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Belgium.R
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Belgium.R
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library(dplyr)
library(readr)
library(data.table)
library(ggridges)
library(ggplot2)
library(lubridate)
library(scales)
library(sqldf)
library(tidyr)
library(gt)
library(zoo)
library(lme4)
current_country_hmd_code = "BEL"
source("country.R")
### COVID DEATH
# Load COVID death
if ( !file.exists("./data/BEL/COVID19BE_MORT.csv")){
download.file("https://epistat.sciensano.be/Data/COVID19BE_MORT.csv", "./data/BEL/COVID19BE_MORT.csv")
}
covid_death <-
read_csv("./data/BEL/COVID19BE_MORT.csv", col_types = cols(REGION = col_skip()))
names(covid_death) <- c("day","age_covidgroup","sex", "covid_death")
# Group COVID data by week.
covid_death$date <- covid_death$day - wday(covid_death$day) - 1
covid_death <- covid_death %>%
group_by(age_covidgroup, sex, date) %>%
summarise(covid_death = sum(covid_death)) %>%
ungroup()
# Complete missing values with 0.
covid_death <-covid_death %>%
complete( sex, date, age_covidgroup, fill = list(covid_death = 0) )
age_covidgroup =
data.frame( age_covidgroup = c("0-24", "25-44", "45-64", "65-74", "75-84", "85+"),
age_min = c(0, 25, 45, 65, 75, 85),
age_max = c(24, 44, 64, 74, 84, age_end) )
age_group = sqldf( "select age_group.*, age_covidgroup
from age_group, age_covidgroup
where age_group.age_min between age_covidgroup.age_min and age_covidgroup.age_max and
age_group.age_max between age_covidgroup.age_min and age_covidgroup.age_max")
excess_death_vs_covid =
death_expected_weekly %>%
merge( age_group, by = c("age_group") ) %>%
merge( covid_death, by = c("sex", "date", "age_covidgroup") )
date_min = min(excess_death_vs_covid$date)
date_max = max(excess_death_vs_covid$date)+6
excess_death_vs_covid %>%
group_by( age_covidgroup ) %>%
summarise( covid_death = sum(covid_death) / sum(death_expected),
excess_death = sum(excess_death) / sum(death_expected)) %>%
melt() %>%
ggplot(aes(x=age_covidgroup , y=value, fill=variable)) +
geom_col(position=position_dodge()) +
scale_y_continuous(labels = scales::percent_format(accuracy = 01), name = "Mortality in % of expected mortality") +
labs( title = sprintf( "COVID-19-attributed mortality vs excess demographic mortality\nin Belgium between %s and %s", date_min, date_max) )
### TEMPERATURES
# https://opendata.meteo.be/geonetwork/srv/eng/catalog.search;jsessionid=B123D8FF9D843F6B8721B8878EB55479#/metadata/BEL/RMI_DATASET_AWS_1DAY
if ( !file.exists("./data/BEL/weather.csv")){
download.file("https://opendata.meteo.be/service/aws/wfs?request=GetFeature&service=WFS&version=1.1.0&typeName=aws:aws_1day&outputFormat=csv",
"./data/BEL/weather.csv")
}
weather <- read_csv("./data/BEL/weather.csv", col_types = cols(FID = col_skip(),
the_geom = col_skip(),
qc_flags = col_skip()))
weather$date <- as.Date( round_date(weather$timestamp) )
weather$code <- as.factor(weather$code)
### DEATH CAUSES
# https://statbel.fgov.be/fr/open-data/BEL/causes-de-deces-par-mois-sexe-groupe-dage-et-region
if ( !file.exists("./data/BEL/opendata_COD_cause.txt")){
download.file("https://statbel.fgov.be/sites/default/files/files/opendata/BEL/COD/opendata_COD_cause.zip", "./data/BEL/opendata_COD_cause.zip")
unzip("opendata_COD_cause.zip", exdir = "./data/BEL")
}
death_cause <- read_delim("data/BEL/opendata_COD_cause.txt",
";", escape_double = FALSE, col_types = cols(CD_RGN_REFNIS = col_skip()),
trim_ws = TRUE)
names(death_cause) <- c("month_of_year", "year", "age_group", "sex", "cause", "death_observed")
