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mpx_scenarios.jl
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mpx_scenarios.jl
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using Distributions, StatsBase, StatsPlots
using Plots.PlotMeasures, CSV, DataFrames
using LinearAlgebra, RecursiveArrayTools
using OrdinaryDiffEq, ApproxBayes, MCMCChains
using JLD2
using MonkeypoxUK
## Grab UK data and model set up
include("mpxv_datawrangling_inff.jl");
include("setup_model.jl");
##
colname = "seqn_fit7"
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
param_draws = load("posteriors/posterior_param_draws_2022-10-17.jld2")["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")
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)
##
chp_t2 = (Date(2022, 7, 23) - Date(2021, 12, 31)).value #Announcement of Public health emergency
inf_duration_red = 0.0
param_draws_no_behav = param_draws .|> θ -> [θ[1:(end-4)]; zeros(4)]
interventions_ensemble = [
(
trans_red2=θ[9] * θ[11],
vac_effectiveness=rand(Uniform(0.7, 0.85)),
trans_red_other2=θ[10] * θ[12],
wkly_vaccinations,
chp_t2,
inf_duration_red,
) for θ in param_draws
]
no_vac_ensemble = [
(
trans_red2=θ[9] * θ[11],#Based on posterior for first change point with extra dispersion
vac_effectiveness=rand(Uniform(0.7, 0.85)),
trans_red_other2=θ[10] * θ[12],
wkly_vaccinations=zeros(size(wkly_vaccinations_ceased)),
chp_t2,
inf_duration_red,
) for θ in param_draws
]
no_vac_and_no_red_ensemble = [
(
trans_red2=0,#Based on posterior for first change point with extra dispersion
vac_effectiveness=rand(Uniform(0.7, 0.85)),
trans_red_other2=θ[10] * θ[12],
wkly_vaccinations=zeros(size(wkly_vaccinations_ceased)),
chp_t2,
inf_duration_red,
) for θ in param_draws_no_behav
]
mpx_sim_function_interventions = MonkeypoxUK.mpx_sim_function_interventions
preds_and_incidence_interventions = map(
(θ, intervention) ->
mpx_sim_function_interventions(θ, constants, long_mpxv_wkly, intervention)[2:4],
param_draws,
interventions_ensemble,
)
preds_and_incidence_interventions_4wkrev = map(
(θ, intervention) ->
mpx_sim_function_interventions(θ, constants, long_mpxv_wkly, intervention, 4)[2:4],
param_draws,
interventions_ensemble,
)
preds_and_incidence_interventions_12wkrev = map(
(θ, intervention) ->
mpx_sim_function_interventions(θ, constants, long_mpxv_wkly, intervention, 12)[2:4],
param_draws,
interventions_ensemble,
)
preds_and_incidence_interventions_cvac = map(
(θ, intervention) ->
mpx_sim_function_interventions(θ, constants, long_mpxv_wkly, intervention)[2:4],
param_draws,
no_vac_ensemble,
)
preds_and_incidence_interventions_cvac_4wkrev = map(
(θ, intervention) ->
mpx_sim_function_interventions(θ, constants, long_mpxv_wkly, intervention, 4)[2:4],
param_draws,
no_vac_ensemble,
)
preds_and_incidence_interventions_cvac_12wkrev = map(
(θ, intervention) ->
mpx_sim_function_interventions(θ, constants, long_mpxv_wkly, intervention, 12)[2:4],
param_draws,
no_vac_ensemble,
)
preds_and_incidence_novac_no_chg = map(
(θ, intervention) ->
mpx_sim_function_interventions(θ, constants, long_mpxv_wkly, intervention)[2:4],
param_draws_no_behav,
no_vac_and_no_red_ensemble,
)
##Gather projections
d1, d2 = size(mpxv_wkly)
preds = [x[1] for x in preds_and_incidence_interventions]
preds_4wk = [x[1] for x in preds_and_incidence_interventions_4wkrev]
preds_12wk = [x[1] for x in preds_and_incidence_interventions_12wkrev]
preds_cvac = [x[1] for x in preds_and_incidence_interventions_cvac]
preds_cvac4wk = [x[1] for x in preds_and_incidence_interventions_cvac_4wkrev]
preds_cvac12wk = [x[1] for x in preds_and_incidence_interventions_cvac_12wkrev]
