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oxcgrt.jl
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oxcgrt.jl
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### A Pluto.jl notebook ###
# v0.19.31
using Markdown
using InteractiveUtils
# ╔═╡ d3e1eb22-9a63-11ec-27a9-43c3ded20267
using DataFrames, CSV, Downloads, Dates, ShiftedArrays, WorldBankData
# ╔═╡ 45feeff5-5c9d-42e4-ba63-c25ca387fefe
using FixedEffectModels, RegressionTables
# ╔═╡ e5ea68e6-f0ec-4501-a00a-f2e93af30f0e
using Plots
# ╔═╡ 0f14368c-6e71-4cd5-86fa-df85081229b8
using Statistics
# ╔═╡ cabd4851-d3a6-43a4-9655-40b3d4b09eee
md"""
This notebook reproduces and improves some results from [Chisadza et al (2021)](https://www.mdpi.com/2071-1050/13/6/3042/htm).
"""
# ╔═╡ ddd2e9a9-6723-4a88-8603-e54da7575c69
# ╔═╡ cd54e8bc-ecb2-4455-bda0-b89ab41cca30
md"""
# Data
The main source of data is the [Oxford Covid Government Response Tracker (OxCGRT)](https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker).
"""
# ╔═╡ 2130f4e4-45fa-47f6-9412-57f2e9d662d5
ox = let
url = "https://github.com/OxCGRT/covid-policy-dataset/raw/main/data/OxCGRT_compact_national_v1.csv"
CSV.read(Downloads.download(url), DataFrame)
end
# ╔═╡ c845fdde-bb29-4fc3-a744-80002bed5111
describe(ox)
# ╔═╡ 189fde90-f8ad-4a5c-9566-cad610830a26
begin
"""
panellag(x::Symbol, data::AbstractDataFrame, id::Symbol, t::Symbol,
lags::Integer=1)
Create lags of variables in panel data.
# Arguments
- `x` variable to create lag of
- `data` DataFrame containing `x`, `id`, and `t`
- `id` cross-section identifier
- `t` time variable
- `lags` number of lags. Can be negative, in which cause leads will
be created
# Returns
- A vector containing lags of data[x]. Will be missing for `id` and
`t` combinations where the lag is not contained in `data`.
"""
function panellag(x::Symbol, data::AbstractDataFrame, id::Symbol, t::Symbol,
lags::Integer=1)
if (!issorted(data, [id, t]))
@warn "data is not sorted, panellag() will be more efficient with a sorted DataFrame"
p = sortperm(data, [id, t])
df = data[p,:]
else
p = nothing
df = data
end
idlag= ShiftedArrays.lag(df[!,id], lags)
tlag = ShiftedArrays.lag(df[!,t], lags)
xlag = ShiftedArrays.lag(df[!,x], lags)
xlag = copy(xlag)
xlag[ ismissing.(tlag) .|
(tlag .!= df[!,t].-lags) .|
(idlag .!= df[!,id]) ] .= missing
if (p == nothing)
return(xlag)
else
xout = similar(xlag)
xout[p] .= xlag
return(xout)
end
end
function panellag(x::AbstractArray, id::AbstractVector, t::AbstractVector,
lags::Integer=1)
idlag= ShiftedArrays.lag(id, lags)
tlag = ShiftedArrays.lag(t, lags)
xlag = copy(ShiftedArrays.lag(x, lags))
xlag[ ismissing.(tlag) .|
(tlag .!= t.-lags) .|
(idlag .!= id) ] .= missing
return(xlag)
end
end
# ╔═╡ 858035fe-22e5-4a6b-9129-107777496234
md"""
We obtain country GDP, population, Diabetes prevalence, hospital beds, and old age dependency ratio (Chisadza et al (2021) uses median age, but we could not find such a variable) from the World Bank.
