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mpx_projections.jl
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mpx_projections.jl
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using Distributions, StatsBase, StatsPlots
using Plots.PlotMeasures, CSV, DataFrames
using LinearAlgebra, RecursiveArrayTools, Dates
using ProgressMeter
using OrdinaryDiffEq, ApproxBayes, MCMCChains
using JLD2
using MpoxUK
## MSM data with data inference
past_mpxv_data_inferred =
CSV.File("data/weekly_data_imputation_2022-09-30.csv", missingstring = "NA") |>
DataFrame
wks = Date.(past_mpxv_data_inferred.week, DateFormat("dd/mm/yyyy"))
include("setup_model.jl");
## Format case data into expected (over GBDT inference) case incidence
colname = "seqn_fit5"
inferred_prop_na_msm = past_mpxv_data_inferred[:, colname] |> x -> x[.~ismissing.(x)]
inferred_prop_na_msm_lwr =
past_mpxv_data_inferred[:, "lower_"*colname] |> x -> x[.~ismissing.(x)]
inferred_prop_na_msm_upr =
past_mpxv_data_inferred[:, "upper_"*colname] |> x -> x[.~ismissing.(x)]
mpxv_wkly =
past_mpxv_data_inferred[1:size(inferred_prop_na_msm, 1), ["gbmsm", "nongbmsm"]] .+
past_mpxv_data_inferred[1:size(inferred_prop_na_msm, 1), "na_gbmsm"] .*
hcat(inferred_prop_na_msm, 1.0 .- inferred_prop_na_msm) |> Matrix
lwr_mpxv_wkly =
past_mpxv_data_inferred[1:size(inferred_prop_na_msm, 1), ["gbmsm", "nongbmsm"]] .+
past_mpxv_data_inferred[1:size(inferred_prop_na_msm, 1), "na_gbmsm"] .*
hcat(inferred_prop_na_msm_lwr, 1.0 .- inferred_prop_na_msm_lwr) |> Matrix
upr_mpxv_wkly =
past_mpxv_data_inferred[1:size(inferred_prop_na_msm, 1), ["gbmsm", "nongbmsm"]] .+
past_mpxv_data_inferred[1:size(inferred_prop_na_msm, 1), "na_gbmsm"] .*
hcat(inferred_prop_na_msm_upr, 1.0 .- inferred_prop_na_msm_upr) |> Matrix
wks = Date.(past_mpxv_data_inferred.week[1:size(mpxv_wkly, 1)], DateFormat("dd/mm/yyyy"))
ts = wks .|> d -> d - Date(2021, 12, 31) .|> t -> t.value
wkly_vaccinations = [
[zeros(12); 1000; 2000; fill(5000, 4)] * 1.675
fill(650, 18)
]
print("cum. vacs = $(sum(wkly_vaccinations))")
## Load posterior draws and saved trajectiories
date_str = "2022-09-26"
description_str = "no_ngbmsm_chg" #<---- This is the main model
# description_str = "no_bv_cng" #<---- This is the version of the model with no behavioural change
# description_str = "one_metapop" #<--- This is the version of the model with no metapopulation structure
# description_str = "" #<--- this is the older version main model
param_draws =
load("posteriors/posterior_param_draws_" * date_str * description_str * ".jld2")["param_draws"]
detected_cases =
load("posteriors/posterior_detected_cases_" * date_str * description_str * ".jld2")["detected_cases"]
vac_effs = load(
"posteriors/posterior_vac_effectivenesses_" * date_str * description_str * ".jld2",
)["vac_effectivenesses"]
no_vac_states =
load("posteriors/posterior_begin_vac_states_" * date_str * description_str * ".jld2")["begin_vac_states"]
sept_states =
load("posteriors/posterior_begin_sept_states_" * date_str * description_str * ".jld2")["begin_sept_states"]
end_states =
load("posteriors/posterior_end_states_" * date_str * description_str * ".jld2")["end_states"]
