-
Notifications
You must be signed in to change notification settings - Fork 0
/
Outputs_20200424.Rmd
1643 lines (1250 loc) · 87.9 KB
/
Outputs_20200424.Rmd
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
---
output: pdf_document
---
#### Planning for COVID-19 in Baltimore City
Prepared by [SORT](https://www.jhsph.edu/departments/epidemiology/tracks/infectious-disease-epidemiology/sort/) from Johns Hopkins
Updated `r Sys.Date()`
```{r setup, include=FALSE, eval=TRUE}
options(tinytex.verbose = TRUE, scipen = 999)
knitr::opts_chunk$set(echo = FALSE, cache = FALSE, warning=FALSE, message=FALSE, fig.align="center")
```
**This report is based on mathematical models of transmission and is not a forecast of the epidemic trajectory. Rather, these estimates should be used to help guide decision making regarding prevention, mitigation and preparedness strategies. Results from these models are based on assumptions about transmission and are specific to Baltimore City. Since they are not forecasts, they are not intended to exactly match the number of incident cases and deaths being reported which may also be biased by reporting, testing, and health seeking behavior. Instead, they are used to recreate SARS-CoV-2 transmission and investigate the impact of various intervention strategies. These are the same scenarios and interventions run for the Maryland State report. This is a living document and can be updated to reflect changes in our understanding of the SARS-CoV-2 virus and public health interventions in the region.**
**Purpose**
<br>
The purpose of this report is to compare the health impact and surge capacity needs for the 2020 COVID-19 epidemic in Baltimore City. We use a stochastic age-structured Susceptible Exposed Infected Recovered (SEIR) model to predict the number of incident cases within age groups, and among healthcare workers and the homeless population. We then use our best understanding of the natural history of SARS-CoV-2 to estimate the number of hospitalizations, ICU admissions, ventilators required, and deaths in Baltimore City.
We compare four intervention strategies under a moderate transmission scenario, where $R_0$ is assumed to be between 2-3 (each person infected with the disease will transmit the disease to 2-3 people on average), for model simulations from March 1, 2020 through August 1, 2020. The intervention strategies considered were:
(A) Moderate Social Distancing with Constant Effectiveness: This scenario has mildly restrictive social distancing from March 13-29, similar to that observed during the 1918 influenza pandemic (44-65% reduction in transmission), followed by a statewide stay-at-home policy from March 30 - July 1 where individuals remain socially distanced with constant effectiveness at a level similar to the UK’s current lockdown for three months (66-86% reduction in transmission). Mildly restrictive social distancing includes school closings, bans on public gatherings and closed businesses. The current lockdown in the UK is equivalent to Maryland’s stay-at-home orders.
(B) Moderate Social Distancing with Degrading Effectiveness: This scenario has mildly restrictive social distancing from March 13-29, similar to that observed during the 1918 influenza pandemic, followed by a statewide stay-at-home policy from March 30 - July 1, similar to that in effect in the UK. During this stay-at-home period, the effectiveness of social distancing decays by 10% every 2 weeks.
(C) Moderate Social Distancing Maintained when Schools Re-open April 27: This scenario has mildly restrictive social distancing from March 13-29, similar to that observed during the 1918 influenza pandemic, followed by a statewide stay-at-home policy from March 30 - July 1, similar to that in effect in the UK. The effectiveness of social distancing decays by 10% every 2 weeks and is further reduced 18% when Maryland schools are open from April 27 - June 15.
(D) All restrictions lifted April 27: This scenario has mildly restrictive social distancing from March 13-29, similar to that observed during the 1918 influenza pandemic, followed by a statewide stay-at-home policy from March 30 - April 27, similar to that in effect in London, UK. After April 28, Maryland schools reopen and all interventions are lifted. During the stay-at-home period, the effectiveness of social distancing decays by 10% every 2 weeks.
**Assumptions**
<br>
Given the limited amount of information on some of the key epidemiologic features of COVID-19, we have used commonly accepted estimates in the literature thus far and believe these are appropriate for planning purposes. These assumptions are subject to change as new information becomes available. Specific details regarding the methods and assumptions used in this report are provided in the technical appendix.
**Brief Guide**
<br>
Below are graphic representations of the four intervention scenarios. We present the graphics below in three sections to answer three primary questions to help with policy planning: 1) when and how big are the peaks of symptomatic cases, hospitalizations, and death; 2) considering new daily infections, discharges, and deaths, what is the daily capacity required for hospital beds, ICU beds, and ventilators; and 3) what is the total impact of COVID-19 in Baltimore City. In each section we present the results for each intervention scenario, A through D. In the first section, we also present the estimates of symptomatic cases, hospitalizations, and deaths for an uncontrolled scenario, where no interventions are used, for comparative purposes. This highlights the importance of intervention planning. These four different decision strategies result in four different estimates of outcomes.
```{r data, include=FALSE, eval=TRUE, echo=FALSE}
require(socialmixr)
require(xlsx)
require(magrittr)
require(stringr)
require(reshape2)
require(dplyr)
require(ggplot2)
require(tidyr)
require(xtable)
require(cowplot)
require(wesanderson)
##load data
setwd("~/Desktop/Hopkins/Hopkins/COVID_baltimore/Baltimore_City_Modeling")
complete_scenarioA <- read.csv("scenarioA_moderate.csv")
complete_scenarioB <- read.csv("scenarioB_mod_degrading.csv")
complete_scenarioC <- read.csv("scenarioC_mod_degrading_scl.csv")
complete_scenarioD <- read.csv("scenarioD_total_reopen.csv")
#setwd("~/Dropbox/Postdoc/COVID19/COVID-BaltimoreCity/output_20200408")
complete_scenarioE <- read.csv("scenario1A_lowR0_uncontrolled.csv")
##load_and_clean function to concatenate age categories
load_and_clean<- function(data, r0val){
complete_incident_r0val<- data%>%