"""
# ╔═╡ 075ca6b5-5e09-474a-aecb-9fac1673bca7
wbdf = let
wbvars = Dict(:GDPpc => "NY.GDP.PCAP.CD",
:Diabetes_prevalence => "SH.STA.DIAB.ZS",
:Old_age_DR => "SP.POP.DPND.OL",
:Hospital_bed_per1000 => "SH.MED.BEDS.ZS",
:Population => "SP.POP.TOTL")
inds = [values(wbvars)...]
wbdf = wdi(inds, "all", 2009, 2019)
gwdf = groupby(wbdf, :country)
function foo(x)
cx = collect(skipmissing(x))
if length(cx)==0
return(missing)
else
return(last(cx))
end
end
gwdf
combine(gwdf, Symbol.(replace.(values(wbvars), r"\." => s"_")) .=> foo .=> keys(wbvars))
end
# ╔═╡ ef58ffa9-08a7-4dfa-a9ed-17b0c561da70
gmr = let
#url = "https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv"
#gmr=CSV.read(Downloads.download(url), DataFrame)
#filter(r->ismissing(r.sub_region_1),gmr)
nothing
end;
# ╔═╡ fcb62e62-178b-461c-90e8-dd89494cd2c3
md"""
## Data Preparation
"""
# ╔═╡ 19c54112-1e98-4e08-84e5-03df35f18c5f
ox0 = let
function datefromint(d)
y, r= divrem(d,10000)
return(Date(y, divrem(r, 100)...))
end
ox.date = datefromint.(ox.Date)
df = filter(x->x.Jurisdiction.=="NAT_TOTAL", ox)
df.t = Dates.value.(df.date - Date(2020,1,1))
sort!(df, [:CountryCode, :t])
df.deaths = df.ConfirmedDeaths - panellag(:ConfirmedDeaths, df, :CountryCode, :t)
df.cases = df.ConfirmedCases - panellag(:ConfirmedCases, df, :CountryCode, :t)
df.deathslag = panellag(:deaths, df, :CountryCode, :t)
leftjoin(df, wbdf, on = :CountryName => :country)
end;
# ╔═╡ fff4b60f-530e-428f-9847-c1b820ab8195
names(ox)
# ╔═╡ c56f41a6-e62f-4399-a34e-732f01520667
ox1 = let
ox1 = deepcopy(ox0)
sort!(ox1,[:CountryCode,:t])
ox1.deaths = (ox1.ConfirmedDeaths - panellag(:ConfirmedDeaths, ox1, :CountryCode, :t, 1))./1
ox1.cases = (ox1.ConfirmedCases - panellag(:ConfirmedCases, ox1, :CountryCode, :t, 1))./1
ox1.deaths = max.(ox1.deaths,0)
ox1.cases = max.(ox1.cases,0)
ox1.casesp100k = ox1.cases./(ox1.Population/1e5)
ox1.deathspm = ox1.deaths./(ox1.Population/1e6)
ox1.deathspmlag = panellag(:deathspm, ox1, :CountryCode, :t, 1)
ox1.indexlag21 = panellag(:StringencyIndex_Average, ox1, :CountryCode, :t, 21)
ox1.deathslag21 = panellag(:deathspm, ox1, :CountryCode, :t, 21)
ox1.casesp100klag21 = panellag(:casesp100k, ox1, :CountryCode, :t, 21)
ox1.logdeathspm = log.(ox1.deathspm.+1e-6)
ox1.deathpmgrowth = log.(ox1.deathspm.+1e-6) - log.(panellag(:deathspm, ox1, :CountryCode, :t, 1).+1e-6) #./
ox1.deathgrowth = log.(ox1.deaths.+1) - log.(panellag(:deaths, ox1, :CountryCode, :t, 1).+1)
#(panellag(:deaths, ox1, :CountryCode, :t, 7) .+ 1)
#ox1.deathgrowth = min.(ox1.deathgrowth,10.0)
ox1 = filter(x->((x.date.>=Date(2020,03,1)) .& (x.date .< Date(2020,10,1))), ox1)
#ox1 = leftjoin(ox1, gmr, on=[:CountryName=>:country_region,:date=>:date])
end;
# ╔═╡ 5b0ca264-3bfd-403b-881f-fe67f264d8bd
describe(ox1)
# ╔═╡ c7664c08-a426-450d-b455-4da3ccb9c829
md"""
# Analysis
## Chisadza results
The table below shows two specifications similar to Table 1 of Chisadza et al (2021). Column (1) regresses daily deaths per million on current stringency index, one-day lagged deaths per million, and controls. Unlike Chisadza et al (2021), we estimate a linear regression instead of a Poisson regression. Column (2) is similar, but with log deaths per million. In both columns, we see that a higher current stringency index is associated with more current deaths.