incidences =
load("posteriors/posterior_incidences_" * date_str * description_str * ".jld2")["incidences"]
sus_mats =
load("posteriors/posterior_susceptibilities_" * date_str * description_str * ".jld2")["susceptibilities"]
param_draws_no_bv_cng = map(θ -> vcat(θ[1:(end-4)], zeros(4)), deepcopy(param_draws))
## Public health emergency effect forecasts
n_lookaheadweeks = 26
long_wks = [wks; [wks[end] + Day(7 * k) for k = 1:n_lookaheadweeks]]
long_mpxv_wkly = [mpxv_wkly; zeros(n_lookaheadweeks, 2)]
wkly_vaccinations = [wkly_vaccinations; fill(0, 52)]
wkly_vaccinations_ceased = [copy(wkly_vaccinations)[1:length(wks)+1]; fill(0, 52)]
plt_vacs = plot(
[wks[1] + Day(7 * (k - 1)) for k = 1:size(long_wks, 1)],
cumsum(wkly_vaccinations)[1:size(long_mpxv_wkly, 1)],
title = "Cumulative number of MPX vaccine doses",
lab = "Projection",
color = :black,
lw = 3,# yticks=0:1000:8000,
ylabel = "Cum. doses",
xticks = (
[Date(2022, 5, 1) + Month(k) for k = 0:12],
[monthname(Date(2022, 5, 1) + Month(k))[1:3] for k = 0:12],
),
yticks = (0:10_000:50_000, string.(0:10_000:50_000)),
size = (800, 600),
left_margin = 5mm,
guidefont = 16,
tickfont = 13,
titlefont = 18,
legendfont = 16,
legend = :right,
right_margin = 5mm,
)
scatter!(
plt_vacs,
[Date(2022, 7, 1), Date(2022, 8, 30), Date(2022, 9, 22)],
[0, 38_079, 40_426],
lab = "UKHSA reported vaccines",
ms = 8,
)
display(plt_vacs)
savefig(plt_vacs, "plots/vaccine_rollout.png")
savefig(plt_vacs, "plots/vaccine_rollout.pdf")
vaccine_projections = DataFrame(
"Date" => [wks[1] + Day(7 * (k - 1)) for k = 1:size(long_wks, 1)],
"Projected cumulative vac doses" =>
cumsum(wkly_vaccinations)[1:size(long_mpxv_wkly, 1)],
"Cumulative vac doses (poor scenario)" =>
cumsum(wkly_vaccinations_ceased)[1:size(long_wks, 1)],
)
CSV.write("projections/vaccine_rollout.csv", vaccine_projections)
## Define counterfactuals and projections
d1, d2 = size(mpxv_wkly)
n_samples = 2000
n_wks_lookahead = 26
f_rev = findfirst(wks .== Date(2022, 8, 29))
f_novac = findfirst(wks .== Date(2022, 7, 25))
wks_proj_fromend = [wks[end] + Day(7k) for k = 1:n_wks_lookahead]
all_wks = [wks; wks_proj_fromend]
wks_reversion = [wks[f_rev] + Day(7k) for k = 1:n_lookaheadweeks+4]
wks_no_vacs = [wks[f_novac] + Day(7k) for k = 1:n_lookaheadweeks+9]
# Checks
println(
"Projection from end and projection from first week in september have same end data is ",
wks_proj_fromend[end] == wks_reversion[end],
)
println(
"Projection from end and projection from last week with no vaccines have same end data is ",
wks_proj_fromend[end] == wks_no_vacs[end],
)
# Make a set of projections
proj_fromend = [
(
ts = wks_proj_fromend .|> d -> (d - Date(2021, 12, 31)).value |> Float64,
wkly_vaccinations = wkly_vaccinations[(d1+1):end],
vac_effectiveness = vac_effs[k],
) for k = 1:n_samples
]
proj_frombeginning_no_mitigation = [
(
ts = all_wks .|> d -> (d - Date(2021, 12, 31)).value |> Float64,
wkly_vaccinations = zeros(100),
vac_effectiveness = vac_effs[k],
) for k = 1:n_samples
]
proj_reversion = [
(
ts = wks_reversion .|> d -> (d - Date(2021, 12, 31)).value |> Float64,
wkly_vaccinations = wkly_vaccinations[((d1-3)+1):end],
