-
Notifications
You must be signed in to change notification settings - Fork 0
/
model.run.R
269 lines (219 loc) · 13.3 KB
/
model.run.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
# Code for the paper
# "Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic"
# by Marta Blangiardo, Michela Cameletti, Monica Pirani, Gianni Corsetti, Marco Battaglini, Gianluca Baio
# Last version: 13/08/2020
library(dplyr)
library(INLA)
# Load extra functions
source("make.functions.R")
############################################################################
# 1. PREPARE DATA
############################################################################
## Load the macro areas data
load("./Data/MacroRegions.Rdata")
# Now prepare the data
Sex = "Females" #other possible choice: Males
area = "NordOvest" #other possible choices: NordEst, Sud, Centro, Lombardia
data = make.data(macro.regions,area,Sex=Sex)
graph = paste0("./Graphs/",tolower(area),".graph")
############################################################################
############################################################################
# 2. RUN INLA & SAVE THE OUTPUT
############################################################################
## Formula with temperature
formula = morti ~ 1 +
f(ID1,model="bym",graph=graph,scale.model=T,
hyper=list(theta1=list(prior="loggamma",param=c(1,0.1)),theta2=list(prior="loggamma",param=c(1,0.1)))) +
f(week,model="rw1",replicate=ID_prov,scale.model=TRUE,hyper=list(prec=list(prior="loggamma",param=c(1,0.1)))) +
f(Anno,model="iid") +
f(IDtemp,model="rw2",scale.model=TRUE,hyper=list(theta=list(prior="loggamma",param=c(1,0.1))))
# INLA SET UP
# Under Poisson uses default set up
control.family=inla.set.control.family.default()
# Defines the correct variable to offset the rates in the log-linear predictor
offset = data$E
m = inla(formula,
data=data,
E=offset,
family="Poisson",
control.family=control.family,
verbose = TRUE,
num.threads = round(parallel::detectCores()*.8),
control.compute=list(config = TRUE)
)
file=paste0("Output/",Sex,"/output",area,".Rdata")
save(m,file=file)
############################################################################
# 3. GIVEN THE INLA OUTPUTS,
# SIMULATE FROM THE POSTERIOR DISTRIBUTION & SAVE THE OUTPUT
############################################################################
#
make.posteriors(area,Sex)
############################################################################
# 4. GIVEN THE INLA OUTPUTS AND SIMULATIONS,
# COMPUTE THE PREDICTIONS FOR 2020 & SAVE THE OUTPUT
############################################################################
make.predictions(area,Sex)
############################################################################
# 5. COMBINE THE OUTCOMES
############################################################################
# Analysis of all output
# Loads all the results
require(dplyr)
pred.italy=list()
for (i in names(macro.regions)){
file=paste0("Output/",Sex,"/predictions",i,".Rdata")
load(file)
pred.italy$SMR=bind_rows(pred.italy$SMR,res$SMR)
pred.italy$prediction=bind_rows(pred.italy$prediction,res$prediction)
rm(res)
}