death_cause$accidental <- (death_cause$cause == "V01-Y98")
death_cause <- death_cause %>%
group_by( year, month_of_year, age_group, sex, accidental ) %>%
summarise( death_observed = sum(death_observed)) %>%
ungroup()
death_cause$date <- make_date( death_cause$year, death_cause$month_of_year, 1 )
accidental_death <-
death_cause %>%
group_by( year, age_group, sex, accidental ) %>%
summarise( death_observed = sum(death_observed) ) %>%
ungroup() %>%
group_by( year, age_group, sex ) %>%
mutate( percent_accidental_death = death_observed / sum(death_observed), all_death_observed = sum(death_observed) ) %>%
filter( accidental == TRUE ) %>%
select(-accidental) %>%
rename( accidental_death_observed = death_observed )
# Classification of diseases: https://www.who.int/classifications/classification-of-diseases
# Variables: https://statbel.fgov.be/sites/default/files/files/opendata/BEL/COD/OpenDataVerklaring_V1.xlsx
### MODELLING
# Compute a liner regression for each age and sex
mortality_lr_coefficients <- transpose( data.frame( mortality %>%
filter( year >= 2009 ) %>%
group_by(sex, age) %>%
group_map( ~ c( .y$sex, .y$age, summary(lm( mortality ~ year, data = .x ))$coefficients[,1]) )))
names(mortality_lr_coefficients) <- c("sex", "age", "intercept", "year")
mortality_lr_coefficients$intercept <- as.double(mortality_lr_coefficients$intercept)
mortality_lr_coefficients$year <- as.double(mortality_lr_coefficients$year)
# Extend the population_structure dataset with death projections based on the mortality model
death_expected_yearly <- merge( population_structure, mortality_lr_coefficients, by = c("sex", "age") )
names(death_expected_yearly) <- c( "sex", "age", "year", "population_count", "age_group", "intercept", "year_coefficient")
death_expected_yearly$death_expected_raw <- with(death_expected_yearly, population_count * ( intercept + (year * year_coefficient)))
death_expected_yearly <- death_expected_yearly[, c("sex", "age", "year", "death_expected_raw")]
death_expected_yearly <-
merge( death_expected_yearly, age, by = "age") %>%
group_by(sex, age_group, year) %>%
summarise( death_expected_raw = sum(death_expected_raw))
# Merge death projections with real death
death_expected_yearly <- merge(death_expected_yearly, death_yearly, by = c("sex", "age_group", "year") )
# Compute correction factors for the model
model_correction <- death_expected_yearly %>%
filter( year <= 2019 ) %>%
group_by(age_group,sex) %>%
summarise( death_real = sum(death_observed_yearly), death_expected_raw = sum(death_expected_raw)) %>%
ungroup()
model_correction$correction = model_correction$death_real / model_correction$death_expected_raw
model_correction <- model_correction[,c("age_group", "sex", "correction")]
# Apply correction factors
death_expected_yearly <- merge( death_expected_yearly, model_correction, by = c("age_group", "sex") )
death_expected_yearly$death_expected_yearly <- death_expected_yearly$death_expected_raw * death_expected_yearly$correction
death_expected_yearly <- death_expected_yearly %>% select(-death_expected_raw) %>% select(-correction)
# Compute expected number of death per week
death_expected_weekly <- death %>%
merge( death_expected_yearly, by = c("year", "age_group", "sex")) %>%
merge( death_per_week_of_year[,c("week_of_year", "age_group", "week_death_percent", "sex")], by = c("week_of_year", "age_group", "sex"))
death_expected_weekly$death_expected = with(death_expected_weekly, death_expected_yearly * week_death_percent)
death_expected_weekly <- death_expected_weekly %>%
select(-week_death_percent) %>%
