-
Notifications
You must be signed in to change notification settings - Fork 3
/
bc_school_tracker.Rmd
299 lines (251 loc) · 11.9 KB
/
bc_school_tracker.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
---
title: "BC School Tracker"
author: "Jens von Bergmann"
date: "Last updated at `r format(Sys.time(), '%d %B, %Y - %H:%M',tz='America/Vancouver')`"
output: rmarkdown::github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE,
fig.height = 5,
fig.retina = 2,
fig.width = 7,
message = FALSE,
warning = FALSE,
dpi = 150
)
library(ggplot2)
library(dplyr)
library(tidyr)
library(stringr)
library(rvest)
library(mountainmathHelpers)
library(sf)
library(PROJ)
library(cancensus)
library(lubridate)
library(sanzo)
source(here::here("R/helpers.R"))
options("cancensus.cache_path"=here::here("data"))
ha_colours <- setNames(c(trios$c157,trios$c149),
c("Fraser","Rest of BC","Vancouver Coastal" , "Vancouver Island", "Interior", "Northern"))
extract_table_data <- function(node){
h <- node %>% rvest::html_nodes("thead th") %>% rvest::html_text()
rows <- node %>% rvest::html_nodes("tbody tr")
data <- rows %>% lapply(function(d) d %>%
rvest::html_nodes("td") %>%
rvest::html_text() %>%
t() %>%
as.data.frame) %>%
bind_rows() %>%
setNames(h)
data
}
```
```{r}
get_data_for_page <- function(p=1){
url = "https://us-east-1-renderer-read.knack.com/v1/scenes/scene_1/views/view_3/records?callback=jQuery17201415845315599671_1606757085608&format=both&rows_per_page=100&sort_field=field_16&sort_order=desc&_=1606757085628"
url <- paste0(url,"&page=",p)
d <- httr::GET(url,httr::set_cookies( "connect.sid"="s%3A51R3S-8YTv08QV_IejVfqJsW1RxnUdln.WuHEEIrAF6niDEAB3MWgjvWA%2FkzArewbBDZ%2FppCUdVY"),
httr::accept("text/javascript, application/javascript, application/ecmascript, application/x-ecmascript, */*; q=0.01"),
httr::add_headers("X-Knack-Application-Id"= "5faae3b10442ac00165da195",
"Accept-Encoding"="gzip, deflate, br",
"X-Knack-REST-API-Key"= "renderer",
"x-knack-new-builder"= "true"))
c <- httr::content(d,"text") %>%
gsub("^.*\\(\\{","{",.) %>%
gsub("\\}\\);$","}",.) %>%
jsonlite::fromJSON()
data <- c$records %>% as_tibble()
#print(paste0("Got data for page ",c$current_page," of ",c$total_pages))
attr(data,"total_pages") <- c$total_pages
data
}
clean_data <- function(d){
tibble(Date=as.Date(d$field_16,format="%m/%d/%Y"),
Name=d$field_13_raw %>% lapply(function(f)f$identifier) %>% as.character,
`Health Authority`=d$field_26_raw %>% lapply(function(f)f$identifier) %>% as.character,
Verification=d$field_14_raw %>% lapply(function(f)f$identifier) %>% as.character,
`Exposure dates`=d$field_15,
`Exposure count`=d$field_25,
#Status =d$field_30,
Variant=d$field_38) %>%
bind_cols(d$field_19_raw %>% as_tibble) %>%
mutate(E=str_extract(`Exposure count`,"Exposure \\d+|Secondary \\d+") %>%
unlist() %>%
gsub("Exposure |Secondary ","",.) %>%
as.integer()) %>%
mutate_at(c("latitude","longitude"),as.numeric)
}
get_all_data <- function(){
p=1
raw_data <- get_data_for_page(p)
data <- clean_data(raw_data)
while (attr(raw_data,"total_pages")>p) {
p <- p + 1
raw_data <- get_data_for_page(p)
data <- bind_rows(data, clean_data(raw_data))
}
data
}
extract_first_exposure_date <- function(ed){
ed %>%
gsub("<p>|</p>|<br />|Exposure date |Exposure dates |Exposures dates |<hr />","",.) %>%
strsplit(",|and") %>%
map(trimws) %>%
map(function(d)d[d!=""]) %>%
map(first) %>%
gsub("-.+| \\(.+| \\[.+","",.) %>%
unlist
}
add_first_exposure_date <- function(data){
data %>%
mutate(fe=extract_first_exposure_date(.data$`Exposure dates`))
}
data <- get_all_data()
```
In BC we don't have good data on community level COVID cases. But we can, somewhat cynically, use school exposures as a proxy. The [BC School Covid Tracker project](https://bcschoolcovidtracker.knack.com/bc-school-covid-tracker#home/) has been keeping count of school exposures and meticulously documenting and geocoding the data. That makes is easy for use to create a heatmap of school exposures.