"""
# ╔═╡ c3aad65d-198a-48ff-996d-b5ed3268a522
let
chisadza_lm = reg(ox1, @formula(deathspm ~ StringencyIndex_Average + deathspmlag +
log(GDPpc) + Diabetes_prevalence + Hospital_bed_per1000 + Old_age_DR
), Vcov.cluster(:CountryCode))
#chisadza_iid = reg(ox1, @formula(deathspm ~ StringencyIndex + deathspmlag +
# log(GDPpc) + Diabetes_prevalence + Hospital_bed_per1000 + Old_age_DR
#))
chisadza_log = reg(ox1, @formula(logdeathspm ~ StringencyIndex_Average + log(deathspmlag+1e-6) +
log(GDPpc) + Diabetes_prevalence + Hospital_bed_per1000 + Old_age_DR
), Vcov.cluster(:CountryCode))
regtable(chisadza_lm, chisadza_log,
renderSettings=latexOutput("chisadza.tex"),estimformat="%.3g")
io = IOBuffer()
regtable(chisadza_lm, chisadza_log, renderSettings=htmlOutput(), out_buffer=io,
estimformat="%.3g");
table=HTML(String(take!(io)));
end
# ╔═╡ 15103dcc-7e04-408c-9d27-d4643f3b4a2a
# ╔═╡ 886dc221-4a45-4454-9062-a0adfd81d519
md"""
The table below shows results when we allow for a 21 day lag between policy changes and deaths. Column (1) has deaths per million as the dependent variable. Column (2) has log deaths per million as the dependent variable. These columns show a positive association between stringency and deaths. However, the specification in these columns is not well-motivated by epidemiology. Basic epidemiological models imply that changes in the contact rate between people should affect the **growth rate** of disease, not the level.
Columns (4)-(6) have the daily change in log deaths (which is approximately the growth rate of deaths) as the dependent variable. Here, we find a negative association between stringency and death growth. The magnitude of the coefficient is substantial. The average death growth rate is 0.0059. Thus, the estimates imply that going from a stringency of 0 to 100 would almost completely eliminate the growth in deaths.