vac_effectiveness = vac_effs[k],
) for k = 1:n_samples
]
proj_no_vaccines = [
(
ts = wks_no_vacs .|> d -> (d - Date(2021, 12, 31)).value |> Float64,
wkly_vaccinations = zeros(100),
vac_effectiveness = vac_effs[k],
) for k = 1:n_samples
]
proj_uniform_vaccines = [
(
ts = wks_no_vacs .|> d -> (d - Date(2021, 12, 31)).value |> Float64,
wkly_vaccinations = wkly_vaccinations[13:end],
vac_effectiveness = vac_effs[k],
) for k = 1:n_samples
]
projections_from_end = @showprogress 0.1 "MPX Forecasts:" map(
(θ, interventions, state) ->
mpx_sim_function_projections(θ, constants, interventions, state),
param_draws,
proj_fromend,
end_states,
)
projections_no_mitigation = @showprogress 0.1 "MPX Forecasts (no mitigation scenario):" map(
θ -> mpx_sim_function_chp(θ, constants_no_vaccines, zeros(size(all_wks, 1), 2))[2],
param_draws_no_bv_cng,
)
projections_reversions_4wk_rev = @showprogress 0.1 "MPX 4 week reversion:" map(
(θ, interventions, state) ->
mpx_sim_function_projections(θ, constants, interventions, state, 4),
param_draws,
proj_reversion,
sept_states,
)
projections_reversions_12wk_rev = @showprogress 0.1 "MPX 12 week reversion:" map(
(θ, interventions, state) ->
mpx_sim_function_projections(θ, constants, interventions, state, 12),
param_draws,
proj_reversion,
sept_states,
)
projections_reversions_4wk_rev_no_vac =
@showprogress 0.1 "MPX 4 week reversion (no vaccines):" map(
(θ, interventions, state) ->
mpx_sim_function_projections(θ, constants, interventions, state, 4),
param_draws,
proj_no_vaccines,
no_vac_states,
)
projections_reversions_4wk_rev_uniform_vac =
@showprogress 0.1 "MPX 4 week reversion (uniform vaccines):" map(
(θ, interventions, state) ->
mpx_sim_function_projections_uniform_vaccination(θ, constants, interventions, state, 4),
param_draws,
proj_uniform_vaccines,
no_vac_states,
)
projections_reversions_12wk_rev_uniform_vac =
@showprogress 0.1 "MPX 12 week reversion (uniform vaccines):" map(
(θ, interventions, state) ->
mpx_sim_function_projections_uniform_vaccination(θ, constants, interventions, state, 12),
param_draws,
proj_uniform_vaccines,
no_vac_states,
)
projections_reversions_12wk_rev_no_vac =
@showprogress 0.1 "MPX 12 week reversion (no vaccines):" map(
(θ, interventions, state) ->
mpx_sim_function_projections(θ, constants, interventions, state, 12),
param_draws,
proj_no_vaccines,
no_vac_states,
)
## Quantiles of projections
cred_int = MpoxUK.cred_intervals(detected_cases, central_est = :median)
cred_unmitigated = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_no_mitigation],
central_est = :median,
)
cred_proj = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_from_end],
central_est = :median,
)
cred_int_12wk = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_12wk_rev],
central_est = :median,
)
cred_int_12wk_no_vacs = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_12wk_rev_no_vac],
central_est = :median,
)
cred_int_4wk_uniform_vacs = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_4wk_rev_uniform_vac],
central_est = :median,
)
cred_int_12wk_uniform_vacs = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_12wk_rev_uniform_vac],