select(time,incid_I1,incid_I2,incid_I3,incid_I4,incid_I5,
incid_I6,incid_I7,incid_I8,incid_I9,
incid_I10,incid_I11,incid_I12,incid_I13,incid_I14)
combined_incident_r0val<- complete_incident_r0val%>%
mutate(cat1=rowSums(.[2:5]))%>%
mutate(cat2=rowSums(.[6:8]))%>%
mutate(cat3=rowSums(.[9]))%>%
mutate(cat4=rowSums(.[10:12]))%>%
mutate(cat5=rowSums(.[13]))%>%
mutate(cat6=rowSums(.[14]))%>% #HCW
mutate(cat7=rowSums(.[15])) #Homeless
combined_incident_r0val<- combined_incident_r0val%>%
select(time, cat1, cat2, cat3, cat4, cat5, cat6, cat7)
complete_asymp_r0val<- data %>%
dplyr::mutate(tot_1=incid_A1+incid_I1,tot_2=incid_A2+incid_I2,tot_3=incid_A3+incid_I3,tot_4=incid_A4+incid_I4,
tot_5=incid_A5+incid_I5,tot_6=incid_A6+incid_I6,tot_7=incid_A7+incid_I7,tot_8=incid_A8+incid_I8,
tot_9=incid_A9+incid_I9,tot_10=incid_A10+incid_I10,tot_11=incid_A11+incid_I11,tot_12=incid_A12+incid_I12,
tot_13=incid_A13+incid_I13,tot_14=incid_A14+incid_I14) %>%
select(time,tot_1,tot_2,tot_3,tot_4,tot_5,
tot_6,tot_7,tot_8,tot_9,
tot_10,tot_11,tot_12,tot_13,tot_14)
combined_asymp_r0val<- complete_asymp_r0val%>%
mutate(cat1=rowSums(.[2:5]))%>%
mutate(cat2=rowSums(.[6:8]))%>%
mutate(cat3=rowSums(.[9]))%>%
mutate(cat4=rowSums(.[10:12]))%>%
mutate(cat5=rowSums(.[13]))%>%
mutate(cat6=rowSums(.[14]))%>% #HCW
mutate(cat7=rowSums(.[15])) #Homeless
combined_asymp_r0val<- combined_asymp_r0val%>%
select(time, cat1, cat2, cat3, cat4, cat5, cat6, cat7)
return(list(combined_incident_r0val,combined_asymp_r0val))
}
##run the function on each transmission scenario
#to get incident symptomatic cases
combined_incident_A<- load_and_clean(complete_scenarioA,A)[[1]]
combined_incident_B<- load_and_clean(complete_scenarioB,B)[[1]]
combined_incident_C<- load_and_clean(complete_scenarioC,C)[[1]]
combined_incident_D<- load_and_clean(complete_scenarioD,D)[[1]]
combined_incident_E<- load_and_clean(complete_scenarioE,E)[[1]]
#to get incident asymptomatic + symptomatic cases
combined_incident_asymp_A<- load_and_clean(complete_scenarioA,A)[[2]]
combined_incident_asymp_B<- load_and_clean(complete_scenarioB,B)[[2]]
combined_incident_asymp_C<- load_and_clean(complete_scenarioC,C)[[2]]
combined_incident_asymp_D<- load_and_clean(complete_scenarioD,D)[[2]]
combined_incident_asymp_E<- load_and_clean(complete_scenarioE,E)[[2]]
```
```{r, message=FALSE, warning=FALSE, echo=FALSE}
#Here we create a probability matrix of working probabilities from the literature for the probability of becoming a hospitalized case, getting admitted to ICU, needing a ventilator and death. These are nested conditional probabilities that rely on the following assumptions:
#The percentage of hospitalized cases in each category is obtained from the [CDC](https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm#T1_down) where we combined the 55-74 age groups and the 75-80 and 80+ age groups.
#We assume the remainder of the nested probabilities from the [CDC](https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm#T1_down) and in each subsequent state (hospitalization to ICU, ICU to death) we shift the denominator to be the preceding state's total (e.g. the probability of getting admitted to the ICU is the number of ICU cases for the age group/number of hospitalized cases for the age group).
#We assume a global probability that 25% of cases admitted to the ICU require a ventilator per [WHO reports](https://www.who.int/publications-detail/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19)).
#We assume healthcare workers and the homeless population follow a similar nested probability scheme as those in the 45-54 year age group.
#Note: Instead of having just a point estimate from the CDC table as described we take this point estmate to be the mean of a beta distribution, where the second shape parameter is 1. The first shape parameter is then defined by (mean)/(1-mean) where the mean is the point estimate of the nested probability obtained from the CDC table.
make_prob_matrix<- function(num_symptomatic,num_severe, num_hospitalized, num_ICU, num_deadFromICU,num_vent, num_deadFromVent){
prob_severe<- num_severe/num_symptomatic
prob_hospitalized<- num_hospitalized/num_severe
prob_ICU<- num_ICU/num_hospitalized
prob_dead_from_icu<-num_deadFromICU/num_ICU
prob_vent_icu<- num_vent/num_ICU
prob_dead_from_vent<- num_deadFromVent/num_vent
prob_matrix<- cbind(prob_severe,prob_hospitalized,prob_ICU,prob_dead_from_icu,
prob_vent_icu,prob_dead_from_vent)
return(prob_matrix)
}
#cdc inputs (4% death)
symptomatic_4<- c(123,705,429,838,354,429,429)
hosp_4<- c(3.08,146.64,121.41,307.05,224.5,121.41,121.41)
ICU_4<-c(0,29.61,44.62,124.94,106.86,44.62,44.62)
dead_ICU_4<- c(0, 1.16,2.93,22.19,32.86,2.93,2.93)
vent_4<- c(0,1,2.5,22.01,51,2.5,2.5)
dead_vent_4<- c(0,0.25,0.5,9,28.5,0.5,0.5)
prob_matrix_4<- make_prob_matrix(symptomatic_4,hosp_4,hosp_4,ICU_4,dead_ICU_4,vent_4,dead_vent_4)
prob_matrix_4[1,c(4,5,6)]<- c(0,0,0)
#2% Death rate
symptomatic_2<- c(123,705,429,838,354,429,429)
hosp_2<- c(3.08,146.64,121.41,307.05,224.5,121.41,121.41)
ICU_2<-c(0,29.61,44.62,124.94,106.86,44.62,44.62)
dead_ICU_2<- c(0, 0.58,1.465,11.095,16.435,1.465,1.465)
vent_2<- c(0,1,2.5,22.01,51,2.5,2.5)
dead_vent_2<- c(0,0.125,0.25,4.5,14.25,0.25,0.25)
prob_matrix_2<- make_prob_matrix(symptomatic_2,hosp_2,hosp_2,ICU_2,dead_ICU_2,vent_2,dead_vent_2)
prob_matrix_2[1,c(4,5,6)]<- c(0,0,0)
#1%death rate
symptomatic_1<- c(123,705,429,838,354,429,429)
hosp_1<- c(3.08,146.64,121.41,307.05,224.5,121.41,121.41)
ICU_1<-c(0,29.61,44.62,124.94,106.86,44.62,44.62)
dead_ICU_1<- c(0, 0.29,0.7325,5.5475,8.2175,0.7325,0.7325)
vent_1<- c(0,1,2.5,22.01,51,2.5,2.5)
dead_vent_1<- c(0,0.062,0.125,2.25,7.125,0.125,0.125)
prob_matrix_1<- make_prob_matrix(symptomatic_1,hosp_1,hosp_1,ICU_1,dead_ICU_1,vent_1,dead_vent_1)
prob_matrix_1[1,c(4,5,6)]<- c(0,0,0)
#define number of days simulations run for
n_days <- 300
start_date <- "2020-03-01"
end_date <- "2020-08-02"
prob_matrix <- prob_matrix_2
```
```{r, message=FALSE, echo=FALSE, warning=FALSE}
##### Estimate number of hospitalizations, ICU admissions, ventilators used, and deaths
incident_classifications<- function(data, complete_data, R0val){
counts_array_R0val<- array(NA, dim=c(nrow(data),ncol(data),5))
counts_array_R0val[,1,]<- data$time
for(i in 2:ncol(data)){
for(j in 1:n_days){
if(j<=4){
counts_array_R0val[which(complete_data$time==j),i,1]<-
data[complete_data$time==j,i,1]*prob_matrix[i-1,1]
}
else{
counts_array_R0val[which(complete_data$time==j),i,1]<-
data[complete_data$time==(j-4),i,1]*prob_matrix[i-1,1]
}
}
counts_array_R0val[,i,2]<- counts_array_R0val[,i,1]*prob_matrix[i-1,2]
for(k in 1:n_days){