"""
# ╔═╡ 022eebe9-e173-443e-a48e-e0c5845337bc
begin
lag = reg(ox1, @formula(deathspm ~ indexlag21 + deathslag21 +
log(GDPpc) + Diabetes_prevalence + Hospital_bed_per1000 + Old_age_DR
), Vcov.cluster(:CountryCode))
laglog2 = reg(ox1, @formula(logdeathspm ~ indexlag21 + log(deathslag21+1e-6) +
log(casesp100klag21+1e-6) + casesp100klag21 + deathslag21 +
log(GDPpc) + Diabetes_prevalence + Hospital_bed_per1000 + Old_age_DR
), Vcov.cluster(:CountryCode))
laglog = reg(ox1, @formula(logdeathspm ~ indexlag21 + log(deathslag21+1e-6) +
log(GDPpc) + Diabetes_prevalence + Hospital_bed_per1000 + Old_age_DR
), Vcov.cluster(:CountryCode))
growth = reg(ox1, @formula(deathgrowth ~ indexlag21 + deathslag21 +
log(GDPpc) + Diabetes_prevalence + Hospital_bed_per1000 + Old_age_DR
), Vcov.cluster(:CountryCode), save=:residuals)
growth2 = reg(ox1, @formula(deathgrowth ~ indexlag21 + deathslag21 + log(deathslag21+1) +
log(GDPpc) + Diabetes_prevalence + Hospital_bed_per1000 + Old_age_DR
), Vcov.cluster(:CountryCode))
growth3 = reg(ox1, @formula(deathgrowth ~ indexlag21 + deathslag21 + log(deathslag21+1) + casesp100klag21 + log(casesp100klag21 + 1e-6) +
log(GDPpc) + Diabetes_prevalence + Hospital_bed_per1000 + Old_age_DR
), Vcov.cluster(:CountryCode))
regtable(lag, laglog, laglog2, growth,growth2, growth3,
renderSettings=latexOutput("lag.tex"), estimformat="%.3g")
io = IOBuffer()
regtable(lag, laglog, laglog2, growth,growth2, growth3, renderSettings=htmlOutput(), out_buffer=io, estimformat="%.3g")
table=HTML(String(take!(io)))
end
# ╔═╡ 4c425c58-6642-4fc5-a536-54c48eebb288
md"""
# Simulating the Impact of Changing the Stringency Index
"""
# ╔═╡ 3900cd1d-9e2a-43d6-90b1-59df295e1317
coefnames(growth)
# ╔═╡ 88b228be-7743-4ef7-ae1b-b833fd19ca64
md"""
In this section, we use the estimates from column (4) above to calculate the impact of changing the stringency impact.
The estimates imply that with 0 stringency and 0 lagged deaths, the median predicted change in log deaths (or approximate growth rate of deaths) is 0.026.
"""
# ╔═╡ 06b3e2a7-251c-4877-8f85-34ab79952afa
xb = let
ox2 = deepcopy(ox1)
ox2.indexlag21 .= 0
ox2.deathslag21 .= 0
xb=predict(growth, ox2)
median(skipmissing(xb))
end
# ╔═╡ 7e91dca5-61b1-4bf3-a9db-34397cdda0c7
function simulateImpact(β, stringency, initialdeath, xb, ϵ; lag=21, model=growth)
length(initialdeath)==lag || error("requires $lag initial values for deaths")
deaths = similar(initialdeath, length(stringency))
deaths[1:length(initialdeath)] .= initialdeath
growd = similar(deaths)
growd .= 0
@show β_deathlag = β[coefnames(model).=="deathslag$lag"][1]
@show β_indexlag = β[coefnames(model).=="indexlag$lag"][1]
for t=(lag+1):length(stringency)
deathlag = deaths[t-lag]
indexlag = stringency[t-lag]
dg = xb + deathlag * β_deathlag + indexlag*β_indexlag + rand(ϵ)
growd[t] = dg
deaths[t] = exp(log(deaths[t-1] + 1) + dg) - 1
end
return((deaths,growd))
end
# ╔═╡ f260d9e0-644a-49d0-8c77-66fe1eb85a46
md"""
Now, we consider beginning with a stringency index of 44 and changing it to 72 after 50 days. We do this for a country with a median value of the covariates. That, is the country would have death growth of 0.026 with no stringency, and approximately 0.013 if stringency remained at 44 forever (ignoring the effect of lagged deaths in the model).
"""
# ╔═╡ cdd36998-f9dd-4fb4-b70e-fc852ca9f895
stringency = let
stringency = ones(200)*44
stringency[50:end] .= 72
stringency
end
# ╔═╡ 7d4400f3-1a9e-4da2-8355-119317e65200
md"""
If stringency remains at 72 forever, the estimates imply the death growth rate is 0.00034. This is very low, so we will see in the figures below that deaths are approximately constant after stringency increases to 72.