pred_unmitigated = [x[1] for x in preds_and_incidence_novac_no_chg]
cum_cases_unmitigated = [cumsum(x[1], dims=1) for x in preds_and_incidence_novac_no_chg]
cum_cases_forwards =
[cumsum(x[1][(d1+1):end, :], dims=1) for x in preds_and_incidence_interventions]
cum_cases_forwards_4wk = [
cumsum(x[1][(d1+1):end, :], dims=1) for x in preds_and_incidence_interventions_4wkrev
]
cum_cases_forwards_12wk = [
cumsum(x[1][(d1+1):end, :], dims=1) for x in preds_and_incidence_interventions_12wkrev
]
cum_cases_forwards_cvac =
[cumsum(x[1][(d1+1):end, :], dims=1) for x in preds_and_incidence_interventions_cvac]
cum_cases_forwards_cvac4wk = [
cumsum(x[1][(d1+1):end, :], dims=1) for
x in preds_and_incidence_interventions_cvac_4wkrev
]
cum_cases_forwards_cvac12wk = [
cumsum(x[1][(d1+1):end, :], dims=1) for
x in preds_and_incidence_interventions_cvac_12wkrev
]
##Simulation projections
cred_int = MonkeypoxUK.cred_intervals(preds)
cred_int_4wk = MonkeypoxUK.cred_intervals(preds_4wk)
cred_int_12wk = MonkeypoxUK.cred_intervals(preds_12wk)
cred_int_cvac = MonkeypoxUK.cred_intervals(preds_cvac)
cred_int_cvac4wk = MonkeypoxUK.cred_intervals(preds_cvac4wk)
cred_int_cvac12wk = MonkeypoxUK.cred_intervals(preds_cvac12wk)
cred_int_unmitigated = MonkeypoxUK.cred_intervals(pred_unmitigated)
cred_int_cum_cases_unmitigated = MonkeypoxUK.cred_intervals(cum_cases_unmitigated)
## MSM projections
d_proj = 19
gbmsm_case_projections = DataFrame()
gbmsm_case_projections[:, "Date"] = long_wks
reported_gbmsm_cases = convert(Vector{Union{Float64,String}}, fill("NA", length(cred_int_cvac4wk.mean_pred[:, 1])))
reported_gbmsm_cases[3:size(mpxv_wkly, 1)] .= mpxv_wkly[3:end, 1]
gbmsm_case_projections[:, "Inferred GBMSM cases"] = reported_gbmsm_cases
gbmsm_case_projections[:, "Projected GBMSM cases (post. mean; no reversion)"] = cred_int.mean_pred[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 10%; no reversion)"] = cred_int.mean_pred[:, 1] .- cred_int.lb_pred_10[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 90%; no reversion)"] = cred_int.mean_pred[:, 1] .+ cred_int.ub_pred_10[:, 1]
plt_msm = plot(;
ylabel="Weekly cases",
title="UK Monkeypox Case Projections (GBMSM)",# yscale=:log10,
legend=:topright,
# yticks=([1, 2, 11, 101, 1001], [0, 1, 10, 100, 1000]),
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=5mm,
size=(800, 600),
dpi=250,
tickfont=13,
titlefont=22,
guidefont=18,
legendfont=11
)
plot!(
plt_msm,
long_wks,
cred_int_12wk.mean_pred[:, 1],
ribbon=(cred_int_12wk.lb_pred_10[:, 1], cred_int_12wk.ub_pred_10[:, 1]),
lw=3,
color=:black,
fillalpha=0.2,
lab="12 week reversion",
)
gbmsm_case_projections[:, "Date"] = long_wks
gbmsm_case_projections[:, "Projected GBMSM cases (post. mean; 12 wk reversion)"] = cred_int_12wk.mean_pred[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 10%; 12 wk reversion)"] = cred_int_12wk.mean_pred[:, 1] .- cred_int_12wk.lb_pred_10[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 90%; 12 wk reversion)"] = cred_int_12wk.mean_pred[:, 1] .+ cred_int_12wk.ub_pred_10[:, 1]
plot!(
plt_msm,
long_wks[1:end],
cred_int_cvac12wk.mean_pred[1:end, 1],
# ribbon=(cred_int_cvac.lb_pred_10[d_proj:end, 1], cred_int_cvac.ub_pred_10[d_proj:end, 1]),
lw=3,
color=:black,
ls=:dash,
fillalpha=0.2,
lab="12 week reversion (no vaccines)",
)
gbmsm_case_projections[:, "Projected GBMSM cases (post. mean; 12 wk reversion + no vacs)"] = cred_int_cvac12wk.mean_pred[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 10%; 12 wk reversion + no vacs)"] = cred_int_cvac12wk.mean_pred[:, 1] .- cred_int_cvac12wk.lb_pred_10[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 90%; 12 wk reversion + no vacs)"] = cred_int_cvac12wk.mean_pred[:, 1] .+ cred_int_cvac12wk.ub_pred_10[:, 1]