if(k<=4){
counts_array_R0val[which(complete_data$time==k),i,3]<-
counts_array_R0val[which(complete_data$time==k),i,2]*prob_matrix[i-1,3]
}
else{
counts_array_R0val[which(complete_data$time==k),i,3]<-
counts_array_R0val[which(complete_data$time==(k-4)),i,2]*prob_matrix[i-1,3]
}
}
for(l in 1:n_days){
if(l<=3){
counts_array_R0val[which(complete_data$time==l),i,4]<-
counts_array_R0val[which(complete_data$time==(l)),i,3]*prob_matrix[i-1,5]
}
else{
counts_array_R0val[which(complete_data$time==l),i,4]<-
counts_array_R0val[which(complete_data$time==(l-3)),i,3]*prob_matrix[i-1,5]
}
}
for(m in 1:n_days){
if(m<=5){
counts_array_R0val[which(complete_data$time==m),i,5]<-
(counts_array_R0val[which(complete_data$time==(m)),i,3]*prob_matrix[i-1,4])+(counts_array_R0val[which(complete_data$time==(m)),i,4]*prob_matrix[i-1,6])
}
else if(m>5&m<=8){
counts_array_R0val[which(complete_data$time==m),i,5]<-
(counts_array_R0val[which(complete_data$time==(m)),i,3]*prob_matrix[i-1,4])+(counts_array_R0val[which(complete_data$time==(m-5)),i,4]*prob_matrix[i-1,6])
}
else{
counts_array_R0val[which(complete_data$time==m),i,5]<-
(counts_array_R0val[which(complete_data$time==(m-8)),i,3]*prob_matrix[i-1,4])+(counts_array_R0val[which(complete_data$time==(m-5)),i,4]*prob_matrix[i-1,6])
}
}
}
return(counts_array_R0val)
}
counts_array_A<- incident_classifications(combined_incident_A,complete_scenarioA,A)
counts_array_B<- incident_classifications(combined_incident_B,complete_scenarioB,B)
counts_array_C<- incident_classifications(combined_incident_C,complete_scenarioC,C)
counts_array_D<- incident_classifications(combined_incident_D,complete_scenarioD,D)
counts_array_E<- incident_classifications(combined_incident_E,complete_scenarioE,E)
counts_array_asymp_A<- incident_classifications(combined_incident_asymp_A,complete_scenarioA,A)
counts_array_asymp_B<- incident_classifications(combined_incident_asymp_B,complete_scenarioB,B)
counts_array_asymp_C<- incident_classifications(combined_incident_asymp_C,complete_scenarioC,C)
counts_array_asymp_D<- incident_classifications(combined_incident_asymp_D,complete_scenarioD,D)
counts_array_asymp_E<- incident_classifications(combined_incident_asymp_E,complete_scenarioE,E)
```
```{r,message=FALSE, warning=FALSE, echo=FALSE}
##define the point estimates of how long people stay in each of the state
length_of_stay<- c(NA,11,11,11,NA)
##cumulative_counts function
cumulative_counts<- function(data, R0val){
cumulative_array_R0val<- array(NA, dim=dim(data))
cumulative_array_R0val[,1,]<- data[,1,]
for(j in 2:dim(cumulative_array_R0val)[2]){
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==1),j,]<-
data[which(data[,1,1]==1),j,]
for(i in 2:n_days){
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==i),j,1]<-
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==(i-1)),j,1]+
data[which(data[,1,1]==i),j,1]
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==i),j,5]<-
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==(i-1)),j,5]+
data[which(data[,1,1]==i),j,5]
}
for(k in 2:n_days){
if(k<=length_of_stay[2]){
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==k),j,2]<-
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==(k-1)),j,2]+
data[which(data[,1,1]==k),j,2]
}
else {
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==k),j,2]<-
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==k-1),j,2]+
data[which(data[,1,1]==k),j,2]-data[which(data[,1,1]==(k-length_of_stay[2])),j,2]
}
}
for(l in 2:n_days){
if(l <= length_of_stay[3]){
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==l),j,3]<-
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==(l-1)),j,3]+
data[which(data[,1,1]==l),j,3]
}
else{
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==l),j,3]<-
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==(l-1)),j,3]+
data[which(data[,1,1]==l),j,3]-data[which(data[,1,1]==(l-length_of_stay[3])),j,3]
}
}
for(m in 2:n_days){
if(m<=length_of_stay[4]){
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==m),j,4]<-
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==(m-1)),j,4]+
data[which(data[,1,1]==m),j,4]
}
else{
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==m),j,4]<-
cumulative_array_R0val[which(cumulative_array_R0val[,1,1]==(m-1)),j,4]+
data[which(data[,1,1]==m),j,4]-data[which(data[,1,1]==(m-length_of_stay[4])),j,4]
}
}
}
return(cumulative_array_R0val)
}
cumulative_array_A<- cumulative_counts(counts_array_A,A)
cumulative_array_B<- cumulative_counts(counts_array_B,B)
cumulative_array_C<- cumulative_counts(counts_array_C,C)
cumulative_array_D<- cumulative_counts(counts_array_D,D)
cumulative_array_E<- cumulative_counts(counts_array_E,E)
cumulative_array_asymp_A<- cumulative_counts(counts_array_asymp_A,A)
cumulative_array_asymp_B<- cumulative_counts(counts_array_asymp_B,B)
cumulative_array_asymp_C<- cumulative_counts(counts_array_asymp_C,C)
cumulative_array_asymp_D<- cumulative_counts(counts_array_asymp_D,D)
cumulative_array_asymp_E<- cumulative_counts(counts_array_asymp_E,E)
```
```{r, message=FALSE, warning-FALSE, echo=FALSE}
median_incident_symptomatic<- function(data, R0val){
median_incident_combined_R0val<- matrix(NA,n_days,ncol(data))
for(i in 1:nrow(median_incident_combined_R0val)){
median_incident_combined_R0val[i,]<-
apply(data[which(data[,1]==i),],2,median)
}
upper_incident_combined_R0val<- matrix(NA,n_days,ncol(data))
for(i in 1:nrow(upper_incident_combined_R0val)){
upper_incident_combined_R0val[i,]<-
apply(data[which(data[,1]==i),],2,quantile, probs=(0.75), na.rm=TRUE)
}
lower_incident_combined_R0val<- matrix(NA,n_days,ncol(data))
for(i in 1:nrow(lower_incident_combined_R0val)){
lower_incident_combined_R0val[i,]<-
apply(data[which(data[,1]==i),],2,quantile, probs=(0.25), na.rm=TRUE)
}
return(list(median_incident_combined_R0val, upper_incident_combined_R0val,
lower_incident_combined_R0val))
}
incident_symptomatic_A<- median_incident_symptomatic(combined_incident_A,A)
median_incident_symptomatic_A<- incident_symptomatic_A[[1]]
upper_incident_symptomatic_A<- incident_symptomatic_A[[2]]
lower_incident_symptomatic_A<- incident_symptomatic_A[[3]]
incident_symptomatic_B<- median_incident_symptomatic(combined_incident_B,B)
median_incident_symptomatic_B<- incident_symptomatic_B[[1]]
upper_incident_symptomatic_B<- incident_symptomatic_B[[2]]
lower_incident_symptomatic_B<- incident_symptomatic_B[[3]]
incident_symptomatic_C<- median_incident_symptomatic(combined_incident_C,C)
median_incident_symptomatic_C<- incident_symptomatic_C[[1]]
upper_incident_symptomatic_C<- incident_symptomatic_C[[2]]
lower_incident_symptomatic_C<- incident_symptomatic_C[[3]]