####
# Removes the records that don't have values in the simulations (comuni that don't match the 2016-2019 data)
# also makes 'week' a numeric variable (not a factor)
pred.italy$SMR=pred.italy$SMR %>% filter(!is.na(Obs)) %>% mutate(week=as.numeric(as.character(week)))
pred.italy$prediction=pred.italy$prediction %>% filter(!is.na(Obs)) %>% mutate(week=as.numeric(as.character(week)))
# Aggregates by province
pred.italy$SMR_prov=(pred.italy$SMR %>% group_by(DEN_UTS,week) %>% arrange(COD_PROV,week) %>% slice(1) %>%
select(COD_PROV,DEN_UTS,SIGLA,DEN_REG,COD_REG,week) %>% ungroup()) %>%
bind_cols(pred.italy$SMR %>% group_by(DEN_UTS,week) %>% arrange(week) %>%
summarise_at(vars("Obs",starts_with("sim")),funs(mean)) %>% ungroup()) %>% select(-c(week...8,DEN_UTS...7)) %>%
rename(week=week...6,DEN_UTS=DEN_UTS...2)
pred.italy$SMR_prov=pred.italy$SMR_prov %>%
mutate(mean=apply(as.matrix(pred.italy$SMR_prov %>% select(contains("sim"))),1,mean,na.rm=TRUE),
median=apply(as.matrix(pred.italy$SMR_prov %>% select(contains("sim"))),1,median,na.rm=TRUE),
sd=apply(as.matrix(pred.italy$SMR_prov %>% select(contains("sim"))),1,sd,na.rm=TRUE),
low=apply(as.matrix(pred.italy$SMR_prov %>% select(contains("sim"))),1,quantile,.025,na.rm=TRUE),
upp=apply(as.matrix(pred.italy$SMR_prov %>% select(contains("sim"))),1,quantile,.975,na.rm=TRUE)) %>%
select(COD_PROV,DEN_UTS,SIGLA,DEN_REG,COD_REG,week,Obs,mean,sd,low,median,upp,everything())
pred.italy$prediction_prov=(pred.italy$prediction %>% group_by(DEN_UTS,week) %>% arrange(COD_PROV,week) %>% slice(1) %>%
select(COD_PROV,DEN_UTS,SIGLA,DEN_REG,COD_REG,week) %>% ungroup()) %>%
bind_cols(pred.italy$prediction %>% group_by(DEN_UTS,week) %>% arrange(COD_PROV,week) %>%
summarise_at(vars("Obs",starts_with("sim")),funs(sum)) %>% ungroup()) %>%
select(-c(week...8,DEN_UTS...7)) %>% rename(week=week...6,DEN_UTS=DEN_UTS...2)
pred.italy$prediction_prov=pred.italy$prediction_prov %>%
mutate(mean=apply(as.matrix(pred.italy$prediction_prov %>% select(contains("sim"))),1,mean,na.rm=TRUE),
median=apply(as.matrix(pred.italy$prediction_prov %>% select(contains("sim"))),1,median,na.rm=TRUE),
sd=apply(as.matrix(pred.italy$prediction_prov %>% select(contains("sim"))),1,sd,na.rm=TRUE),
low=apply(as.matrix(pred.italy$prediction_prov %>% select(contains("sim"))),1,quantile,.025,na.rm=TRUE),
upp=apply(as.matrix(pred.italy$prediction_prov %>% select(contains("sim"))),1,quantile,.975,na.rm=TRUE)) %>%
select(COD_PROV,DEN_UTS,SIGLA,DEN_REG,COD_REG,week,Obs,mean,sd,low,median,upp,everything())
# Aggregates by region
pred.italy$prediction_reg=(pred.italy$prediction %>% group_by(DEN_REG,week) %>% arrange(COD_PROV,week) %>% slice(1) %>%
select(COD_REG,DEN_REG,week) %>% ungroup()) %>%
bind_cols(pred.italy$prediction %>% group_by(DEN_REG,week) %>% arrange(COD_PROV,week) %>%
summarise_at(vars("Obs",starts_with("sim")),funs(sum)) %>% ungroup()) %>%
select(-c(week...5,DEN_REG...4)) %>% rename(week=week...3,DEN_REG=DEN_REG...2)
pred.italy$prediction_reg=pred.italy$prediction_reg %>%
mutate(mean=apply(as.matrix(pred.italy$prediction_reg %>% select(contains("sim"))),1,mean,na.rm=TRUE),
median=apply(as.matrix(pred.italy$prediction_reg %>% select(contains("sim"))),1,median,na.rm=TRUE),
sd=apply(as.matrix(pred.italy$prediction_reg %>% select(contains("sim"))),1,sd,na.rm=TRUE),
low=apply(as.matrix(pred.italy$prediction_reg %>% select(contains("sim"))),1,quantile,.025,na.rm=TRUE),