plot!(
plt_msm,
long_wks[d_proj:end],
cred_int_4wk.mean_pred[d_proj:end, 1],
ribbon=(cred_int_4wk.lb_pred_10[d_proj:end, 1], cred_int_4wk.ub_pred_10[d_proj:end, 1]),
lw=3,
color=2,
fillalpha=0.2,
lab="4 week reversion",
)
gbmsm_case_projections[:, "Projected GBMSM cases (post. mean; 4 wk reversion)"] = cred_int_4wk.mean_pred[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 10%; 4 wk reversion)"] = cred_int_4wk.mean_pred[:, 1] .- cred_int_4wk.lb_pred_10[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 90%; 4 wk reversion)"] = cred_int_4wk.mean_pred[:, 1] .+ cred_int_4wk.ub_pred_10[:, 1]
plot!(
plt_msm,
long_wks[19:end],
cred_int_cvac4wk.mean_pred[19:end, 1],
# ribbon=(cred_int_4wk.lb_pred_10[19:end, 1], cred_int_4wk.ub_pred_10[19:end, 1]),
lw=3,
ls=:dash,
color=2,
fillalpha=0.2,
lab="4 week reversion (no vaccines)",
)
gbmsm_case_projections[:, "Projected GBMSM cases (post. mean; 4 wk reversion + no vacs)"] = cred_int_cvac4wk.mean_pred[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 10%; 4 wk reversion + no vacs)"] = cred_int_cvac4wk.mean_pred[:, 1] .- cred_int_cvac4wk.lb_pred_10[:, 1]
gbmsm_case_projections[:, "Projected GBMSM cases (post. 90%; 4 wk reversion + no vacs)"] = cred_int_cvac4wk.mean_pred[:, 1] .+ cred_int_cvac4wk.ub_pred_10[:, 1]
scatter!(
plt_msm,
wks[(end):end],
mpxv_wkly[(end):end, 1],
lab="",
ms=6,
color=:black,
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,
color=:black,
)
CSV.write("projections/gbmsm_case_projections" * string(wks[end]) * ".csv", gbmsm_case_projections)
display(plt_msm)
##
nongbmsm_case_projections = DataFrame()
nongbmsm_case_projections[:, "Date"] = long_wks
reported_nongbmsm_cases = convert(Vector{Union{Float64,String}}, fill("NA", length(cred_int_cvac4wk.mean_pred[:, 1])))
reported_nongbmsm_cases[3:size(mpxv_wkly, 1)] .= mpxv_wkly[3:end, 2]
nongbmsm_case_projections[:, "Inferred non-GBMSM cases"] = reported_nongbmsm_cases
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. mean; no reversion)"] = cred_int.mean_pred[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 10%; no reversion)"] = cred_int.mean_pred[:, 2] .- cred_int.lb_pred_10[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 90%; no reversion)"] = cred_int.mean_pred[:, 2] .+ cred_int.ub_pred_10[:, 2]
plt_nmsm = plot(;
ylabel="Weekly cases",
title="UK Monkeypox Case Projections (non-GBMSM)",# yscale=:log10,
legend=:topright,
# yticks=([1, 2, 11, 101, 1001], [0, 1, 10, 100, 1000]),
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,
long_wks,
cred_int_12wk.mean_pred[:, 2],
ribbon=(cred_int_12wk.lb_pred_10[:, 2], cred_int_12wk.ub_pred_10[:, 2]),
lw=3,
color=:black,
fillalpha=0.2,
lab="12 week reversion",
)
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. mean; 12wk reversion)"] = cred_int_12wk.mean_pred[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 10%; 12wk reversion)"] = cred_int_12wk.mean_pred[:, 2] .- cred_int_12wk.lb_pred_10[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 90%; 12wk reversion)"] = cred_int_12wk.mean_pred[:, 2] .+ cred_int_12wk.ub_pred_10[:, 2]
plot!(
plt_nmsm,
long_wks[d_proj:end],
cred_int_cvac12wk.mean_pred[d_proj:end, 2],
# ribbon=(cred_int_cvac.lb_pred_10[d_proj:end, 1], cred_int_cvac.ub_pred_10[d_proj:end, 1]),
lw=3,
color=:black,
ls=:dash,
fillalpha=0.2,
lab="12 week reversion (no vaccines)",
)