central_est = :median,
)
cred_int_4wk = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_4wk_rev],
central_est = :median,
)
cred_int_4wk_no_vacs = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_4wk_rev_no_vac],
central_est = :median,
)
cum_cred_int =
MpoxUK.cred_intervals(cumsum(detected_cases, dims = 1), central_est = :median)
cum_cred_int_12wk = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_12wk_rev] .|> x -> cumsum(x, dims = 1),
central_est = :median,
)
cum_cred_unmitigated = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_no_mitigation] .|> x -> cumsum(x, dims = 1),
central_est = :median,
)
cum_cred_unmitigated_all_infections = MpoxUK.matrix_cred_intervals(
[sum(proj.detected_cases,dims = 2) for proj in projections_no_mitigation] .|> x -> cumsum(x, dims = 1),
central_est = :median,
)
cum_cred_int_12wk_no_vacs = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_12wk_rev_no_vac] .|> x -> cumsum(x, dims = 1),
central_est = :median,
)
cum_cred_int_4wk = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_4wk_rev] .|> x -> cumsum(x, dims = 1),
central_est = :median,
)
cum_cred_int_4wk_no_vacs = MpoxUK.cred_intervals(
[proj.detected_cases for proj in projections_reversions_4wk_rev_no_vac] .|> x -> cumsum(x, dims = 1),
central_est = :median,
)
cum_cred_infs =
MpoxUK.matrix_cred_intervals(cumsum.(incidences, dims = 1), central_est = :median)
gbmsm_total_infs = [sum(inc[:,1:10], dims = 2) for inc in incidences]
nongbmsm_total_infs = [inc[:,[11]] for inc in incidences]
cum_cred_total_gbmsm_infs =
MpoxUK.matrix_cred_intervals(cumsum.(gbmsm_total_infs, dims = 1)./N_msm, central_est = :median)
cum_cred_total_nongbmsm_infs =
MpoxUK.matrix_cred_intervals(cumsum.(nongbmsm_total_infs, dims = 1)./(N_uk-N_msm), central_est = :median)
## Output quantities reported in paper to LaTeX
median_pred_total_gbmsm = cum_cred_total_gbmsm_infs.median_pred[end] * 100 |> x -> round(x, sigdigits = 3)
qs_pred_total_gbmsm = ((cum_cred_total_gbmsm_infs.median_pred[end] - cum_cred_total_gbmsm_infs.lb_pred_025[end]), cum_cred_total_gbmsm_infs.median_pred[end] + cum_cred_total_gbmsm_infs.ub_pred_025[end]) .|> x -> round(x * 100, sigdigits = 3)
median_pred_total_nongbmsm = cum_cred_total_nongbmsm_infs.median_pred[end] * 100 |> x -> round(x, sigdigits = 3)
qs_pred_total_nongbmsm = ((cum_cred_total_nongbmsm_infs.median_pred[end] - cum_cred_total_nongbmsm_infs.lb_pred_025[end]), cum_cred_total_nongbmsm_infs.median_pred[end] + cum_cred_total_nongbmsm_infs.ub_pred_025[end]) .|> x -> round(x * 100, sigdigits = 3)
case_output = ""
case_output = case_output * raw"\newcommand{\totalgbmsminf}{" * "$(median_pred_total_gbmsm)" * raw"\%" * " ($(qs_pred_total_gbmsm[1]) -- $(qs_pred_total_gbmsm[2])}\n"
case_output = case_output * raw"\newcommand{\totalnongbmsminf}{" * "$(median_pred_total_nongbmsm)" * raw"\%" * " ($(qs_pred_total_nongbmsm[1]) -- $(qs_pred_total_nongbmsm[2])}\n"
## MSM projections
plt_msm = plot(;
ylabel = "Weekly cases",
title = "UK Monkeypox Case Projections (GBMSM)",
legend = :topright,
# ylims = (-5, 700),
xticks = (
[Date(2022, 5, 1) + Month(k) for k = 0:11],