"""
# ╔═╡ e42f871d-6f82-4c53-be21-87565bbe0507
xb + coef(growth)[coefnames(growth).=="indexlag21"][1]*72
# ╔═╡ 7d408f52-ae97-443d-81c7-102e03c023c1
initialdeaths = ones(21)
# ╔═╡ fc312502-8a2f-474e-9d18-4bd0ed9aa457
date=sort(unique(ox1.date))[1:200]
# ╔═╡ 8e6c46d0-8e6a-405c-899a-537c2a42ead9
deaths,dg=simulateImpact(coef(growth), stringency, initialdeaths, xb, zeros(2))
# ╔═╡ 2e1d7b5a-d135-4398-8570-3c6fcc9604b4
baselinedeaths,bdg=simulateImpact(coef(growth), ones(200)*44, initialdeaths, xb, zeros(2));
# ╔═╡ cec19d5b-21a4-4422-8bbd-c8871eaf80dc
md"""
We now plot the path of deaths and death growth. Since the model involves lags of 21 days, we must initialize the first 21 days to something. We set deaths to 1 and death growth to 0 for the first 21 days.
"""
# ╔═╡ 0f489086-d940-4e3e-9a23-c0d9f73199ce
let
T = 100
Plots.plot(
#Plots.plot(date[1:T], stringency[1:T], legend=:none),
Plots.plot(date[1:T],hcat(baselinedeaths[1:T], deaths[1:T]),
labels=["Stringency=44" "Stringency=72 after 2020-04-19"],
ylabel="Deaths"),
Plots.plot(date[1:T],hcat(bdg[1:T], dg[1:T]),
labels=["Stringency=44" "Stringency=72 after 2020-04-19"],
ylabel="Death growth"),
layout=(2,1)
)
end
# ╔═╡ 20edcc41-eec3-4795-ac9f-01b66007421a
let
T = 200
Plots.plot(
#Plots.plot(date[1:T], stringency[1:T], legend=:none),
Plots.plot(date[1:T],hcat(baselinedeaths[1:T], deaths[1:T]),
labels=["Stringency=44" "Stringency=72 after 2020-04-19"],
ylabel="Deaths"),
Plots.plot(date[1:T],hcat(bdg[1:T], dg[1:T]),
labels=["Stringency=44" "Stringency=72 after 2020-04-19"],
ylabel="Death growth"),
layout=(2,1)
)
end
# ╔═╡ 4e3f3ecd-9741-42fc-b14d-a86e0b8c27aa
xb + coef(growth)[2]*72
# ╔═╡ 50a4bb46-1982-4faf-aaab-33bfbaff19e9
coef(growth)
# ╔═╡ eada015e-164f-4306-ae36-1b880d87ef64
md"""
## Calculating some descriptive statistics for 2022
This is looks at how mean stringency evolved in the US during 2022.
"""
# ╔═╡ 999c8821-ac3b-4247-a8fa-2ddecdac94c4
# ╔═╡ da6898e0-60d4-4d14-a582-6eb499895f51
ox0.deathgrowth = log.(max.(ox0.deaths,0).+1) - log.(max.(ox0.deathslag,0) .+ 1)
# ╔═╡ 204a60fb-9e42-4f11-8d67-0737132b584e
ind = (ox0.date.>=Dates.Date(2022,05,01)) .&
(ox0.date.<=Dates.Date(2022,11,01)) .& (ox0.CountryCode .== "USA")
# ╔═╡ 64dad17a-d39c-440d-b2ba-492b35e566df
mean(skipmissing(ox0.deathgrowth[ind]))
# ╔═╡ a1e9249d-e96a-4655-926b-1c712a56bd52
Plots.plot(ox0.date[ind], ox0.StringencyIndex_Average[ind])
# ╔═╡ 00000000-0000-0000-0000-000000000001
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