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. mean; 12wk reversion + no vacs)"] = cred_int_cvac12wk.mean_pred[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 10%; 12wk reversion + no vacs)"] = cred_int_cvac12wk.mean_pred[:, 2] .- cred_int_cvac12wk.lb_pred_10[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 90%; 12wk reversion + no vacs)"] = cred_int_cvac12wk.mean_pred[:, 2] .+ cred_int_cvac12wk.ub_pred_10[:, 2]
plot!(
plt_nmsm,
long_wks[19:end],
cred_int_4wk.mean_pred[19:end, 2],
ribbon=(cred_int_4wk.lb_pred_10[19:end, 2], cred_int_4wk.ub_pred_10[19:end, 2]),
lw=3,
color=2,
fillalpha=0.2,
lab="4 week reversion",
)
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. mean; 4wk reversion)"] = cred_int_4wk.mean_pred[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 10%; 4wk reversion)"] = cred_int_4wk.mean_pred[:, 2] .- cred_int_4wk.lb_pred_10[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 90%; 4wk reversion)"] = cred_int_4wk.mean_pred[:, 2] .+ cred_int_4wk.ub_pred_10[:, 2]
plot!(
plt_nmsm,
long_wks[19:end],
cred_int_cvac4wk.mean_pred[19:end, 2],
# ribbon=(cred_int_4wk.lb_pred_10[19:end, 1], cred_int_4wk.ub_pred_10[19:end, 1]),
lw=3,
ls=:dash,
color=2,
fillalpha=0.2,
lab="4 week reversion (no vaccines)",
)
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. mean; 4wk reversion + no vacs)"] = cred_int_cvac4wk.mean_pred[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 10%; 4wk reversion + no vacs)"] = cred_int_cvac4wk.mean_pred[:, 2] .- cred_int_cvac4wk.lb_pred_10[:, 2]
nongbmsm_case_projections[:, "Projected non-GBMSM cases (post. 90%; 4wk reversion + no vacs)"] = cred_int_cvac4wk.mean_pred[:, 2] .+ cred_int_cvac4wk.ub_pred_10[:, 2]
scatter!(
plt_nmsm,
wks[(end):end],
mpxv_wkly[(end):end, 2],
lab="",
ms=6,
color=:black,
shape=:square,
)
scatter!(
plt_nmsm,
wks[3:(end-1)],
mpxv_wkly[3:(end-1), 2],
lab="Data",
ms=6,
color=:black,
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] ),
)
CSV.write("projections/nongbmsm_case_projections" * string(wks[end]) * ".csv", nongbmsm_case_projections)
display(plt_nmsm)
##cumulative Incidence plots
cred_int_cum_incidence = MonkeypoxUK.cred_intervals(cum_cases_forwards)
cred_int_cum_incidence4wks = MonkeypoxUK.cred_intervals(cum_cases_forwards_4wk)
cred_int_cum_incidence12wks = MonkeypoxUK.cred_intervals(cum_cases_forwards_12wk)
cred_int_cum_incidence_cvac = MonkeypoxUK.cred_intervals(cum_cases_forwards_cvac)
cred_int_cum_incidence_cvac4wks = MonkeypoxUK.cred_intervals(cum_cases_forwards_cvac4wk)
cred_int_cum_incidence_cvac12wks = MonkeypoxUK.cred_intervals(cum_cases_forwards_cvac12wk)
total_cases = sum(mpxv_wkly, dims=1)
gbmsm_cum_case_projections = DataFrame()
gbmsm_cum_case_projections[:, "Date"] = long_wks[((d1+1)):end]
gbmsm_cum_case_projections[:, "Projected Cum. GBMSM cases (post. mean; no reversion)"] = total_cases[:, 1] .+ cred_int_cum_incidence.mean_pred[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 10%; no reversion)"] = total_cases[:, 1] .+ cred_int_cum_incidence.mean_pred[:, 1] .- cred_int_cum_incidence.lb_pred_10[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 90%; no reversion)"] = total_cases[:, 1] .+ cred_int_cum_incidence.mean_pred[:, 1] .+ cred_int_cum_incidence.ub_pred_10[:, 1]
plt_cm_msm = plot(;
ylabel="Cumulative cases",
title="UK Monkeypox cumulative case projections (GBMSM)",#yscale=:log10,
legend=:topleft,
# yticks=(0:2500:12500, 0:2500:100),
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=15,
titlefont=16,
guidefont=24,
legendfont=11
)
plot!(
plt_cm_msm,
long_wks[((d1+1)):end],
total_cases[:, 1] .+ cred_int_cum_incidence12wks.mean_pred[:, 1],
ribbon=(
cred_int_cum_incidence12wks.lb_pred_10[:, 1],
cred_int_cum_incidence12wks.ub_pred_10[:, 1],
),
lw=3,
color=:black,
fillalpha=0.4,
lab="12 week reversion",
)