incident_symptomatic_D<- median_incident_symptomatic(combined_incident_D,D)
median_incident_symptomatic_D<- incident_symptomatic_D[[1]]
upper_incident_symptomatic_D<- incident_symptomatic_D[[2]]
lower_incident_symptomatic_D<- incident_symptomatic_D[[3]]
incident_symptomatic_E<- median_incident_symptomatic(combined_incident_E,E)
median_incident_symptomatic_E<- incident_symptomatic_E[[1]]
upper_incident_symptomatic_E<- incident_symptomatic_E[[2]]
lower_incident_symptomatic_E<- incident_symptomatic_E[[3]]
incident_asymptomatic_A<- median_incident_symptomatic(combined_incident_asymp_A,A)
incident_asymptomatic_B<- median_incident_symptomatic(combined_incident_asymp_B,B)
incident_asymptomatic_C<- median_incident_symptomatic(combined_incident_asymp_C,C)
incident_asymptomatic_D<- median_incident_symptomatic(combined_incident_asymp_D,D)
incident_asymptomatic_E<- median_incident_symptomatic(combined_incident_asymp_E,E)
####################################################################
median_counts_array<- function(data, R0val){
median_counts_array<- array(NA, dim=c(n_days,dim(data)[2], dim(data)[3]))
for(i in 1:dim(median_counts_array)[3]){
for(j in 1:dim(median_counts_array)[1]){
median_counts_array[j,,i]<- apply(data[which(data[,1,1]==j),,i],2,median)
}
}
upper_counts_array<- array(NA, dim=c(n_days,dim(data)[2], dim(data)[3]))
for(i in 1:dim(upper_counts_array)[3]){
for(j in 1:dim(upper_counts_array)[1]){
upper_counts_array[j,,i]<- apply(data[which(data[,1,1]==j),,i],2,quantile, prob=0.75,na.rm=T)
}
}
lower_counts_array<- array(NA, dim=c(n_days,dim(data)[2], dim(data)[3]))
for(i in 1:dim(lower_counts_array)[3]){
for(j in 1:dim(lower_counts_array)[1]){
lower_counts_array[j,,i]<- apply(data[which(data[,1,1]==j),,i],2,quantile, prob=0.25,na.rm=T)
}
}
return(list(median_counts_array, upper_counts_array,lower_counts_array))
}
summary_counts_array_A<- median_counts_array(counts_array_A,A)
median_counts_array_A<- summary_counts_array_A[[1]]
upper_counts_array_A<- summary_counts_array_A[[2]]
lower_counts_array_A<- summary_counts_array_A[[3]]
summary_counts_array_B<- median_counts_array(counts_array_B,B)
median_counts_array_B<- summary_counts_array_B[[1]]
upper_counts_array_B<- summary_counts_array_B[[2]]
lower_counts_array_B<- summary_counts_array_B[[3]]
summary_counts_array_C<- median_counts_array(counts_array_C,C)
median_counts_array_C<- summary_counts_array_C[[1]]
upper_counts_array_C<- summary_counts_array_C[[2]]
lower_counts_array_C<- summary_counts_array_C[[3]]
summary_counts_array_D<- median_counts_array(counts_array_D,D)
median_counts_array_D<- summary_counts_array_D[[1]]
upper_counts_array_D<- summary_counts_array_D[[2]]
lower_counts_array_D<- summary_counts_array_D[[3]]
summary_counts_array_E<- median_counts_array(counts_array_E,E)
median_counts_array_E<- summary_counts_array_E[[1]]
upper_counts_array_E<- summary_counts_array_E[[2]]
lower_counts_array_E<- summary_counts_array_E[[3]]
summary_counts_array_A<- median_counts_array(counts_array_asymp_A,A)
summary_counts_array_B<- median_counts_array(counts_array_asymp_B,B)
summary_counts_array_C<- median_counts_array(counts_array_asymp_C,C)
summary_counts_array_D<- median_counts_array(counts_array_asymp_D,D)
summary_counts_array_E<- median_counts_array(counts_array_asymp_E,E)
########################################
median_cumulative_array<- function(data, R0val){
median_cumulative_array<- array(NA, dim=c(n_days,dim(data)[2], dim(data)[3]))
for(i in 1:dim(median_cumulative_array)[3]){
for(j in 1:dim(median_cumulative_array)[1]){
median_cumulative_array[j,,i]<- apply(data[which(data[,1,1]==j),,i],2,median,na.rm=T)
}
}
upper_cumulative_array<- array(NA, dim=c(n_days,dim(data)[2], dim(data)[3]))
for(i in 1:dim(upper_cumulative_array)[3]){
for(j in 1:dim(upper_cumulative_array)[1]){
upper_cumulative_array[j,,i]<- apply(data[which(data[,1,1]==j),,i],2,quantile, prob=0.75,na.rm=T)
}
}
lower_cumulative_array<- array(NA, dim=c(n_days,dim(data)[2], dim(data)[3]))
for(i in 1:dim(lower_cumulative_array)[3]){
for(j in 1:dim(lower_cumulative_array)[1]){
lower_cumulative_array[j,,i]<- apply(data[which(data[,1,1]==j),,i],2,quantile, prob=0.25,na.rm=T)
}
}
return(list(median_cumulative_array, upper_cumulative_array,lower_cumulative_array))
}
summary_cumulative_array_A<- median_cumulative_array(cumulative_array_A,A)
median_cumulative_array_A<- summary_cumulative_array_A[[1]]
upper_cumulative_array_A<- summary_cumulative_array_A[[2]]
lower_cumulative_array_A<- summary_cumulative_array_A[[3]]
summary_cumulative_array_B<- median_cumulative_array(cumulative_array_B,B)
median_cumulative_array_B<- summary_cumulative_array_B[[1]]
upper_cumulative_array_B<- summary_cumulative_array_B[[2]]
lower_cumulative_array_B<- summary_cumulative_array_B[[3]]
summary_cumulative_array_C<- median_cumulative_array(cumulative_array_C,C)
median_cumulative_array_C<- summary_cumulative_array_C[[1]]
upper_cumulative_array_C<- summary_cumulative_array_C[[2]]
lower_cumulative_array_C<- summary_cumulative_array_C[[3]]
summary_cumulative_array_D<- median_cumulative_array(cumulative_array_D,D)
median_cumulative_array_D<- summary_cumulative_array_D[[1]]
upper_cumulative_array_D<- summary_cumulative_array_D[[2]]
lower_cumulative_array_D<- summary_cumulative_array_D[[3]]
summary_cumulative_array_E<- median_cumulative_array(cumulative_array_E,E)
median_cumulative_array_E<- summary_cumulative_array_E[[1]]
upper_cumulative_array_E<- summary_cumulative_array_E[[2]]
lower_cumulative_array_E<- summary_cumulative_array_E[[3]]
summary_cumulative_asymp_array_A<- median_cumulative_array(cumulative_array_asymp_A,A)
summary_cumulative_asymp_array_B<- median_cumulative_array(cumulative_array_asymp_B,B)
summary_cumulative_asymp_array_C<- median_cumulative_array(cumulative_array_asymp_C,C)
summary_cumulative_asymp_array_D<- median_cumulative_array(cumulative_array_asymp_D,D)
summary_cumulative_asymp_array_E<- median_cumulative_array(cumulative_array_asymp_E,E)
```
-------------------------------
**Key Questions and Findings**
*Question 1: When and how big are the peaks of infection, hospitalization, and death?*
*Important considerations:*
- We differentiate between asymptomatic and symptomatic infections and consider that a different ratio of asymptomatic:symptomatic cases exists by age group. Early evidence from six countries suggests that the probability of manifesting clinical symptoms may be positively related with age, with symptoms in approximately 20% of children under 10 and in over 70% of older adults [Davies et al. 2020](https://www.medrxiv.org/content/10.1101/2020.03.24.20043018v1.full.pdf).