The code for this notebook is [available for anyone to adapt and use for their own purposes](https://github.com/mountainMath/BCCovidSnippets/blob/main/bc_school_tracker.Rmd).
## Variants of concern
The share of school exposures that are related to variants of concern can give an indication of the general proliferation of variants of concern.
We look at the share of school exposures involving variants of concern for each week, where we split the week between Monday and Tuesday as exposure notifications coming out on Monday usually relate to exposures in the preceding week. Variant of concern screening takes some extra time and exposures might get identified as realting to variants of concern a couple of days later, so shares in the most recent week (and sometimes also the week before that) may rise as exposures gets updated. In particular, the lag will bias the share of exposures involving variants of concern in the current week downward.
Shares are computed separately for each Health Authority, we only show Health Authorities that have flagged exposure events as relating to variants of concern.
```{r schools_voc}
plot_data <- data %>%
mutate(`Health Authority`=recode(`Health Authority`,
"Fraser Health Authority"="Fraser",
"Interior Health Authority"="Interior",
"Vancouver Coastal Health"="Vancouver Coastal",
"Vancouver Island Health Authority"="Vancouver Island",
"Northern Health"="Northern")) %>%
mutate(VOC=grepl("concern|variant",`Variant`,ignore.case = TRUE)) %>%
mutate(Week=ceiling_date(Date,"week",week_start=2)-1) %>%
count(Week,VOC,`Health Authority`) %>%
complete(Week,VOC,`Health Authority`,fill=list(n=0)) %>%
group_by(Week,`Health Authority`) %>%
mutate(share=n/sum(n)) %>%
#filter(Week >= min(filter(.,VOC)$Week)) %>%
filter(VOC) %>%
filter(Week >= min(filter(.,VOC,n>0)$Week)) %>%
ungroup %>%
mutate(week=factor(strftime(Week,"%b %d"),levels=sort(unique(Week)) %>% strftime(.,"%b %d")),
w=strftime(Week-2,format = "%U") %>% as.integer)
plot_data %>%
group_by(`Health Authority`) %>%
filter(sum(n)>0) %>%
ggplot(aes(x=week,y=share,fill=`Health Authority`)) +
geom_bar(stat="identity") +
scale_y_continuous(labels=scales::percent) +
scale_fill_manual(values=ha_colours,guide='none') +
facet_wrap("`Health Authority`",ncol=2) +
labs(title="School exposures involving variants of concern",
x="Initial exposure letter in week ending",
y="Share of exposures involving variant of concern",
caption="MountainMath, Data: BC School Covid Tracker")
```
## School Exposure Heat Map
This data is not normalized by population, so it should be viewd in context of the school aged (5-17yo) population further down.
```{r school-tracker-schools}
bb<-metro_van_bbox()
m <- cancensus::get_census("CA16",regions=list(CMA="59933"),geo_format = "sf",level="CSD",quiet = TRUE)
school_data <- data %>%
mutate_at(c("latitude","longitude"),as.numeric) %>%
filter(!is.na(latitude)) %>%
filter(between(latitude,bb$ymin,bb$ymax),
between(longitude,bb$xmin,bb$xmax)) %>%
mutate(E=coalesce(E,1)) %>%
group_by(Name) %>%
top_n(1,E) %>%
expand(latitude,longitude,E,count = seq(1:E)) %>%
st_as_sf(coords=c("longitude","latitude"),crs=4326,agr="constant") %>%
ungroup() %>%
st_jitter(amount=0.005) %>%
cbind(st_coordinates(.)) %>%
rename(!!!c("longitude"="X","latitude"="Y"))
#sf::st_as_sf(coords=c("longitude","latitude"),agr="constant",na.fail = FALSE) %>%
g <- ggplot(m) +
stat_density2d(data=school_data,
aes(x=longitude,y=latitude, fill = ..level.., alpha = ..level..),
h=c(0.05,0.05*cos(pi/180*49)),#n=1000,
bins = 16, geom = "polygon", breaks=c(1,2.5,5,7.5,10,15,20,25,30,40,50,60)) +
scale_fill_viridis_c(guide='none',option="magma") +
scale_alpha_continuous(guide='none') +
geom_roads() +
geom_water(color="grey",size=0.1) +
geom_sf(data=school_data,size=0.1,alpha=0.4) +
geom_sf(fill=NA,color="brown",size=0.1) +
coord_bbox(metro_van_bbox("tight")) +
labs(x=NULL,y=NULL,fill=NULL,
title="Geographic distribution of school exposure notifications",
caption="MountainMath, Data: BC School Covid Tracker")
g
#r<-graph_to_s3(g,"bccovid","school-tracker-schools.png",width=knitr::opts_chunk$get('fig.width'),height=knitr::opts_chunk$get('fig.height'))
```
## Monthly exposure density
Looking at monthly school exposure denisty gives us some sense of how the distribution of exposure notifications has changed over time. The last month only has partial data and the heat map will appear to have lower values in comparison until the month is complete..