gbmsm_cum_case_projections[:, "Projected Cum. GBMSM cases (post. mean; 12wk reversion)"] = total_cases[:, 1] .+ cred_int_cum_incidence12wks.mean_pred[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 10%; 12wk reversion)"] = total_cases[:, 1] .+ cred_int_cum_incidence12wks.mean_pred[:, 1] .- cred_int_cum_incidence12wks.lb_pred_10[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 90%; 12wk reversion)"] = total_cases[:, 1] .+ cred_int_cum_incidence12wks.mean_pred[:, 1] .+ cred_int_cum_incidence12wks.ub_pred_10[:, 1]
plot!(
plt_cm_msm,
long_wks[((d1+1)):end],
total_cases[:, 1] .+ cred_int_cum_incidence_cvac12wks.mean_pred[:, 1],
ribbon=(
cred_int_cum_incidence_cvac12wks.lb_pred_10[:, 1],
cred_int_cum_incidence_cvac12wks.ub_pred_10[:, 1],
),
lw=3,
ls=:dash,
color=:black,
fillalpha=0.2,
fillstyle = :x,
lab="12 week reversion (no vaccines)",
)
gbmsm_cum_case_projections[:, "Projected Cum. GBMSM cases (post. mean; 12wk reversion + no vacs)"] = total_cases[:, 1] .+ cred_int_cum_incidence_cvac12wks.mean_pred[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 10%; 12wk reversion + no vacs)"] = total_cases[:, 1] .+ cred_int_cum_incidence_cvac12wks.mean_pred[:, 1] .- cred_int_cum_incidence_cvac12wks.lb_pred_10[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 90%; 12wk reversion + no vacs)"] = total_cases[:, 1] .+ cred_int_cum_incidence_cvac12wks.mean_pred[:, 1] .+ cred_int_cum_incidence_cvac12wks.ub_pred_10[:, 1]
plot!(
plt_cm_msm,
long_wks[((d1+1)):end],
total_cases[:, 1] .+ cred_int_cum_incidence4wks.mean_pred[:, 1],
ribbon=(
cred_int_cum_incidence4wks.lb_pred_10[:, 1],
cred_int_cum_incidence4wks.ub_pred_10[:, 1],
),
lw=3,
color=2,
fillalpha=0.4,
lab="4 week reversion",
)
gbmsm_cum_case_projections[:, "Projected Cum. GBMSM cases (post. mean; 4wk reversion)"] = total_cases[:, 1] .+ cred_int_cum_incidence4wks.mean_pred[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 10%; 4wk reversion)"] = total_cases[:, 1] .+ cred_int_cum_incidence4wks.mean_pred[:, 1] .- cred_int_cum_incidence4wks.lb_pred_10[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 90%; 4wk reversion)"] = total_cases[:, 1] .+ cred_int_cum_incidence4wks.mean_pred[:, 1] .+ cred_int_cum_incidence4wks.ub_pred_10[:, 1]
plot!(
plt_cm_msm,
long_wks[((d1+1)):end],
total_cases[:, 1] .+ cred_int_cum_incidence_cvac4wks.mean_pred[:, 1],
ribbon=(
cred_int_cum_incidence_cvac4wks.lb_pred_10[:, 1],
cred_int_cum_incidence_cvac4wks.ub_pred_10[:, 1],
),
lw=3,
ls=:dash,
fillstyle = :x,
color=2,
fillalpha=0.3,
lab="4 week reversion (no vaccines)",
)
gbmsm_cum_case_projections[:, "Projected Cum. GBMSM cases (post. mean; 4wk reversion + no vacs)"] = total_cases[:, 1] .+ cred_int_cum_incidence_cvac4wks.mean_pred[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 10%; 4wk reversion + no vacs)"] = total_cases[:, 1] .+ cred_int_cum_incidence_cvac4wks.mean_pred[:, 1] .- cred_int_cum_incidence_cvac4wks.lb_pred_10[:, 1]
gbmsm_cum_case_projections[:, "Projected GBMSM cases (post. 90%; 4wk reversion + no vacs)"] = total_cases[:, 1] .+ cred_int_cum_incidence_cvac4wks.mean_pred[:, 1] .+ cred_int_cum_incidence_cvac4wks.ub_pred_10[:, 1]
plot!(
plt_cm_msm,
long_wks,
cred_int_cum_cases_unmitigated.mean_pred[:, 1],
ribbon=(
cred_int_cum_cases_unmitigated.lb_pred_10[:, 1],
cred_int_cum_cases_unmitigated.ub_pred_10[:, 1],
),
lw=3,
ls=:dash,
color= :green,
fillalpha=0.2,
fillstyle = :x,
lab="Unmitigated",
)
scatter!(
plt_cm_msm,
wks,
cumsum(mpxv_wkly[:, 1], dims=1),
lab="Data",
ms=6,
color=:black,
)
CSV.write("projections/gbmsm_cumulative_case_projections" * string(wks[end]) * ".csv", gbmsm_cum_case_projections)