[monthname(Date(2022, 5, 1) + Month(k))[1:3] for k = 0:11],
),
left_margin = 10mm,
top_margin = 5mm,
size = (800, 600),
dpi = 250,
tickfont = 13,
titlefont = 22,
guidefont = 18,
legendfont = 11,
)
plot!(
plt_msm,
wks[3:size(cred_int.median_pred, 1)+2],
cred_int.median_pred[:, 1],
ribbon = (cred_int.lb_pred_25[:, 1], cred_int.ub_pred_25[:, 1]),
lw = 3,
color = :black,
fillalpha = 0.15,
lab = "Fitted trajectory",
)
plot!(
plt_msm,
wks[3:size(cred_int.median_pred, 1)+2],
cred_int.median_pred[:, 1],
ribbon = (cred_int.lb_pred_10[:, 1], cred_int.ub_pred_10[:, 1]),
lw = 0,
color = :black,
fillalpha = 0.15,
lab = "",
)
plot!(
plt_msm,
wks[3:size(cred_int.median_pred, 1)+2],
cred_int.median_pred[:, 1],
ribbon = (cred_int.lb_pred_025[:, 1], cred_int.ub_pred_025[:, 1]),
lw = 0,
color = :black,
fillalpha = 0.15,
lab = "",
)
plot!(
plt_msm,
wks_reversion,
cred_int_12wk.median_pred[:, 1],
ribbon = (cred_int_12wk.lb_pred_25[:, 1], cred_int_12wk.ub_pred_25[:, 1]),
lw = 3,
color = :blue,
fillalpha = 0.15,
lab = "12 week reversion",
)
plot!(
plt_msm,
wks_reversion,
cred_int_12wk.median_pred[:, 1],
ribbon = (cred_int_12wk.lb_pred_10[:, 1], cred_int_12wk.ub_pred_10[:, 1]),
lw = 0,
color = :blue,
fillalpha = 0.15,
lab = "",
)
plot!(
plt_msm,
wks_reversion,
cred_int_12wk.median_pred[:, 1],
ribbon = (cred_int_12wk.lb_pred_025[:, 1], cred_int_12wk.ub_pred_025[:, 1]),
lw = 0,
color = :blue,
fillalpha = 0.15,
lab = "",
)
plot!(
plt_msm,
wks_no_vacs,
cred_int_12wk_no_vacs.median_pred[:, 1],
# ribbon = (cred_int_12wk.lb_pred_25[:, 1], cred_int_12wk.ub_pred_25[:, 1]),
lw = 3,
color = :blue,
ls = :dot,
fillalpha = 0.15,
lab = "12 week reversion (no vaccines)",
)
plot!(
plt_msm,
wks_reversion,
cred_int_4wk.median_pred[:, 1],
ribbon = (cred_int_4wk.lb_pred_25[:, 1], cred_int_4wk.ub_pred_25[:, 1]),
lw = 3,
color = :red,
fillalpha = 0.15,
lab = "4 week reversion",
)
plot!(
plt_msm,
wks_reversion,
cred_int_4wk.median_pred[:, 1],
ribbon = (cred_int_4wk.lb_pred_10[:, 1], cred_int_4wk.ub_pred_10[:, 1]),
lw = 0,
color = :red,
fillalpha = 0.15,
lab = "",
)
plot!(
plt_msm,
wks_reversion,
cred_int_4wk.median_pred[:, 1],
ribbon = (cred_int_4wk.lb_pred_025[:, 1], cred_int_4wk.ub_pred_025[:, 1]),
lw = 0,
color = :red,
fillalpha = 0.15,
lab = "",
)
plot!(
plt_msm,
wks_no_vacs,
cred_int_4wk_no_vacs.median_pred[:, 1],
lw = 3,
color = :red,
ls = :dot,
fillalpha = 0.15,
lab = "4 week reversion (no vaccines)",
)
scatter!(
plt_msm,
wks[[1, 2, size(wks, 1)]],
mpxv_wkly[[1, 2, size(wks, 1)], 1],
lab = "",
ms = 6,
markercolor = :black,
color = :black,
alpha = 1,
markerstrokewidth = 0,
lw = 4,
shape = :square,
)
scatter!(
plt_msm,
wks[3:(end-1)],
mpxv_wkly[3:(end-1), 1],
yerrors = (
mpxv_wkly[3:(end-1), 1] .- lwr_mpxv_wkly[3:(end-1), 1],
upr_mpxv_wkly[3:(end-1), 1] .- mpxv_wkly[3:(end-1), 1],
),
lab = "Data",
ms = 6,
markercolor = :black,
color = :black,
alpha = 1,
markerstrokewidth = 0,
lw = 4,
)
display(plt_msm)
##
plt_nmsm = plot(;
ylabel = "Weekly cases",
title = "UK Monkeypox Case Projections (non-GBMSM)",
legend = :topright,
ylims = (-1, 100),
xticks = (
[Date(2022, 5, 1) + Month(k) for k = 0:11],
[monthname(Date(2022, 5, 1) + Month(k))[1:3] for k = 0:11],
