vs non-severe outcomes for Covid-19
-```{r}
-### Prepare the data and do some exploration
-#baseline demographic characteristics (Table 1)
-data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","ICU_crit_care"),]
-data1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",]
-data2 <- supp_table_filled[supp_table_filled$COVID_severity =="ICU_crit_care",]
-data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod"))
-```
-
-```{r}
-## Exploratory analysis of the dataset: 
-print(paste0("Total number of individuals: ",sum(data$count)))
-print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count)))
-print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count)))
-print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count)))
-print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count)))
-print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count)))
-print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count)))
-```
-
-## Logistic regression
-```{r}
-m3<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit"))
-#summary(m3)
-m3.ci<-confint(m3)
-```
-
-```{r}
-m3.table <- cbind(coef(m3),m3.ci)
-colnames(m3.table) <- c("estimate","lower","upper")
-m3.table <- exp(m3.table)
-View(m3.table)
-```
-
-##Check collinearity
-```{r}
-vif(m3)
-```
-
-## Fourth part of the analysis: restrict to people with ICU critical care vs respiratory severe outcomes for Covid-19
-```{r}
-### Prepare the data and do some exploration
-#baseline demographic characteristics (Table 1)
-data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("ICU_crit_care","resp_severe"),]
-data1 <- supp_table_filled[supp_table_filled$COVID_severity == "ICU_crit_care",]
-data2 <- supp_table_filled[supp_table_filled$COVID_severity =="resp_severe",]
-data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod"))
-```
-
-```{r}
-## Exploratory analysis of the dataset: 
-print(paste0("Total number of individuals: ",sum(data$count)))
-print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count)))
-print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count)))
-print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count)))
-print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count)))
-print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count)))
-print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count)))
-```
-
-## Logistic regression
-```{r}
-m4<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit"))
-#summary(m4)
-m4.ci<-confint(m4)
-```
-
-```{r}
-m4.table <- cbind(coef(m4),m4.ci)
-colnames(m4.table) <- c("estimate","lower","upper")
-m4.table <- exp(m4.table)
-View(m4.table)
-```
-
-##Check collinearity
-```{r}
-vif(m4)
-```
-
-
diff --git a/PCV_analysis_with_pharma_exploratory.Rmd b/PCV_analysis_with_pharma_exploratory.Rmd
deleted file mode 100644
index cc50b2b..0000000
--- a/PCV_analysis_with_pharma_exploratory.Rmd
+++ /dev/null
@@ -1,50 +0,0 @@
----
-title: "PCV_analysis"
-output: html_document
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-```
-
-## Load required R packages
-```{r}
-library(table1)
-library(dplyr)
-library(ggplot2)
-```
-
-```{r}
-### Load the data: 
-core_table <- read.csv("./Data/core_table_filled.csv")
-core_table_norace <- read.csv("./Data/core_table_droprace_filled.csv")
-supp_table_filled <-read.csv("~/Documents/Ottavia/Data_pharma/suppl_table_filled.csv")
-supp_table_6mo_filled <- read.csv("./Data/suppl_6mo_table_filled.csv")
-```
-
-## Check that individuals are the same across dataframes
-```{r}
-print(paste0("Total number of individuals: "))
-sum(core_table$count)
-print("\n")
-sum(core_table_norace$count)
-print("\n")
-sum(supp_table_filled$count)
-print("\n")
-sum(supp_table_6mo_filled$count)
-```
-
-## Exploratory analysis: descriptive stats
-```{r}
-Tot_ind <- sum(supp_table_filled$count)
-vax_by_age<-supp_table_filled %>% group_by(age,PCV_combined) %>% summarise(prop = sum(count)/Tot_ind)
-vax_by_com<-supp_table_filled %>% group_by(comorbidities,PCV_combined) %>% summarise(prop = sum(count)/Tot_ind)
-```
-
-## Plot histograms
-```{r}
-ggplot(vax_by_age,aes(age,prop,fill=PCV_combined))+
-  geom_bar(stat="identity",position='dodge')