upp=apply(as.matrix(pred.italy$prediction_reg %>% select(contains("sim"))),1,quantile,.975,na.rm=TRUE)) %>%
select(DEN_REG,COD_REG,week,Obs,mean,sd,low,median,upp,everything())
####
# Creates excess deaths [= (est-obs)/obs]
xs=-1*sweep(as.matrix(pred.italy$prediction_prov %>% select(contains("sim"))),
1,pred.italy$prediction_prov$Obs,FUN ="-")
xs=sweep(as.matrix(xs),1,pred.italy$prediction_prov$Obs,FUN ="/")
colnames(xs)=stringr::str_replace(colnames(xs),"sim","xs")
pred.italy$prediction_prov=pred.italy$prediction_prov %>% bind_cols(as_tibble(xs))
pred.italy$prediction_prov=pred.italy$prediction_prov %>% mutate(
mean.excess=apply(as.matrix(pred.italy$prediction_prov %>% select(contains("xs"))),1,mean,na.rm=T),
sd.excess=apply(as.matrix(pred.italy$prediction_prov %>% select(contains("xs"))),1,sd,na.rm=T),
low.excess=apply(as.matrix(pred.italy$prediction_prov %>% select(contains("xs"))),1,quantile,.025,na.rm=T),
upp.excess=apply(as.matrix(pred.italy$prediction_prov %>% select(contains("xs"))),1,quantile,.975,na.rm=T)
) %>% select(COD_PROV,DEN_UTS,SIGLA,DEN_REG,COD_REG,week,Obs,mean.excess,sd.excess,low.excess,upp.excess,everything())
# Creates excess deaths [= (est-obs)/obs]
xs=-1*sweep(as.matrix(pred.italy$prediction %>% select(contains("sim"))),
1,pred.italy$prediction$Obs,FUN ="-")
xs=sweep(as.matrix(xs),1,pred.italy$prediction$Obs,FUN ="/")
colnames(xs)=stringr::str_replace(colnames(xs),"sim","xs")
pred.italy$prediction=pred.italy$prediction %>% bind_cols(as_tibble(xs))
pred.italy$prediction=pred.italy$prediction %>%
mutate(
mean.excess=apply(as.matrix(pred.italy$prediction %>% select(contains("xs"))),1,mean,na.rm=T),
sd.excess=apply(as.matrix(pred.italy$prediction %>% select(contains("xs"))),1,sd,na.rm=T),
low.excess=apply(as.matrix(pred.italy$prediction %>% select(contains("xs"))),1,quantile,.025,na.rm=T),
upp.excess=apply(as.matrix(pred.italy$prediction %>% select(contains("xs"))),1,quantile,.975,na.rm=T)
) %>% select(COD_PROV,DEN_UTS,SIGLA,DEN_REG,COD_REG,week,Obs,mean.excess,sd.excess,low.excess,upp.excess,everything())
# Add population from official data and extrapolation at 2020
pop.tot=as_tibble(read.table("p20_att.txt",header = TRUE))
# Aggregate by age groups
pop.tot=pop.tot %>% rename(COD_PROVCOM=PRO_COM_ATT) %>% group_by(COD_PROVCOM) %>% mutate(Total.male=sum(n_m),Total.female=sum(n_f)) %>%
slice(1) %>% ungroup() %>% select(COD_PROVCOM,contains("Total"))
# Add population to predictions
pred.italy$prediction=pred.italy$prediction %>% left_join(pop.tot) %>% select(COD_PROVCOM,COMUNE,Total.male,Total.female,everything()) %>%
mutate(mean.excess=case_when(Obs==0~NA_real_,
TRUE~mean.excess),
sd.excess=case_when(Obs==0~NA_real_,
TRUE~sd.excess),
low.excess=case_when(Obs==0~NA_real_,
TRUE~low.excess),
upp.excess=case_when(Obs==0~NA_real_,
TRUE~upp.excess)
)
save(pred.italy,file=paste0("Output/",Sex,"/predItaly.Rdata"))
############################################################################
# 6. VISUALISE THE RESULTS
############################################################################
library(gridExtra)
file=paste0("Output/",Sex,"/predItaly.Rdata")
load(file)