),
left_margin = 5mm,
size = (800, 600),
dpi = 250,
tickfont = 13,
titlefont = 20,
guidefont = 18,
legendfont = 11,
)
plot!(
plt_nmsm,
wks[3:size(cred_int.median_pred, 1)+2],
cred_int.median_pred[:, 2],
ribbon = (cred_int.lb_pred_25[:, 2], cred_int.ub_pred_25[:, 2]),
lw = 3,
color = :black,
fillalpha = 0.2,
lab = "Fitted trajectory",
)
plot!(
plt_nmsm,
wks[3:size(cred_int.median_pred, 1)+2],
cred_int.median_pred[:, 2],
ribbon = (cred_int.lb_pred_10[:, 2], cred_int.ub_pred_10[:, 2]),
lw = 0,
color = :black,
fillalpha = 0.2,
lab = "",
)
plot!(
plt_nmsm,
wks[3:size(cred_int.median_pred, 1)+2],
cred_int.median_pred[:, 2],
ribbon = (cred_int.lb_pred_025[:, 2], cred_int.ub_pred_025[:, 2]),
lw = 0,
color = :black,
fillalpha = 0.2,
lab = "",
)
plot!(
plt_nmsm,
wks_reversion,
cred_int_12wk.median_pred[:, 2],
ribbon = (cred_int_12wk.lb_pred_25[:, 2], cred_int_12wk.ub_pred_25[:, 2]),
lw = 3,
color = :blue,
fillalpha = 0.2,
lab = "12 week reversion",
)
plot!(
plt_nmsm,
wks_reversion,
cred_int_12wk.median_pred[:, 2],
ribbon = (cred_int_12wk.lb_pred_10[:, 2], cred_int_12wk.ub_pred_10[:, 2]),
lw = 0,
color = :blue,
fillalpha = 0.2,
lab = "",
)
plot!(
plt_nmsm,
wks_reversion,
cred_int_12wk.median_pred[:, 2],
ribbon = (cred_int_12wk.lb_pred_025[:, 2], cred_int_12wk.ub_pred_025[:, 2]),
lw = 0,
color = :blue,
fillalpha = 0.2,
lab = "",
)
plot!(
plt_nmsm,
wks_no_vacs,
cred_int_12wk_no_vacs.median_pred[:, 2],
# ribbon = (cred_int_12wk.lb_pred_25[:, 2], cred_int_12wk.ub_pred_25[:, 2]),
ls = :dot,
lw = 3,
color = :blue,
fillalpha = 0.2,
lab = "12 week reversion (no vaccines)",
)
plot!(
plt_nmsm,
wks_reversion,
cred_int_4wk.median_pred[:, 2],
ribbon = (cred_int_4wk.lb_pred_25[:, 2], cred_int_4wk.ub_pred_25[:, 2]),
lw = 3,
color = :red,
fillalpha = 0.2,
lab = "4 week reversion",
)
plot!(
plt_nmsm,
wks_reversion,
cred_int_4wk.median_pred[:, 2],
ribbon = (cred_int_4wk.lb_pred_10[:, 2], cred_int_4wk.ub_pred_10[:, 2]),
lw = 0,
color = :red,
fillalpha = 0.2,
lab = "",
)
plot!(
plt_nmsm,
wks_reversion,
cred_int_4wk.median_pred[:, 2],
ribbon = (cred_int_4wk.lb_pred_025[:, 2], cred_int_4wk.ub_pred_025[:, 2]),
lw = 0,
color = :red,
fillalpha = 0.2,
lab = "",
)
plot!(
plt_nmsm,
wks_no_vacs,
cred_int_4wk_no_vacs.median_pred[:, 2],
# ribbon = (cred_int_12wk.lb_pred_25[:, 2], cred_int_12wk.ub_pred_25[:, 2]),
ls = :dot,
lw = 3,
color = :red,
fillalpha = 0.2,
lab = "4 week reversion (no vaccines)",
)
scatter!(
plt_nmsm,
wks[[1, 2, size(wks, 1)]],
mpxv_wkly[[1, 2, size(wks, 1)], 2],
lab = "",
ms = 6,
markercolor = :black,
color = :black,
alpha = 1,
markerstrokewidth = 0,
lw = 4,
shape = :square,
)
scatter!(
plt_nmsm,
wks[3:(end-1)],
mpxv_wkly[3:(end-1), 2],
lab = "Data",
ms = 6,
markercolor = :black,
color = :black,
alpha = 1,
markerstrokewidth = 0,
lw = 4,
yerrors = (
mpxv_wkly[3:(end-1), 2] .- lwr_mpxv_wkly[3:(end-1), 2],
upr_mpxv_wkly[3:(end-1), 2] .- mpxv_wkly[3:(end-1), 2],
),
)
display(plt_nmsm)
## Change in transmission over time
long_wks_reversion = [wks[1:(end-3)]; wks_reversion]
ts_reversion =
(long_wks_reversion[1]-Date(2021, 12, 31)).value:1.0:(long_wks_reversion[end]-Date(2021,12,31)).value