-ggplot(vax_by_com,aes(comorbidities,prop,fill=PCV_combined))+
-  geom_bar(stat="identity",position='dodge')
-```
diff --git a/R/Example_logistic_reg_agg_data.R b/R/Example_logistic_reg_agg_data.R
deleted file mode 100644
index d6e99e2..0000000
--- a/R/Example_logistic_reg_agg_data.R
+++ /dev/null
@@ -1,18 +0,0 @@
-library(tidyverse)
-set.seed(5)
-df<-tibble(Gender = as.factor(sample(c("m","f"), 200, replace = TRUE, prob=c(0.6,0.4))),
-           Age_Group = as.factor(sample(c("[<30]","[30-65]", "[65+]"), 200, replace = TRUE, prob=c(0.3,0.6,0.1))),
-           Response = rbinom(200, 1, prob = 0.2))
-df
-model1<-glm(Response ~ Gender+Age_Group, data = df, family = binomial("logit"))
-summary(model1)
-
-df_agg<-df%>%group_by(Gender, Age_Group)%>%summarise(Impressions=n(), Responses=sum(Response))%>%
-  ungroup()%>%mutate(RR=Responses/Impressions)
-df_agg
-
-df_agg$No_Responses <- df_agg$Impressions- df_agg$Responses
-df_agg
-
-m3<-glm(cbind(Responses, No_Responses) ~ Gender+Age_Group, data=df_agg, family = binomial("logit"))
-summary(m3)
\ No newline at end of file
diff --git a/R/Plotting_paper.R b/R/Plotting_paper.R
new file mode 100644
index 0000000..f92a0fa
--- /dev/null
+++ b/R/Plotting_paper.R
@@ -0,0 +1,110 @@
+######Plotting
+library(dplyr)
+library(ggplot2)
+library(duckdb)
+library(arrow)
+library(lubridate)
+library(reshape2)
+library(vtable)
+library(tidyr)
+#library(plyr)
+library(data.table)
+library(forestplot)
+library(metafor)
+library(patchwork)
+library(ggplotify)
+
+#### Load data
+mod1<-readRDS("./Results/m.table.mod1")
+mod2<-readRDS("./Results/m.table.mod2")
+mod3<-readRDS("./Results/m.table.mod3")
+mod4<-readRDS("./Results/m.table.mod4")
+colnames(mod1)<-c("mean","lower","upper")
+colnames(mod2)<-c("mean","lower","upper")
+colnames(mod3)<-c("mean","lower","upper")
+colnames(mod4)<-c("mean","lower","upper")
+
+
+mod1<-as.data.frame(mod1[c(1,2,3,6,7),])
+mod1$model<-c("PPSV23 >5yrs","PPSV23 <=5yrs","PCV13 only","PCV13 and PPSV23 <=5yrs","PCV13 and PPSV23 >5yrs")
+mod1$OR<-c("1.01 (0.95,1.06)","1.05 (1.01,1.08)","0.91 (0.88,0.93)","0.95 (0.91,0.99)","0.91 (0.85,0.97)")
+mod2<-as.data.frame(mod2[c(1,2,3,6,7),])
+mod2$model<-c("PPSV23 >5yrs","PPSV23 <=5yrs","PCV13 only","PCV13 and PPSV23 <=5yrs","PCV13 and PPSV23 >5yrs")
+mod2$OR<-c("0.99 (0.90,1.10)","1.12 (1.05,1.20)","1.08 (1.02,1.14)","1.21 (1.13,1.29)","1.07 (0.96,1.19)")#round(mod2$mean,2)
+mod3<-as.data.frame(mod3[c(1,2,3,6,7),])
+mod3$model<-c("PPSV23 >5yrs","PPSV23 <=5yrs","PCV13 only","PCV13 and PPSV23 <=5yrs","PCV13 and PPSV23 >5yrs")
+mod3$OR<-c("0.96 (0.88,1.06)","1.03 (0.97,1.10)","0.92 (0.88,0.97)","0.96 (0.88,1.03)","0.89 (0.79,0.99)")#round(mod3$mean,2)
+mod4<-as.data.frame(mod4[c(1,2,3,6,7),])
+mod4$model<-c("PPSV23 >5yrs","PPSV23 <=5yrs","PCV13 only","PCV13 and PPSV23 <=5yrs","PCV13 and PPSV23 >5yrs")
+mod4$OR<-c("0.97 (0.89,1.06)","1.07 (1.01,1.14)","0.85 (0.81,0.90)","0.92 (0.85,0.98)","0.83 (0.73,0.92)")#round(mod4$mean,2)
+
+
+#par(mfrow = c(2, 2)) # 1-by-2 grid of plots
+g1<-mod1 %>% forestplot(labeltext=c(model,OR),
+                    zero=1,
+                    title="A) Severe respiratory or ICU vs non-severe outcomes",
+                    boxsize = 0.1,
+                    xlab="OR",
+                    xticks=c(0.8,1.0,1.2,1.6)) %>% 
+         fp_set_style(box = "royalblue",
+               line = "darkblue",
+               summary = "royalblue") %>%
+         fp_add_header(OR="OR (95%CI)")
+g2<-mod2 %>% forestplot(labeltext=c(model,OR),
+                    zero=1,
+                    title="B) Severe respiratory or ICU vs severe non-resp outcomes",
+                    boxsize = 0.1,
+                    xlab="OR",
+                    xticks=c(0.8,1.0,1.2,1.6)) %>% 
+  fp_set_style(box = "royalblue",
+               line = "darkblue",
+               summary = "royalblue") %>%
+  fp_add_header(OR="OR (95%CI)")
+g3<-mod3 %>% forestplot(labeltext=c(model,OR),
+                    zero=1,
+                    title="C) ICU vs severe respiratory outcomes",
+                    boxsize = 0.1,
+                    xlab="OR",
+                    xticks=c(0.8,1.0,1.2,1.6)) %>% 
+  fp_set_style(box = "royalblue",