display(plt_cm_msm)
savefig(plt_cm_msm,"plt_cm_msm.svg")
##
total_cases = sum(mpxv_wkly, dims=1)
nongbmsm_cum_case_projections = DataFrame()
nongbmsm_cum_case_projections[:, "Date"] = long_wks[((d1+1)):end]
nongbmsm_cum_case_projections[:, "Projected Cum. non-GBMSM cases (post. mean; no reversion)"] = total_cases[:, 2] .+ cred_int_cum_incidence.mean_pred[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 10%; no reversion)"] = total_cases[:, 2] .+ cred_int_cum_incidence.mean_pred[:, 2] .- cred_int_cum_incidence.lb_pred_10[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 90%; no reversion)"] = total_cases[:, 2] .+ cred_int_cum_incidence.mean_pred[:, 2] .+ cred_int_cum_incidence.ub_pred_10[:, 2]
plt_cm_nmsm = plot(;
ylabel="Cumulative cases",
title="UK Monkeypox cumulative case projections (non-GBMSM)",#yscale=:log10,
legend=:topleft,
# yticks=(0:2500:12500, 0:2500:12500),
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=15,
titlefont=16,
guidefont=24,
legendfont=11
)
plot!(
plt_cm_nmsm,
long_wks[((d1+1)):end],
total_cases[:, 2] .+ cred_int_cum_incidence12wks.mean_pred[:, 2],
ribbon=(
cred_int_cum_incidence12wks.lb_pred_10[:, 2],
cred_int_cum_incidence12wks.ub_pred_10[:, 2],
),
lw=3,
color=:black,
fillalpha=0.4,
lab="12 week reversion",
)
nongbmsm_cum_case_projections[:, "Projected Cum. non-GBMSM cases (post. mean; 12wk reversion)"] = total_cases[:, 2] .+ cred_int_cum_incidence12wks.mean_pred[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 10%; 12wk reversion)"] = total_cases[:, 2] .+ cred_int_cum_incidence12wks.mean_pred[:, 2] .- cred_int_cum_incidence12wks.lb_pred_10[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 90%; 12wk reversion)"] = total_cases[:, 2] .+ cred_int_cum_incidence12wks.mean_pred[:, 2] .+ cred_int_cum_incidence12wks.ub_pred_10[:, 2]
plot!(
plt_cm_nmsm,
long_wks[((d1+1)):end],
total_cases[:, 2] .+ cred_int_cum_incidence_cvac12wks.mean_pred[:, 2],
ribbon=(
cred_int_cum_incidence_cvac12wks.lb_pred_10[:, 2],
cred_int_cum_incidence_cvac12wks.ub_pred_10[:, 2],
),
lw=3,
ls=:dash,
color=:black,
fillalpha=0.2,
fillstyle = :x,
lab="12 week reversion (no vaccines)",
)
nongbmsm_cum_case_projections[:, "Projected Cum. non-GBMSM cases (post. mean; 12wk reversion + no vacs)"] = total_cases[:, 2] .+ cred_int_cum_incidence_cvac12wks.mean_pred[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 10%; 12wk reversion + no vacs)"] = total_cases[:, 2] .+ cred_int_cum_incidence_cvac12wks.mean_pred[:, 2] .- cred_int_cum_incidence_cvac12wks.lb_pred_10[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 90%; 12wk reversion + no vacs)"] = total_cases[:, 2] .+ cred_int_cum_incidence_cvac12wks.mean_pred[:, 2] .+ cred_int_cum_incidence_cvac12wks.ub_pred_10[:, 2]
plot!(
plt_cm_nmsm,
long_wks[((d1+1)):end],
total_cases[:, 2] .+ cred_int_cum_incidence4wks.mean_pred[:, 2],
ribbon=(
cred_int_cum_incidence4wks.lb_pred_10[:, 2],
cred_int_cum_incidence4wks.ub_pred_10[:, 2],
),
lw=3,
color=2,
fillalpha=0.4,
lab="4 week reversion",
)
nongbmsm_cum_case_projections[:, "Projected Cum. non-GBMSM cases (post. mean; 4wk reversion)"] = total_cases[:, 2] .+ cred_int_cum_incidence4wks.mean_pred[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 10%; 4wk reversion)"] = total_cases[:, 2] .+ cred_int_cum_incidence4wks.mean_pred[:, 2] .- cred_int_cum_incidence4wks.lb_pred_10[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 90%; 4wk reversion)"] = total_cases[:, 2] .+ cred_int_cum_incidence4wks.mean_pred[:, 2] .+ cred_int_cum_incidence4wks.ub_pred_10[:, 2]
plot!(
plt_cm_nmsm,
long_wks[((d1+1)):end],
total_cases[:, 2] .+ cred_int_cum_incidence_cvac4wks.mean_pred[:, 2],