transmission_risks_with_vaccines_no_rev =
map(θ -> reproductive_ratios(θ, constants, ts_reversion), param_draws)
transmission_risks_with_vaccines_12wk_rev =
map(θ -> reproductive_ratios(θ, constants, 12, ts_reversion), param_draws)
transmission_risks_with_vaccines_4wk_rev =
map(θ -> reproductive_ratios(θ, constants, 4, ts_reversion), param_draws)
creds_R0_gbmsm_no_rev = matrix_cred_intervals(
[
reshape(R.R₀_gbmsm, length(R.R₀_gbmsm), 1) for
R in transmission_risks_with_vaccines_no_rev
],
central_est = :median,
)
creds_R0_ngbmsm_no_rev = matrix_cred_intervals(
[
reshape(R.R₀_ngbmsm, length(R.R₀_gbmsm), 1) for
R in transmission_risks_with_vaccines_no_rev
],
central_est = :median,
)
creds_R0_gbmsm_12wk = matrix_cred_intervals(
[
reshape(R.R₀_gbmsm, length(R.R₀_gbmsm), 1) for
R in transmission_risks_with_vaccines_12wk_rev
],
central_est = :median,
)
creds_R0_ngbmsm_12wk = matrix_cred_intervals(
[
reshape(R.R₀_ngbmsm, length(R.R₀_gbmsm), 1) for
R in transmission_risks_with_vaccines_12wk_rev
],
central_est = :median,
)
creds_R0_gbmsm_4wk = matrix_cred_intervals(
[
reshape(R.R₀_gbmsm, length(R.R₀_gbmsm), 1) for
R in transmission_risks_with_vaccines_4wk_rev
],
central_est = :median,
)
creds_R0_ngbmsm_4wk = matrix_cred_intervals(
[
reshape(R.R₀_ngbmsm, length(R.R₀_gbmsm), 1) for
R in transmission_risks_with_vaccines_4wk_rev
],
central_est = :median,
)
# Susceptibility matrices are saved weekly so need to compare correctly with the daily R0 estimates
R_idxs = findall([Date(2021,12,31) + Day(Int64(t)) ∈ wks[1:end] for t in ts_reversion])
eff_susceptibles = [[1.0; 1.0; mat[:, 1:10] * (mean_daily_cnts ./ (10 * mean(mean_daily_cnts)))] for mat in sus_mats]
n_days_fit = size(eff_susceptibles[1], 1)
creds_eff_susceptibles = matrix_cred_intervals(eff_susceptibles .|> x -> reshape(1 .- x, size(x, 1), 1),central_est = :median)
m = creds_eff_susceptibles.median_pred[end-3] * 100 |> x -> round(x, sigdigits = 3)
qs = (creds_eff_susceptibles.median_pred[end-3] - creds_eff_susceptibles.lb_pred_025[end-3], creds_eff_susceptibles.median_pred[end-3] + creds_eff_susceptibles.ub_pred_025[end-3]) .|> x -> x * 100 .|> x -> round(x, sigdigits = 3)
case_output = case_output * raw"\newcommand{\dropinReff}{" * "$(m)\\% ($(qs[1]) -- $(qs[2])\\% }\n"
creds_effR0_gbmsm_no_rev = matrix_cred_intervals(
[
reshape(R.R₀_gbmsm[R_idxs] .* eff_susceptibles[i] , n_days_fit, 1) for
(i, R) in enumerate(transmission_risks_with_vaccines_no_rev)
],
central_est = :median,
)
creds_effR0_gbmsm_12wk = matrix_cred_intervals(
[
reshape(R.R₀_gbmsm[R_idxs] .* eff_susceptibles[i] , n_days_fit, 1) for
(i, R) in enumerate(transmission_risks_with_vaccines_12wk_rev)
],
central_est = :median,
)
creds_effR0_gbmsm_4wk = matrix_cred_intervals(
[
reshape(R.R₀_gbmsm[R_idxs] .* eff_susceptibles[i] , n_days_fit, 1) for
(i, R) in enumerate(transmission_risks_with_vaccines_4wk_rev)
],
central_est = :median,
)
##
dates = [Date(2021, 12, 31) + Day(t) for t in ts_reversion]
f = findfirst(dates .== Date(2022, 7, 23))
f2 = findfirst(dates .== Date(2022, 9, 1))
f3 = findfirst(dates .== wks[end-1])