+               line = "darkblue",
+               summary = "royalblue") %>%
+  fp_add_header(OR="OR (95%CI)")
+g4<-mod4 %>% forestplot(labeltext=c(model,OR),
+                    zero=1,
+                    title="D) ICU vs non-severe outcomes",
+                    boxsize = 0.1,
+                    xlab="OR",
+                    xticks=c(0.8,1.0,1.2,1.6)) %>% 
+  fp_set_style(box = "royalblue",
+               line = "darkblue",
+               summary = "royalblue") %>%
+  fp_add_header(OR="OR (95%CI)")
+
+
+
+
+g1<-grid2grob(print(g1))
+g2<-grid2grob(print(g2))
+g3<-grid2grob(print(g3))
+g4<-grid2grob(print(g4))
+
+wrap_elements(g1)*wrap_elements(g2)/(wrap_elements(g3)*wrap_elements(g4))
+
+
+
+ggsave(gsave,"./Figures/Figure1.png")
+
+forest(x=mod1$mean, 
+       ci.lb = mod1$lower,
+       ci.ub = mod1$upper,
+       cex = 1.5,
+       annotate=FALSE, 
+       at=seq(0.8,1,1.2),
+       xlim=c(0.8,1.2),
+       xlab="OR"
+       #slab=rownames(dat),
+       #psize=psize*.7,
+       #atransf=exp
+)
+
diff --git a/R/Tests.R b/R/Tests.R
deleted file mode 100644
index 23999fa..0000000
--- a/R/Tests.R
+++ /dev/null
@@ -1,20 +0,0 @@
-data[data$year_month=="2021-12" & data$age=="0" & data$patient_gender_code=="F"&data$race_code=="U"&data$PCV_combined=="U"&data$flu_vacc
-     =="FALSE"&data$zoster_vacc=="FALSE"&data$bmi_30_plus=="FALSE"&data$comorbidities=="FALSE"&data$income_est_mod=="U",]
-
-
-data_comb[data_comb$year_month=="2021-12" & data_comb$age=="0" & data_comb$patient_gender_code=="F"&data_comb$race_code=="U"&data_comb$PCV_combined=="U"&data_comb$flu_vacc
-     =="FALSE"&data_comb$zoster_vacc=="FALSE"&data_comb$bmi_30_plus=="FALSE"&data_comb$comorbidities=="FALSE"&data_comb$income_est_mod=="U",]
-
-
-
-
-supp_table_filled[supp_table_filled$year_month=="2021-12" & supp_table_filled$age=="1" & supp_table_filled$patient_gender_code=="F"& supp_table_filled$race_code=="U"&supp_table_filled$PCV_combined=="U"&
-                    supp_table_filled$flu_vacc
-     =="FALSE"&supp_table_filled$zoster_vacc=="FALSE"&supp_table_filled$bmi_30_plus=="FALSE"&supp_table_filled$comorbidities=="FALSE"&supp_table_filled$income_est_mod=="U"
-     &supp_table_filled$COVID_severity!="non_resp_severe"& supp_table_filled$COVID_severity!="ICU_crit_care",]
-
-
-core_table[core_table$year_month=="2021-12" & core_table$age=="1" & core_table$patient_gender_code=="F"& core_table$race_code=="U"&core_table$PCV_combined=="U"&
-             core_table$flu_vacc
-                  =="FALSE"&core_table$zoster_vacc=="FALSE"&core_table$bmi_30_plus=="FALSE"&core_table$comorbidities=="FALSE"&core_table$income_est_mod=="U"
-                  &core_table$COVID_severity!="non_resp_severe"& core_table$COVID_severity!="ICU_crit_care",]
diff --git a/R/functions.R b/R/functions.R
deleted file mode 100644
index f091982..0000000
--- a/R/functions.R
+++ /dev/null
@@ -1,28 +0,0 @@
-run_model <- function(df,list_cov,list_out)
-  {## Convert columns of list_cov to factors
-  col_names <- list_cov[2:length(list_cov)]
-  df[col_names] <- lapply(df[col_names], factor)
-  df$race_code <- relevel(df$race_code,ref="W")
-  df$PCV_combined <- relevel(df$PCV_combined,ref="U")
-  ## Create df of response and non-responses to be fed to the logistic regression
-  df1 <- df[df$COVID_severity %in% list_out,]
-  data1 <- df1[df1$COVID_severity == list_out[1],]
-  data2 <- df1[df1$COVID_severity ==list_out[2],]
-  #list_cov_tot <- paste(c("year_month",list_cov))
-  data_comb<-merge(data1,data2,all.x=TRUE,all.y=TRUE)
-  ## 
-  if(sum(df1$count)==sum(data_comb$count.x)+sum(data_comb$count.y)){print("OK")}
-  else print("ERROR")
-  ## Run logistic regression model
-  data_comb$COVID_severity.x<-NULL
-  data_comb$COVID_severity.y<-NULL
-  m<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+PCV_combined+. -year_month, data=data_comb, family = binomial("logit"))
-  #summary(m1)
-  m.ci<-confint(m)
-  m.table <- cbind(coef(m),m.ci)
-  colnames(m.table) <- c("estimate","lower","upper")
-  m.table <- exp(m.table)
-  m.vif <- vif(m)
-  result <- list("m.table"=m.table,"vif"=m.vif)
-  return(result)
-}
\ No newline at end of file
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