# Plots the predicted deaths by comune & week
# Selects comuni with the highest percentage excess deaths
comuni2plot=as.character((pred.italy$prediction %>% filter(Total.male+Total.female>=100000) %>% arrange(desc(mean.excess)) %>%
select(mean.excess,COMUNE,DEN_UTS,DEN_REG) %>% group_by(COMUNE) %>% slice(1) %>% ungroup() %>%
arrange(desc(mean.excess)))$COMUNE)[1:9]
comuni2plot=c("Bergamo","Piacenza","Brescia","Parma","Milano","Cremona","Pesaro","Verona","Bologna")
plot.list=lapply(comuni2plot,function(x) vis.xs.rate(pred.italy,x, sex="Females"))
pdf("excess_rate_females.pdf",width=30,height=24)
do.call(grid.arrange,c(plot.list,ncol=3))
dev.off()
plot.list2=lapply(comuni2plot,function(x) vis.rate(pred.italy,x,sex="Females", multi=TRUE))
pdf("excess_mortality_females.pdf",width=30,height=24)
do.call(grid.arrange,c(plot.list2,ncol=3))
dev.off()
pdf("WeeklyTrendProv_Females.pdf")
lemon::grid_arrange_shared_legend(
make.map(1),make.map(2),make.map(3),make.map(4),make.map(5),make.map(6),
make.map(7),make.map(8),make.map(9),
make.map(10),make.map(11),make.map(12),
make.map(13),nrow=4,ncol=4,position="bottom"
)
dev.off()
# Overall trends
# BUT MAKE TRENDS x10,000
vis.trends(pred.italy,"Lombardia")
regions=as.character((pred.italy$prediction_reg %>% group_by(DEN_REG) %>% slice(1) %>% ungroup())$DEN_REG)
plot.list=lapply(regions,function(x) vis.trends(pred.italy,x))
pdf("total_trends.pdf",width=38,height=30)
do.call(grid.arrange,c(plot.list,ncol=4))
dev.off()
relevant=list()
relevant[[1]]=c("Piemonte","Valle d'Aosta", "Liguria")
relevant[[2]]=c("Lombardia")
relevant[[3]]=c("Emilia-Romagna", "Trentino-Alto Adige","Veneto","Friuli Venezia Giulia")
relevant[[4]]=c("Abruzzo","Lazio","Marche","Molise","Toscana","Umbria")
relevant[[5]]=c("Basilicata","Calabria","Campania","Puglia","Sardegna","Sicilia")
names(relevant)=c("NordOvest","Lombardia","NordEst","Centro","Sud")
groups=list()
groups[[1]]=c("Piemonte","Valle d'Aosta", "Liguria")
groups[[2]]="Lombardia"
groups[[3]]=c("Emilia-Romagna", "Trentino-Alto Adige","Veneto","Friuli Venezia Giulia")
groups[[4]]=c("Lazio","Marche","Toscana","Umbria")
groups[[5]]=c("Abruzzo","Basilicata","Calabria","Campania","Molise","Puglia","Sardegna","Sicilia")
names(groups)=c("North-western Italy","Lombardia","North-eastern Italy","Central Italy","Southern Italy + Islands")
plot.list=lapply(groups, function(x) vis.trends(pred.italy,x,ylim=c(500,4000)))
pdf(paste0("total_trends_macroarea_",Sex,".pdf"),width=18,height=16)
do.call(grid.arrange,c(plot.list,ncol=2))
dev.off()
grid.arrange(arrangeGrob(plot.list,ncol=2,heights=c(4,1),widths=c(2,1)))
# This will tell me the posterior probability that Lombardia is outside the observed range in week 9 to complement the text
datatemp=males$prediction_reg %>% filter(DEN_REG %in% region) %>% arrange(week)
datatemp=datatemp %>%
mutate(low=apply(as.matrix(datatemp %>% select(contains("sim"))),1,quantile,q[1]),
upp=apply(as.matrix(datatemp %>% select(contains("sim"))),1,quantile,q[2]))
datatemp=datatemp %>% left_join(pred$prediction %>% filter(week==1) %>% group_by(DEN_REG) %>% summarise(pop=sum(Total.male)) %>% ungroup()) %>%
select(DEN_REG,pop,everything())
datatemp %>% mutate(prob=apply(as.matrix(datatemp %>% select(contains("sim"))),1,function(x) sum(x/pop*scale > Obs/pop*scale)/1000)) %>% select(prob,everything())