# Posterior probability of >10% decrease in risk
# p_sx_risk_pheic = mean([p_trans[f] < p_trans[1] * 0.9 for p_trans in p_sx_trans_risks])
plt_R_gbmsm = plot(
dates[1:f3],
creds_R0_gbmsm_no_rev.median_pred[1:f3],
ribbon = (
creds_R0_gbmsm_no_rev.lb_pred_25[1:f3],
creds_R0_gbmsm_no_rev.ub_pred_25[1:f3],
),
lw = 3,
fillalpha = 0.2,
lab = "Fitted R₀",
title = "Reproductive number (GBMSM)",
ylabel = "R₀(t) (GBMSM)",
xlims = (long_wks[1] - Day(7), long_wks[end] + Day(7)),
ylims = (0, 10.5),
xticks = (
[Date(2022, 5, 1) + Month(k) for k = 0:11],
[monthname(Date(2022, 5, 1) + Month(k))[1:3] for k = 0:11],
),
yticks = 0:10,
color = :black,
left_margin = 8mm,
size = (800, 600),
dpi = 250,
tickfont = 13,
titlefont = 22,
guidefont = 18,
legendfont = 11,
)
plot!(
plt_R_gbmsm,
dates[1:f3],
creds_R0_gbmsm_no_rev.median_pred[1:f3],
ribbon = (
creds_R0_gbmsm_no_rev.lb_pred_10[1:f3],
creds_R0_gbmsm_no_rev.ub_pred_10[1:f3],
),
fillalpha = 0.2,
lab = "",
lw = 0,
c = :black,
)
plot!(
plt_R_gbmsm,
dates[1:f3],
creds_R0_gbmsm_no_rev.median_pred[1:f3],
ribbon = (
creds_R0_gbmsm_no_rev.lb_pred_025[1:f3],
creds_R0_gbmsm_no_rev.ub_pred_025[1:f3],
),
fillalpha = 0.2,
lab = "",
lw = 0,
c = :black,
)
plot!(
plt_R_gbmsm,
wks[1:(end-1)],
creds_effR0_gbmsm_no_rev.median_pred[1:(end-1)],
ls = :dash,
lab = "Fitted eff. R₀",
lw = 3,
c = :black,
)
plot!(
plt_R_gbmsm,
dates[f2:end],
creds_R0_gbmsm_4wk.median_pred[f2:end],
ribbon = (creds_R0_gbmsm_4wk.lb_pred_25[f2:end], creds_R0_gbmsm_4wk.ub_pred_25[f2:end]),
fillalpha = 0.2,
lab = "4 weeks reversion",
lw = 3,
c = :red,
)
plot!(
plt_R_gbmsm,
dates[f2:end],
creds_R0_gbmsm_4wk.median_pred[f2:end],
ribbon = (creds_R0_gbmsm_4wk.lb_pred_10[f2:end], creds_R0_gbmsm_4wk.ub_pred_10[f2:end]),
fillalpha = 0.2,
lab = "",
lw = 3,
c = :red,
)
plot!(
plt_R_gbmsm,
dates[f2:end],
creds_R0_gbmsm_4wk.median_pred[f2:end],
ribbon = (
creds_R0_gbmsm_4wk.lb_pred_025[f2:end],
creds_R0_gbmsm_4wk.ub_pred_025[f2:end],
),
fillalpha = 0.2,
lab = "",
lw = 3,
c = :red,
)
plot!(
plt_R_gbmsm,
dates[f2:end],
creds_R0_gbmsm_12wk.median_pred[f2:end],
ribbon = (
creds_R0_gbmsm_12wk.lb_pred_25[f2:end],
creds_R0_gbmsm_12wk.ub_pred_25[f2:end],
),
fillalpha = 0.2,
lab = "12 week reversion",
lw = 3,
c = :blue,
)
plot!(
plt_R_gbmsm,
dates[f2:end],
creds_R0_gbmsm_12wk.median_pred[f2:end],
ribbon = (
creds_R0_gbmsm_12wk.lb_pred_10[f2:end],
creds_R0_gbmsm_12wk.ub_pred_10[f2:end],
),
fillalpha = 0.2,
lab = "",
lw = 3,
c = :blue,
)
plot!(
plt_R_gbmsm,
dates[f2:end],
creds_R0_gbmsm_12wk.median_pred[f2:end],
ribbon = (
creds_R0_gbmsm_12wk.lb_pred_025[f2:end],
creds_R0_gbmsm_12wk.ub_pred_025[f2:end],
),
fillalpha = 0.2,
lab = "",
lw = 3,
c = :blue,
)
plot!(
plt_R_gbmsm,
dates[210:end],
creds_R0_gbmsm_4wk.median_pred[210:end],
alpha = 0.75,
lab = "",
ls = :dot,
lw = 3,
c = :red,
)
vline!(
plt_R_gbmsm,
[Date(2022, 7, 23)],
lw = 3,
color = :black,
ls = :dot,
lab = "",
annotation = (Date(2022, 7, 23) + Day(7), 7.5, "WHO declaration"),
annotationrotation = 270,
)
##
dates = [Date(2021, 12, 31) + Day(t) for t in ts_reversion]
f = findfirst(dates .== Date(2022, 7, 23))
f2 = findfirst(dates .== Date(2022, 9, 1))
f3 = findfirst(dates .== wks[end-1])
plt_R_ngbmsm = plot(