ribbon=(
cred_int_cum_incidence_cvac4wks.lb_pred_10[:, 2],
cred_int_cum_incidence_cvac4wks.ub_pred_10[:, 2],
),
lw=3,
ls=:dash,
color=2,
fillalpha=0.2,
fillstyle = :x,
lab="4 week reversion (no vaccines)",
)
nongbmsm_cum_case_projections[:, "Projected Cum. non-GBMSM cases (post. mean; 4wk reversion + no vacs)"] = total_cases[:, 2] .+ cred_int_cum_incidence_cvac4wks.mean_pred[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 10%; 4wk reversion + no vacs)"] = total_cases[:, 2] .+ cred_int_cum_incidence_cvac4wks.mean_pred[:, 2] .- cred_int_cum_incidence_cvac4wks.lb_pred_10[:, 2]
nongbmsm_cum_case_projections[:, "Projected non-GBMSM cases (post. 90%; 4wk reversion + no vacs)"] = total_cases[:, 2] .+ cred_int_cum_incidence_cvac4wks.mean_pred[:, 2] .+ cred_int_cum_incidence_cvac4wks.ub_pred_10[:, 2]
plot!(
plt_cm_nmsm,
long_wks,
cred_int_cum_cases_unmitigated.mean_pred[:, 2],
ribbon=(
cred_int_cum_cases_unmitigated.lb_pred_10[:, 2],
cred_int_cum_cases_unmitigated.ub_pred_10[:, 2],
),
lw=3,
ls=:dash,
color= :green,
fillalpha=0.2,
fillstyle = :x,
lab="Unmitigated",
)
scatter!(
plt_cm_nmsm,
wks,
cumsum(mpxv_wkly[:, 2], dims=1),
lab="Data",
ms=6,
color=:black,
)
CSV.write("projections/nongbmsm_cumulative_case_projections" * string(wks[end]) * ".csv", nongbmsm_cum_case_projections)
display(plt_cm_nmsm)
savefig(plt_cm_nmsm,"plt_cm_nmsm.svg")
##Combined plot for cases
lo = @layout [a b; c d]
plt = plot(
plt_msm,
plt_nmsm,
plt_cm_msm,
plt_cm_nmsm,
size=(1600, 1200),
dpi=250,
left_margin=10mm,
bottom_margin=10mm,
right_margin=10mm,
layout=lo,
)
display(plt)
savefig(plt, "plots/case_projections_" * string(wks[end]) * ".png")
savefig(plt, "plots/case_projections_" * string(wks[end]) * ".svg")
## Change in transmission over time
prob_trans = [θ[4] for θ in param_draws]
mean_inf_periods = [θ[3] for θ in param_draws]
red_sx_trans = [θ[9] for θ in param_draws]
chp1 = [θ[8] for θ in param_draws]
red_sx_trans2 = [int.trans_red2 for int in interventions_ensemble]
"""
function generate_trans_risk_over_time(p_trans, trans_red, trans_red2, chp, chp2, ts)
Calculate the transmission risk daily.
"""
function generate_trans_risk_over_time(p_trans, trans_red, trans_red2, chp, chp2, ts)
log_p = [log(p_trans) + log(1 - trans_red) * (t >= chp) + log(1 - trans_red2) * (t >= chp2) for t in ts]
return exp.(log_p)
end
"""
function generate_trans_risk_over_time(
p_trans,
trans_red,
trans_red2,
chp,
chp2,
ts,
days_reversion;
reversion_time=(Date(2022, 9, 1) - Date(2021, 12, 31)).value
)
Calculate the transmission risk daily, with a 1% -> 99% reversion to normal occuring over `days_reversion`.
"""
function generate_trans_risk_over_time(
p_trans,
trans_red,
trans_red2,
chp,
chp2,
ts,
days_reversion;
reversion_time=(Date(2022, 9, 1) - Date(2021, 12, 31)).value
)
# log_r = -(log(1 - trans_red) + log(1 - trans_red2)) / days_reversion
T₅₀ = reversion_time + (days_reversion / 2) # 50% return to normal point
κ = (days_reversion / 2) / 4.6 # logistic scale for return to normal: 4.6 is κ = 1 time to go from 0.01 to 0.5 and 0.5 to 0.99
p_min = p_trans * (1 - trans_red) * (1 - trans_red2)
log_p = [log(p_trans) + log(1 - trans_red) * (t >= chp) + log(1 - trans_red2) * (t >= chp2) for t in ts]
return exp.(log_p) .+ [(p_trans - p_min) * sigmoid((t - T₅₀) / κ) for t in ts]
end
"""
function generate_R_gbmsm_over_time(
p_trans,
mean_inf_period,
trans_red,
trans_red2,
chp,
chp2,
ts,
days_reversion;
av_cnt_rate = mean(mean_daily_cnts),
reversion_time=(Date(2022, 9, 1) - Date(2021, 12, 31)).value
)
Calculate the R₀ for GBMSM only, with a 1% -> 99% reversion to normal occuring over `days_reversion`.