- Our estimate of how many incident infections become severe and get hospitalized does not consider the prevalence of co-morbidities like diabetes and hypertension in the population or any related increased likelihood of death. Though we consider the age structure of Baltimore and age-specific hospitalization rates from the CDC, our estimates of who gets hospitalized and experiences severe disease may be underestimated.
- We assume there are no deaths among individuals with asymptomatic and mild infections.
- We run 1000 simulations for each intervention strategy. This allows us to examine the variability in our model estimates. We visualize this by displaying 15 random simulations in our figures in addition to the median of all simulations. The uncertainty ranges shown in the text boxes are IQRs.
**Figure 1.** The number of incident symptomatic infections for A) an uncontrolled scenario and B) for each intervention strategy (labelled A through D). The dark line represents the median of 1000 simulations per strategy and the faint lines represent the 15 random simulations. Note the square root scaled y-axis.
```{r,message=FALSE, echo=FALSE, warning=FALSE}
make_fig_1<- function(complete_data_1,complete_data_2,complete_data_3,complete_data_4,complete_data_5,
median_data_1,median_data_2,median_data_3,median_data_4,median_data_5,
combined_data_1,combined_data_2,combined_data_3,combined_data_4,combined_data_5){
random_sample_A<- which(complete_data_1$run_index%in%sample(rep(1:1000),15,replace=F))
random_sample_B<- which(complete_data_2$run_index%in%sample(rep(1:1000), 15, replace=F))
random_sample_C<- which(complete_data_3$run_index%in%sample(rep(1:1000), 15, replace = F))
random_sample_D<- which(complete_data_4$run_index%in%sample(rep(1:1000), 15, replace = F))
random_sample_E<- which(complete_data_5$run_index%in%sample(rep(1:500), 15, replace = F))
incident_symptomatic_wide<- as.data.frame(cbind(median_data_1[,1],
apply(median_data_1[,-1],1,sum),
apply(median_data_2[,-1],1,sum),
apply(median_data_3[,-1],1,sum),
apply(median_data_4[,-1],1,sum),
apply(median_data_5[,-1],1,sum)))
colnames(incident_symptomatic_wide)<- c("Time", "A,Median", "B,Median", "C,Median", "D,Median", "E,Median")
random_sims<- as.data.frame(cbind(combined_data_1[random_sample_A,1],
apply(combined_data_1[random_sample_A,-1],1,sum),
apply(combined_data_2[random_sample_B,-1],1,sum),
apply(combined_data_3[random_sample_C,-1],1,sum),
apply(combined_data_4[random_sample_D,-1],1,sum),
apply(combined_data_5[random_sample_E,-1],1,sum)))
colnames(random_sims)<- c("Time", "A,Random", "B,Random", "C,Random", "D,Random", "E,Random")
incident_symptomatic_long<- incident_symptomatic_wide%>%
gather(scenario, incident_symptomatic, c("A,Median", "B,Median", "C,Median", "D,Median", "E,Median"), factor_key = TRUE)
random_sims_long<- random_sims%>%
gather(scenario, incident_symptomatic, c("A,Random", "B,Random", "C,Random", "D,Random", "E,Random"), factor_key=TRUE)
df_sep<- incident_symptomatic_long %>%
separate(scenario, into=c("scenario", "summary_stat"), sep=",")%>%
dplyr::mutate(scenario = recode(scenario,"E"="Uncontrolled"))%>%
mutate(Time=rep(seq(as.Date(start_date),by='day',length.out=n_days),5))
random_df_sep<- random_sims_long %>%
separate(scenario, into=c("scenario", "summary_stat"), sep=",")%>%
dplyr::mutate(scenario = recode(scenario,"E"="Uncontrolled"))%>%
mutate(Time=rep(seq(as.Date(start_date),by='day',length.out=n_days),75),
number=c(rep(1:15,each=n_days),rep(1:15,each=n_days),rep(1:15,each=n_days),
rep(1:15,each=n_days),rep(1:15,each=n_days)))
###cumulative cases figure
cumsum_cases <- df_sep %>% dplyr::filter(scenario!="Uncontrolled") %>%
group_by(scenario) %>%
dplyr::mutate(cum_symp = cumsum(incident_symptomatic)) %>% ungroup
return(list(random_sample_A,random_sample_B,random_sample_C,random_sample_D,random_sample_E,df_sep,random_df_sep,cumsum_cases))
}
f_1<- make_fig_1(complete_scenarioA,complete_scenarioB,complete_scenarioC,complete_scenarioD, complete_scenarioE,
median_incident_symptomatic_A,median_incident_symptomatic_B,median_incident_symptomatic_C,
median_incident_symptomatic_D,median_incident_symptomatic_E,
combined_incident_A,combined_incident_B,combined_incident_C,combined_incident_D,combined_incident_E)
f_1_asymp<- make_fig_1(complete_scenarioA,complete_scenarioB,complete_scenarioC,complete_scenarioD, complete_scenarioE,
incident_asymptomatic_A[[1]],incident_asymptomatic_B[[1]],incident_asymptomatic_C[[1]],
incident_asymptomatic_D[[1]],incident_asymptomatic_E[[1]],
combined_incident_asymp_A,combined_incident_asymp_B,combined_incident_asymp_C,
combined_incident_asymp_D,combined_incident_asymp_E)
########plots
plt_incident_cases<- ggplot() +
geom_line(data=f_1[[6]] %>% dplyr::filter(scenario!="Uncontrolled"),aes(y = incident_symptomatic, x=Time,color=str_wrap(scenario,10)), size=1.2)+
geom_line(data=f_1[[7]] %>% dplyr::filter(scenario!="Uncontrolled"),aes(y = incident_symptomatic, x=Time, group=number,color=str_wrap(scenario,10)),alpha=0.2, size=0.75)+
labs(y="Incident Symptomatic Cases",x="Time in Days") + theme(text = element_text(size=30)) +