```{r school-tracker-monthly, fig.height=10, fig.height=13}
school_data <- data %>%
mutate_at(c("latitude","longitude"),as.numeric) %>%
filter(!is.na(latitude)) %>%
filter(between(latitude,bb$ymin,bb$ymax),
between(longitude,bb$xmin,bb$xmax)) %>%
mutate(Period=strftime(Date,"%b %Y")) %>%
mutate(Period=factor(Period, levels= arrange(.,Date)$Period %>% unique)) %>%
st_as_sf(coords=c("longitude","latitude"),crs=4326,agr="constant") %>%
ungroup() %>%
st_jitter(amount=0.005) %>%
cbind(st_coordinates(.)) %>%
rename(!!!c("longitude"="X","latitude"="Y"))
bb <- c(1,2.5,5,7.5,10,15,20,25,30,40,50,60,100)
bb <- c(1000,2500,5000,7500,10000,15000,20000,25000,30000)
#sf::st_as_sf(coords=c("longitude","latitude"),agr="constant",na.fail = FALSE) %>%
g <- ggplot(m) +
stat_density_2d(data=school_data,
contour_var="count",
aes(x=longitude,y=latitude, fill = ..level.., alpha = ..level..),
h=c(0.05,0.05*cos(pi/180*49)),#n=1000,
geom = "polygon", breaks=bb
) +
scale_fill_viridis_c(guide='none',option="magma",limits=c(0,30000)) +
scale_alpha_continuous(guide='none',limits=c(0,30000)) +
geom_roads() +
geom_water(color="grey",size=0.1) +
geom_sf(data=school_data,size=0.1,alpha=0.4) +
geom_sf(fill=NA,color="brown",size=0.1) +
facet_wrap("Period",ncol=2) +
coord_bbox(metro_van_bbox("tight")) +
labs(x=NULL,y=NULL,fill=NULL,
title="Geographic distribution of school exposure notifications",
caption="MountainMath, Data: BC School Covid Tracker")
g
```
## Children density heat map for comparison
```{r school-tracker-children}
d <- get_census("CA16",regions=list(CMA="59933"),level="DA",geo_format = "sf",vectors=c("v_CA16_25","v_CA16_43","v_CA16_67","v_CA16_70","v_CA16_73"),labels="short", quiet=TRUE) %>%
mutate(children=select(.,matches("v_")) %>% st_drop_geometry()%>% rowSums(na.rm=TRUE))
dd <- d %>%
select(children) %>%
filter(children>0) %>%
mutate(children=as.integer(children/5)) %>%
dotdensity::compute_dots("children") %>%
#st_sample((.)$children) %>%
st_coordinates() %>%
as_tibble()
g <- ggplot(m) +
stat_density2d(data=dd,
aes(x=X,y=Y, fill = ..level.., alpha = ..level..),
h=c(0.05,0.05*cos(pi/180*49)),#n=1000,
bins = 16, geom = "polygon") + #, breaks=c(1,2.5,5,7.5,10,12.5,15,17.5,20,25,30,35)) +
scale_fill_viridis_c(guide='none',option="viridis") +
scale_alpha_continuous(guide='none') +
geom_roads() +
geom_water(color="grey",size=0.1) +
#geom_sf(data=school_data,size=0.1,alpha=0.4) +
geom_sf(fill=NA,color="brown",size=0.1) +
coord_bbox(metro_van_bbox("tight")) +
labs(x=NULL,y=NULL,fill=NULL,
title="Geographic distribution of children 5-17yo",
caption="MountainMath, StatCan Census 2016")
g
#r<-graph_to_s3(g,"bccovid","school-tracker-children.png",width=knitr::opts_chunk$get('fig.width'),height=knitr::opts_chunk$get('fig.height'))
```