"""
function generate_R_gbmsm_over_time(
p_trans,
mean_inf_period,
trans_red,
trans_red2,
chp,
chp2,
ts,
days_reversion;
av_cnt_rate=mean(mean_daily_cnts),
reversion_time=(Date(2022, 9, 1) - Date(2021, 12, 31)).value
)
# log_r = -(log(1 - trans_red) + log(1 - trans_red2)) / days_reversion
T₅₀ = reversion_time + (days_reversion / 2) # 50% return to normal point
κ = (days_reversion / 2) / 4.6 # logistic scale for return to normal: 4.6 is κ = 1 time to go from 0.01 to 0.5 and 0.5 to 0.99
p_min = p_trans * (1 - trans_red) * (1 - trans_red2)
log_p = [log(p_trans) + log(1 - trans_red) * (t >= chp) + log(1 - trans_red2) * (t >= chp2) for t in ts]
pₜ = exp.(log_p) .+ [(p_trans - p_min) * sigmoid((t - T₅₀) / κ) for t in ts]
return mean_inf_period .* av_cnt_rate .* pₜ
end
##
ts_risk = 1:2*365
p_sx_trans_risks =
map(
(p_tr, red_sx_tr, red_sx_tr2, ch1) -> generate_trans_risk_over_time(
p_tr,
red_sx_tr,
red_sx_tr2,
ch1,
chp_t2,
ts_risk,
),
prob_trans,
red_sx_trans,
red_sx_trans2,
chp1,
) .|> x -> reshape(x, length(x), 1)
p_sx_trans_risks_4wk =
map(
(p_tr, red_sx_tr, red_sx_tr2, ch1) -> generate_trans_risk_over_time(
p_tr,
red_sx_tr,
red_sx_tr2,
ch1,
chp_t2,
ts_risk,
4 * 7,
),
prob_trans,
red_sx_trans,
red_sx_trans2,
chp1,
) .|> x -> reshape(x, length(x), 1)
R_gbmsm_4wk =
map(
(p_tr, mean_inf_period, red_sx_tr, red_sx_tr2, ch1) -> generate_R_gbmsm_over_time(
p_tr,
mean_inf_period,
red_sx_tr,
red_sx_tr2,
ch1,
chp_t2,
ts_risk,
4 * 7,
),
prob_trans,
mean_inf_periods,
red_sx_trans,
red_sx_trans2,
chp1,
) .|> x -> reshape(x, length(x), 1)
p_sx_trans_risks_12wk =
map(
(p_tr, red_sx_tr, red_sx_tr2, ch1) -> generate_trans_risk_over_time(
p_tr,
red_sx_tr,
red_sx_tr2,
ch1,
chp_t2,
ts_risk,
12 * 7,
),
prob_trans,
red_sx_trans,
red_sx_trans2,
chp1,
) .|> x -> reshape(x, length(x), 1)
R_gbmsm_12wk =
map(
(p_tr, mean_inf_period, red_sx_tr, red_sx_tr2, ch1) -> generate_R_gbmsm_over_time(
p_tr,
mean_inf_period,
red_sx_tr,
red_sx_tr2,
ch1,
chp_t2,
ts_risk,
12 * 7,
),
prob_trans,
mean_inf_periods,
red_sx_trans,
red_sx_trans2,
chp1,
) .|> x -> reshape(x, length(x), 1)
sx_trans_risk_cred_int = prev_cred_intervals(p_sx_trans_risks)
sx_trans_risk_cred_int_4wk = prev_cred_intervals(p_sx_trans_risks_4wk)
sx_trans_risk_cred_int_12wk = prev_cred_intervals(p_sx_trans_risks_12wk)
R_gbmsms_cred_int_4wk = prev_cred_intervals(R_gbmsm_4wk)
R_gbmsms_cred_int_12wk = prev_cred_intervals(R_gbmsm_12wk)
dates = [Date(2021, 12, 31) + Day(t) for t in ts_risk]
f = findfirst(dates .== Date(2022, 7, 23))
#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])
sx_cnt_trans_risk = DataFrame()
sx_cnt_trans_risk[:, "Date"] = dates[dates.>=Date(2022, 5, 1)]
R_gbmsm_df = DataFrame()
R_gbmsm_df[:, "Date"] = dates[dates.>=Date(2022, 5, 1)]
plt_chng = plot(
dates,
sx_trans_risk_cred_int_12wk.mean_pred,
ribbon=(sx_trans_risk_cred_int_12wk.lb_pred_10, sx_trans_risk_cred_int_12wk.ub_pred_10),
lw=3,
fillalpha=0.2,
lab="Transmission probability (12 week reversion)",
title="Transmission probability (sexual contacts)",
ylabel="Prob. per sexual contact",
xlims=(long_wks[1] - Day(7), long_wks[end] + Day(7)),
ylims=(0, 0.7),
xticks=(
[Date(2022, 5, 1) + Month(k) for k = 0:11],
[monthname(Date(2022, 5, 1) + Month(k))[1:3] for k = 0:11],