#labs(color="Strategy",linetype="Summary Statistic") +
#theme(axis.text.x = element_text(face="bold", color="#993333", size=20, angle=45),axis.text.y = element_text(face="bold",size=20)) +
theme_bw() + facet_grid(.~scenario) + guides(color=FALSE) + scale_x_date(date_labels = "%b", date_breaks = "2 month", date_minor_breaks = "1 month",limits = as.Date(c(NA,end_date))) +
theme(strip.background = element_blank(), strip.text.x = element_text(size = 12,face = "bold.italic")) + scale_color_brewer(palette="Dark2") #+ ylim(0,100) #+ scale_y_sqrt(breaks = c(100, 1000, 2500, 5000, 7500))
f_1[[6]]<- as.data.frame(cbind(f_1[[6]], rep(0,nrow(f_1[[6]]))))
colnames(f_1[[6]])<- c("Time", "scenario", "summary_stat", "incident_symptomatic",
"number")
incident_cases_df<- as.data.frame(rbind(f_1[[6]],f_1[[7]]))
write.csv(incident_cases_df, "Figure_1_incident_cases.csv")
plt_incident_cases_E<- ggplot() +
geom_line(data=f_1[[6]] %>% dplyr::filter(scenario=="Uncontrolled"),aes(y = incident_symptomatic, x=Time,color=str_wrap(scenario,10)), size=1.2)+
geom_line(data=f_1[[7]] %>% dplyr::filter(scenario=="Uncontrolled"),aes(y = incident_symptomatic, x=Time,color=str_wrap(scenario,10),group=number), alpha=0.2, size=0.75)+
labs(y="Incidenct Symptomatic Cases",x="Time in Days") + theme(text = element_text(size=20)) +
theme_bw() + facet_grid(.~scenario) + guides(color=FALSE) + scale_x_date(date_labels = "%b", date_breaks = "1 month", date_minor_breaks = "1 month",limits =as.Date(c(NA,end_date))) +
theme(strip.background = element_blank(), strip.text.x = element_text(size = 12,face = "bold.italic"))+ scale_color_manual(values=wes_palette(n=3, name="Moonrise2")) #+ scale_y_sqrt(breaks = c(100, 1000, 2500, 5000))
plt_incident_cases_total<- ggplot() +
geom_line(data=f_1_asymp[[6]] %>% dplyr::filter(scenario!="Uncontrolled"),aes(y = incident_symptomatic, x=Time,color=str_wrap(scenario,10)), size=1.2)+
geom_line(data=f_1_asymp[[7]] %>% dplyr::filter(scenario!="Uncontrolled"),aes(y = incident_symptomatic, x=Time, group=number,color=str_wrap(scenario,10)),alpha=0.2, size=0.75)+
labs(y="Incident Cases (Asymp + Symp)",x="Time in Days") + theme(text = element_text(size=20)) +
theme_bw() + facet_grid(.~scenario) + guides(color=FALSE) + scale_x_date(date_labels = "%b", date_breaks = "2 month", date_minor_breaks = "1 month",limits = as.Date(c(NA,end_date))) +
theme(strip.background = element_blank(), strip.text.x = element_text(size = 12,face = "bold.italic")) + scale_color_brewer(palette="Dark2") #+ ylim(0,100) #+ scale_y_sqrt(breaks = c(100, 1000, 2500, 5000, 7500,10000))
f_1_asymp[[6]]<- as.data.frame(cbind(f_1_asymp[[6]],rep(0,nrow(f_1_asymp[[6]]))))
colnames(f_1_asymp[[6]])<- c("Time", "scenario", "summary_stat", "incident_symptomatic",
"number")
incident_cases_asymp_df<- as.data.frame(rbind(f_1_asymp[[6]],f_1_asymp[[7]]))
write.csv(incident_cases_asymp_df, "Figure_1_incident_cases_asymp.csv")
plt_incident_cases_total_E<- ggplot() +
geom_line(data=f_1_asymp[[6]] %>% dplyr::filter(scenario=="Uncontrolled"),aes(y = incident_symptomatic, x=Time,color=str_wrap(scenario,10)), size=1.2)+
geom_line(data=f_1_asymp[[7]] %>% dplyr::filter(scenario=="Uncontrolled"),aes(y = incident_symptomatic, x=Time,color=str_wrap(scenario,10),group=number), alpha=0.2, size=0.75)+
labs(y="Incident Cases (Asymp + Symp)",x="Time in Days") + theme(text = element_text(size=20)) +
theme_bw() + facet_grid(.~scenario) + guides(color=FALSE) + scale_x_date(date_labels = "%b", date_breaks = "1 month", date_minor_breaks = "1 month",limits =as.Date(c(NA,end_date))) +
theme(strip.background = element_blank(), strip.text.x = element_text(size = 12,face = "bold.italic"))+ scale_color_manual(values=wes_palette(n=3, name="Moonrise2")) #+ scale_y_sqrt(breaks = c(100, 1000, 2500, 5000))
########peak estimates table
#change inputs from median to lower or upper to get bounds
calc_1 <- f_1[[6]] %>% dplyr::group_by(scenario,summary_stat) %>% dplyr::filter(Time<=as.Date(end_date)) %>%
dplyr::summarise(incident_symptomatic=max(incident_symptomatic,na.rm=TRUE)) %>%
ungroup %>% left_join(.,f_1[[6]])
```
<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "row"><div class = "col-md-6"><div class = "blue">
*Input text box description here.*
</div></div>
<div class = "col-md-6">
```{r,message=FALSE, echo=FALSE, warning=FALSE, fig.align="center", fig.cap="Figure 1A."}
plt <- cowplot::plot_grid(plt_incident_cases_E, labels=c('A'), align = "h", nrow=1)
print(plt)
```
</div>
</div>
<br><br>
<div class = "row"><div class = "col-md-6"><div class = "blue">
*Input text box description here.*
</div></div>
<div class = "col-md-6">
```{r,message=FALSE, echo=FALSE, warning=FALSE, fig.align="center", fig.cap="Figure 1B."}
plt <- cowplot::plot_grid(plt_incident_cases , labels=c('B'), align = "h", nrow=1)
print(plt)
```
</div>
</div>
<br><br>
**Figure 2.** The number of incident hospitalizations for A) an uncontrolled scenario and B) for each intervention strategy (labelled A through D). The dark line represents the median of 1000 simulations per strategy and the faint lines represent the 15 random simulations. Note the square root scaled y-axis.