select(-death_expected_yearly)%>%
select(-death_observed_yearly)
death_expected_weekly$excess_death <- death_expected_weekly$death_observed - death_expected_weekly$death_expected
# Cumulative excess mortality
cum_death_expected_weekly <- death_expected_weekly %>%
arrange(age_group, sex, date) %>%
group_by( age_group, sex ) %>%
mutate( "cum_excess_deaths" = cumsum(excess_death)) %>%
arrange( age_group, sex, date )
# Graph: cumulative excess mortality
# Table: excess mortality in 2020
excess_death_2020 <-
death_expected_weekly %>%
filter(year==2020) %>%
group_by( age_group,sex ) %>%
summarise(
expected_death = sum(death_expected),
excess_death = sum(excess_death),
death_observed = sum(death_observed),
excess_death_percent = sum(excess_death) / sum(death_expected))
excess_death_2020 <-
merge( excess_death_2020, population_structure_by_age_group %>% filter(year == 2020), by = c( "age_group", "sex" ) ) %>%
select(-year)
excess_death_2020$excess_mortality <- excess_death_2020$excess_death / excess_death_2020$population_count
excess_death_2020$expected_mortality <- excess_death_2020$expected_death / excess_death_2020$population_count
excess_death_2020$observed_mortality <- excess_death_2020$death_observed / excess_death_2020$population_count
# Comparing excess deaths to COVID deaths
compared_covid_death <- merge( covid_death, death_expected_weekly, by = c("date", "age_group", "sex"), all.y = TRUE )
compared_covid_death %>%
summarise( covid_death = sum(covid_death), excess_death = sum(excess_death) )
# Cause of mortality
mortality_cause <- merge( death_cause, population_structure_by_age_group, by = c("year", "age_group", "sex"))
mortality_cause$mortality <- mortality_cause$death_observed / mortality_cause$population_count
# Age group 0-24
cum_death_expected_weekly %>%
filter( age_group == "0-24") %>%
ggplot( aes( x = date, color = sex )) +
geom_line(aes(y = cum_excess_deaths))
# Accidental deaths in age group 0-24 by pear
mortality_cause %>%
filter( age_group == "0-24" ) %>%
group_by( year, accidental, sex ) %>%
summarise( mortality = sum( mortality)) %>%
ggplot( aes( x = year, color = sex, linetype = accidental )) +
geom_line(aes(y = mortality)) +
scale_x_continuous(name = "year", breaks = 2009:2019) +
scale_y_continuous(labels = scales::percent_format(), name = "mortality", limits = c(0,NA))
# Extrapolate accidental death to 2020
accidental_death_lr_coefficients <- transpose( data.frame( accidental_death %>%
group_by(sex, age_group) %>%
group_map( ~ c( .y$sex, .y$age_group, coefficients(lm( percent_accidental_death ~ year, data = .x ))) )))
names(accidental_death_lr_coefficients) <- c("sex", "age_group", "intercept", "year_coefficient")
accidental_death_lr_coefficients$intercept <- as.double(accidental_death_lr_coefficients$intercept)
accidental_death_lr_coefficients$year_coefficient <- as.double(accidental_death_lr_coefficients$year_coefficient)
accidental_death_2020 <-
accidental_death_lr_coefficients %>%
filter( age_group == "0-24" ) %>%
mutate( percent_accidental_death = intercept + 2020 * year_coefficient )
### MORTALITY VARIANCE
mortality_deviation <-
mortality %>%
group_by( year, sex, age_group ) %>%
summarise( mortality = mean(mortality)) %>%
group_by( sex, age_group ) %>%
mutate( mortality_rollmean = rollmedian( mortality, 5, align="center", na.pad=TRUE ))
mortality_deviation$deviation <-
mortality_deviation$mortality - mortality_deviation$mortality_rollmean
mortality_deviation$relative_deviation <-
mortality_deviation$mortality / mortality_deviation$mortality_rollmean - 1