```{r, message=FALSE, warning=FALSE, echo=FALSE}
make_fig_2<- function(random_sample_1,random_sample_2,random_sample_3,random_sample_4,random_sample_5,
counts_data_1,counts_data_2,counts_data_3,counts_data_4,counts_data_5,
median_counts_data_1,median_counts_data_2,median_counts_data_3,
median_counts_data_4,median_counts_data_5){
incident_hosp_wide<- as.data.frame(cbind(median_counts_data_1[,1,1],
apply(median_counts_data_1[,-1,1],1,sum),
apply(median_counts_data_2[,-1,1],1,sum),
apply(median_counts_data_3[,-1,1],1,sum),
apply(median_counts_data_4[,-1,1],1,sum),
apply(median_counts_data_5[,-1,1],1,sum)))
colnames(incident_hosp_wide)<- c("Time", "A,Median", "B,Median", "C,Median", "D,Median", "E,Median")
random_sims<- as.data.frame(cbind(counts_data_1[random_sample_1,1,1],
apply(counts_data_1[random_sample_1,-1,1],1,sum),
apply(counts_data_2[random_sample_2,-1,1],1,sum),
apply(counts_data_3[random_sample_3,-1,1],1,sum),
apply(counts_data_4[random_sample_4,-1,1],1,sum),
apply(counts_data_5[random_sample_5,-1,1],1,sum)))
colnames(random_sims)<- c("Time", "A,Random", "B,Random", "C,Random", "D,Random", "E,Random")
incident_hosp_long<- incident_hosp_wide%>%
gather(scenario, incident_hosp, c("A,Median", "B,Median", "C,Median", "D,Median", "E,Median"), factor_key = TRUE)
random_sims_long<- random_sims%>%
gather(scenario, incident_hosp, c("A,Random", "B,Random", "C,Random", "D,Random", "E,Random"), factor_key=TRUE)
df_sep<- incident_hosp_long %>%
separate(scenario, into=c("scenario", "summary_stat"), sep=",")%>%
dplyr::mutate(scenario = recode(scenario,"E"="Uncontrolled"))%>%
mutate(Time=rep(seq(as.Date(start_date),by='day',length.out=n_days),5))
random_df_sep<- random_sims_long %>%
separate(scenario, into=c("scenario", "summary_stat"), sep=",")%>%
dplyr::mutate(scenario = recode(scenario,"E"="Uncontrolled"))%>%
mutate(Time=rep(seq(as.Date(start_date),by='day',length.out=n_days),75),
number=c(rep(1:15,each=n_days),rep(1:15,each=n_days),rep(1:15,each=n_days),
rep(1:15,each=n_days),rep(1:15,each=n_days)))
return(list(df_sep,random_df_sep))
}
f_2 <- make_fig_2(f_1[[1]],f_1[[2]],f_1[[3]],f_1[[4]],f_1[[5]],
counts_array_A,counts_array_B,counts_array_C,counts_array_D,counts_array_E,
median_counts_array_A,median_counts_array_B,median_counts_array_C,
median_counts_array_D,median_counts_array_E)
########plots
plt_hosp_cases<- ggplot() +
geom_line(data=f_2[[1]] %>% dplyr::filter(scenario!="Uncontrolled"),aes(y = incident_hosp, x=Time,color=str_wrap(scenario,10)), size=1.2)+
geom_line(data=f_2[[2]] %>% dplyr::filter(scenario!="Uncontrolled"),aes(y = incident_hosp, x=Time, group=number,color=str_wrap(scenario,10)),alpha=0.2, size=0.75)+
labs(y="Incident Hospitalizations",x="Time in Days") +
#labs(color="Strategy",linetype="Summary Statistic") +
theme(axis.text.x = element_text(face="bold", color="#993333", size=20, angle=45),
axis.text.y = element_text(face="bold",size=20)) + theme_bw() + facet_grid(.~scenario) + guides(color=FALSE) + scale_x_date(date_labels = "%b", date_breaks = "2 months", date_minor_breaks = "1 month",limits = as.Date(c(NA,end_date))) +
theme(strip.background = element_blank(), strip.text.x = element_text(size = 12,face = "bold.italic")) + scale_color_brewer(palette="Dark2") #+ ylim(0,100) #+ scale_y_sqrt(breaks = c(100, 1000, 2500))
f_2[[1]]<- as.data.frame(cbind(f_2[[1]], rep(0,nrow(f_2[[1]]))))
colnames(f_2[[1]])<- c("Time", "scenario", "summary_stat", "incident_hosp",
"number")
hosp_cases_df<- as.data.frame(rbind(f_2[[1]],f_2[[2]]))
write.csv(hosp_cases_df, "Figure_2_incident_hosp.csv")
plt_hosp_cases_E<- ggplot() +
geom_line(data=f_2[[1]] %>% dplyr::filter(scenario=="Uncontrolled"),aes(y = incident_hosp, x=Time,color=str_wrap(scenario,10)), size=1.2)+
geom_line(data=f_2[[2]] %>% dplyr::filter(scenario=="Uncontrolled"),aes(y = incident_hosp, x=Time,color=str_wrap(scenario,10),group=number), alpha=0.2, size=0.75)+
labs(y="Incidenct Hospitalizations",x="Time in Days") +
#labs(color="Strategy",linetype="Summary Statistic") +
theme(axis.text.x = element_text(face="bold", color="#993333", size=20, angle=45),
axis.text.y = element_text(face="bold",size=20)) + theme_bw() + facet_grid(.~scenario) + guides(color=FALSE) + scale_x_date(date_labels = "%b", date_breaks = "1 month", date_minor_breaks = "1 month",limits =as.Date(c(NA,end_date))) +
theme(strip.background = element_blank(), strip.text.x = element_text(size = 12,face = "bold.italic"))+ scale_color_manual(values=wes_palette(n=3, name="Moonrise2")) #+ scale_y_sqrt(breaks = c(100, 1000, 2500))
#theme(text = element_text(size=20))
########peak estimates table
calc_2 <- f_2[[1]] %>% dplyr::group_by(scenario,summary_stat) %>% dplyr::filter(Time<=as.Date(end_date)) %>%
dplyr::summarise(incident_hosp=max(incident_hosp,na.rm=TRUE)) %>%
ungroup %>% left_join(.,f_2[[1]])
```
<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "row"><div class = "col-md-6"><div class = "blue">
*Input text box description here.*
</div></div>
<div class = "col-md-6">
```{r,message=FALSE, echo=FALSE, warning=FALSE, fig.align="center", fig.cap="Figure 2A."}
plt <- cowplot::plot_grid(plt_hosp_cases_E, labels=c('A'), align = "h", nrow=1)
print(plt)
```
</div>
</div>
<br><br>
<div class = "row"><div class = "col-md-6"><div class = "blue">
*Input text box description here.*
</div></div>
<div class = "col-md-6">
```{r,message=FALSE, echo=FALSE, warning=FALSE, fig.align="center", fig.cap="Figure 2B."}
plt <- cowplot::plot_grid(plt_hosp_cases , labels=c('B'), align = "h", nrow=1)
print(plt)
```
</div>
</div>
<br><br>
**Figure 3.** The number of incident deaths for A) an uncontrolled scenario and B) for each intervention strategy (labelled A through D). The dark line represents the median of 1000 simulations per strategy and the faint lines represent the 15 random simulations. Note the square root scaled y-axis.
```{r, message=FALSE, warning=FALSE, echo=FALSE}
make_fig_3<- function(random_sample_1,random_sample_2,random_sample_3,random_sample_4,random_sample_5,
counts_data_1,counts_data_2,counts_data_3,counts_data_4,counts_data_5,
median_counts_data_1,median_counts_data_2,median_counts_data_3,
median_counts_data_4,median_counts_data_5){
incident_death_wide<- as.data.frame(cbind(median_counts_data_1[,1,5],
apply(median_counts_data_1[,-1,5],1,sum),
apply(median_counts_data_2[,-1,5],1,sum),
apply(median_counts_data_3[,-1,5],1,sum),
apply(median_counts_data_4[,-1,5],1,sum),
apply(median_counts_data_5[,-1,5],1,sum)))
colnames(incident_death_wide)<- c("Time", "A,Median", "B,Median", "C,Median", "D,Median", "E,Median")
random_sims<- as.data.frame(cbind(counts_data_1[random_sample_1,1,5],
apply(counts_data_1[random_sample_1,-1,5],1,sum),
apply(counts_data_2[random_sample_2,-1,5],1,sum),
apply(counts_data_3[random_sample_3,-1,5],1,sum),
apply(counts_data_4[random_sample_4,-1,5],1,sum),
apply(counts_data_5[random_sample_5,-1,5],1,sum)))
colnames(random_sims)<- c("Time", "A,Random", "B,Random", "C,Random", "D,Random", "E,Random")
incident_death_long<- incident_death_wide%>%
gather(scenario, incident_death, c("A,Median", "B,Median", "C,Median", "D,Median", "E,Median"), factor_key = TRUE)
random_sims_long<- random_sims%>%
gather(scenario, incident_death, c("A,Random", "B,Random", "C,Random", "D,Random", "E,Random"), factor_key=TRUE)
df_sep<- incident_death_long %>%
separate(scenario, into=c("scenario", "summary_stat"), sep=",")%>%
dplyr::mutate(scenario = recode(scenario,"E"="Uncontrolled"))%>%
mutate(Time=rep(seq(as.Date(start_date),by='day',length.out=n_days),5))
random_df_sep<- random_sims_long %>%
separate(scenario, into=c("scenario", "summary_stat"), sep=",")%>%
dplyr::mutate(scenario = recode(scenario,"E"="Uncontrolled"))%>%
mutate(Time=rep(seq(as.Date(start_date),by='day',length.out=n_days),75),
number=c(rep(1:15,each=n_days),rep(1:15,each=n_days),rep(1:15,each=n_days),
rep(1:15,each=n_days),rep(1:15,each=n_days)))
#cumsum
cumsum <- df_sep %>% dplyr::filter(scenario!="Uncontrolled") %>%
group_by(scenario) %>%
dplyr::mutate(cum_death = cumsum(incident_death)) %>% ungroup
return(list(df_sep,random_df_sep,cumsum))
}
f_3<- make_fig_3(f_1[[1]],f_1[[2]],f_1[[3]],f_1[[4]],f_1[[5]],
counts_array_A,counts_array_B,counts_array_C,counts_array_D,counts_array_E,
upper_counts_array_A,upper_counts_array_B,upper_counts_array_C,
upper_counts_array_D,upper_counts_array_E)
########plots
plt_deaths<- ggplot() +
geom_line(data=f_3[[1]] %>% dplyr::filter(scenario!="Uncontrolled"),aes(y = incident_death, x=Time,color=str_wrap(scenario,10)), size=1.2)+
geom_line(data=f_3[[2]] %>% dplyr::filter(scenario!="Uncontrolled"),aes(y = incident_death, x=Time, group=number,color=str_wrap(scenario,10)),alpha=0.2, size=0.75)+
labs(y="Incident Deaths",x="Time in Days") +
#labs(color="Strategy",linetype="Summary Statistic") +
theme(axis.text.x = element_text(face="bold", color="#993333", size=20, angle=45),
axis.text.y = element_text(face="bold",size=20)) + theme_bw() + facet_grid(.~scenario) + guides(color=FALSE) + scale_x_date(date_labels = "%b", date_breaks = "2 months", date_minor_breaks = "1 month",limits = as.Date(c(NA,end_date))) +
theme(strip.background = element_blank(), strip.text.x = element_text(size = 12,face = "bold.italic")) + scale_color_brewer(palette="Dark2") #+ ylim(0,25) #+ scale_y_sqrt(breaks = c(100, 1000, 2500))
f_3[[1]]<- as.data.frame(cbind(f_3[[1]], rep(0,nrow(f_3[[1]]))))
colnames(f_3[[1]])<- c("Time", "scenario", "summary_stat", "incident_death",
"number")
death_cases_df<- as.data.frame(rbind(f_3[[1]],f_3[[2]]))
write.csv(death_cases_df, "Figure_3_incident_death.csv")
plt_deaths_E<- ggplot() +
geom_line(data=f_3[[1]] %>% dplyr::filter(scenario=="Uncontrolled"),aes(y = incident_death, x=Time,color=str_wrap(scenario,10)), size=1.2)+
geom_line(data=f_3[[2]] %>% dplyr::filter(scenario=="Uncontrolled"),aes(y = incident_death, x=Time,color=str_wrap(scenario,10),group=number), alpha=0.2, size=0.75)+
labs(y="Incidenct Deaths",x="Time in Days") +
#labs(color="Strategy",linetype="Summary Statistic") +
theme(axis.text.x = element_text(face="bold", color="#993333", size=20, angle=45),
axis.text.y = element_text(face="bold",size=20)) + theme_bw() + facet_grid(.~scenario) + guides(color=FALSE) + scale_x_date(date_labels = "%b", date_breaks = "1 month", date_minor_breaks = "1 month",limits =as.Date(c(NA,end_date))) +
theme(strip.background = element_blank(), strip.text.x = element_text(size = 12,face = "bold.italic"))+ scale_color_manual(values=wes_palette(n=3, name="Moonrise2")) #+ scale_y_sqrt(breaks = c(100, 1000, 2500))
#theme(text = element_text(size=20))
########peak estimates table
calc_3 <- f_3[[1]] %>% dplyr::group_by(scenario,summary_stat) %>% dplyr::filter(Time<=as.Date(end_date)) %>%
dplyr::summarise(incident_death=max(incident_death,na.rm=TRUE)) %>%
ungroup %>% left_join(.,f_3[[1]])
```
<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "row"><div class = "col-md-6"><div class = "blue">
*Input text box description here.*
</div></div>
<div class = "col-md-6">
```{r,message=FALSE, echo=FALSE, warning=FALSE, fig.align="center", fig.cap="Figure 3A."}
plt <- cowplot::plot_grid(plt_deaths_E , labels=c('A'), align = "h", nrow=1)
print(plt)
```
</div>
</div>
<br><br>
<div class = "row"><div class = "col-md-6"><div class = "blue">
*Input text box description here.*
</div></div>
<div class = "col-md-6">
```{r,message=FALSE, echo=FALSE, warning=FALSE, fig.align="center", fig.cap="Figure 3B."}
plt <- cowplot::plot_grid(plt_deaths , labels=c('B'), align = "h", nrow=1)
print(plt)
```
</div>
</div>
<br><br>
-------------------------------
*Question 2: Considering new daily infections, discharges, and deaths, what is the daily capacity required for hospital beds, ICU beds, and ventilators?*
*Important considerations:*
- These estimates reflect the Baltimore population and do not consider that people from outside Baltimore will seek and require hospitalization in the county.
- Not all infections that require hospitalization will seek care. If home care is common even for severe infections, we would expect a delay in reaching hospital capacity but higher overall mortality.
- We assume cases experience a time lag between illness onset and hospitalization (4 days), ICU admission and death (8 days), ventilation and death (4.5 days) and discharge following recovery (11 days). These estimates were gathered from the literature and are listed in the Technical Appendix.
**Figure 4**. Daily A) hospital beds, B) ICU beds, and C) ventilators in use for each intervention strategy. The dark line represents the median of 1000 simulations per strategy and scenario and the faint lines represent the 15 random simulations. Note the square root scaled y-axis.
```{r, message=FALSE, warning=FALSE, echo=FALSE, fig.align="center", fig.width=6, fig.height=10, fig.cap="Figure 4."}
make_fig_4<- function(random_sample_1,random_sample_2,random_sample_3,random_sample_4,random_sample_5,
cumulative_median_1,cumulative_median_2,cumulative_median_3,cumulative_median_4,cumulative_median_5,
cumulative_data_1,cumulative_data_2,cumulative_data_3,cumulative_data_4,cumulative_data_5){
cumulative_hosp_wide<- as.data.frame(cbind(cumulative_median_1[,1,2],
apply(cumulative_median_1[,-1,2],1,sum),