Province forecasts

This page gives the one-week-ahead forecast for each province from the joint model. The projection is defined in the province forecast Methods section. The national forecast is on the forecasts page. How these forecasts have scored against what each province went on to report is in the forecast by province evaluation. How well the model reproduces each province's share is on the in-sample checks page.

Load packages, data and fitted chains

julia

# Shared setup: packages, observations and the fit registry. See
# `docs/pages/_setup.jl`.
using BVDOutbreakSize
include(joinpath(pkgdir(BVDOutbreakSize), "docs", "pages", "_setup.jl"))

validation_forecast_from (generic function with 1 method)

julia

# The fits this page reads, loaded from the cache here.
chn_joint = load_fit("joint");

Summary

The table gives each province's forecast for the week to as a 90% predictive interval, and the bullets under it compare the provinces draw by draw. The provinces add up to the national forecast.

Project each province a week ahead

julia

# Read from the same draws as the national forecast page, so the provinces
# add up to the national forecast shown there.
province_draws = fit_forecast("joint");
province_projection = forecast_provinces(
    province_draws; horizon = 7, n_patches = N_PATCHES
);
province_forecast = province_forecast_table(
    province_draws, province_projection;
    n_patches = N_PATCHES
);
province_forecast_fig = plot_province_forecast(
    province_draws, province_projection;
    n_patches = N_PATCHES
);
province_week_end = obs.cutoff + Day(7);
province_forecast_md = province_forecast_headline(
    province_projection; n_patches = N_PATCHES
);

Province	New confirmed cases	New confirmed deaths	Patients in isolation	Isolation beds	New admissions	New infections	R at T+7	P(R > 1)
Ituri	164–433	66–201	299–553	1015–1247	374–663	189–946	0.5–1.37	31%
Nord-Kivu	85–282	54–186	188–354	354–404	240–432	111–751	0.44–1.45	29%
Haut-Uele	8–70	1–37	30–70	138–170	39–89	17–156	0.47–1.72	41%
Other provinces	2–31	0–15	19–30	28–32	23–36	8–97	0.59–2.01	64%

Most new confirmed cases: Ituri, with probability 88%.
Most new confirmed deaths: Ituri, with probability 60%.
Growing at T+7: 1 of 4 provinces are more likely than not to be growing (Other provinces).
Beds full at T+7: no province is more likely than not to be at or above its beds.

One-week-ahead forecast by province

Each panel is one forecast target, the provinces side by side, for the week to province_week_end.

The maps shade each province by its patch's forecast, so the pooled patch shades all its provinces alike. The reproduction number is centred on one, and a province whose 90% predictive interval spans one is washed out.

Build the forecast maps

julia

province_forecast_draws(col) = [
    float.(province_projection[province_projection.patch .== p, col])
        for p in 1:N_PATCHES
]
province_forecast_map = plot_province_map(
    filter(
        !isnothing,
        [
            (;
                province_map_summary(province_forecast_draws(:rt_forecast))...,
                title = "Reproduction number at T+7", diverging_at = 1.0,
                colorbar_label = "R (median)",
            ),
            (;
                values = province_map_summary(
                    province_forecast_draws(:confirmed_new)
                ).values,
                title = "New confirmed cases by T+7",
                scale = CairoMakie.Makie.pseudolog10,
                colorbar_label = "Confirmed cases (median)",
            ),
            :isolation_level in propertynames(province_projection) ? (;
                    values = province_map_summary(
                        province_forecast_draws(:isolation_level)
                    ).values,
                    title = "Patients in isolation at T+7",
                    scale = CairoMakie.Makie.pseudolog10,
                    colorbar_label = "Patients (median)",
                ) : nothing,
        ]
    )
);

Forecast summary table by province

Province	Quantity	Lower 90%	Lower 60%	Lower 30%	Upper 30%	Upper 60%	Upper 90%
Ituri	New confirmed cases by T+7	164	217	250	314	352	433
Ituri	New confirmed deaths by T+7	66	92	110	142	164	201
Ituri	Patients in isolation at T+7	299	348	378	435	475	553
Ituri	Isolation beds at T+7	1015	1015	1030	1091	1140	1247
Ituri	New admissions by T+7	374	431	464	533	577	663
Ituri	New infections by T+7	189	283	357	515	625	946
Ituri	Reproduction number at T+7	0.5	0.66	0.77	0.97	1.09	1.37
Nord-Kivu	New confirmed cases by T+7	85	124	146	188	216	282
Nord-Kivu	New confirmed deaths by T+7	54	77	94	124	142	186
Nord-Kivu	Patients in isolation at T+7	188	222	243	282	307	354
Nord-Kivu	Isolation beds at T+7	354	354	354	357	372	404
Nord-Kivu	New admissions by T+7	240	277	301	347	375	432
Nord-Kivu	New infections by T+7	111	182	241	374	476	751
Nord-Kivu	Reproduction number at T+7	0.44	0.62	0.74	0.96	1.09	1.45
Haut-Uele	New confirmed cases by T+7	8	16	22	36	46	70
Haut-Uele	New confirmed deaths by T+7	1	5	7	15	21	37
Haut-Uele	Patients in isolation at T+7	30	38	43	52	58	70
Haut-Uele	Isolation beds at T+7	138	138	141	150	156	170
Haut-Uele	New admissions by T+7	39	47	53	64	72	89
Haut-Uele	New infections by T+7	17	30	41	65	89	156
Haut-Uele	Reproduction number at T+7	0.47	0.66	0.78	1.05	1.23	1.72
Other provinces	New confirmed cases by T+7	2	5	8	14	20	31
Other provinces	New confirmed deaths by T+7	0	1	2	5	8	15
Other provinces	Patients in isolation at T+7	19	23	26	28	28	30
Other provinces	Isolation beds at T+7	28	28	28	28	29	32
Other provinces	New admissions by T+7	23	27	28	31	33	36
Other provinces	New infections by T+7	8	15	20	35	51	97
Other provinces	Reproduction number at T+7	0.59	0.83	0.99	1.29	1.51	2.01

Forecast for each province

Each province has its own forecast figure, one histogram per target with its 90% predictive interval shaded. The dashed rule on the case and death panels is the count the province reported over its most recent week in the spatial tables.

Build the per-province figures

julia

# The spatial tables' most recent week in each province, clamped as the
# compositions are, so a downward revision reads as no new cases.
recent_cases = province_recent_counts(
    obs.province_confirmed_history, PROVINCE_NAMES, N_PATCHES
)
recent_deaths = province_recent_counts(
    obs.province_death_history, PROVINCE_NAMES, N_PATCHES
)
function forecast_province_observed(p)
    o = (;)
    recent_cases === nothing ||
        (o = merge(o, (; confirmed_new = recent_cases.counts[p])))
    recent_deaths === nothing ||
        (o = merge(o, (; confirmed_deaths_new = recent_deaths.counts[p])))
    return o
end
forecast_province_fig(p) = plot_province_forecast_detail(
    province_draws, province_projection;
    province = p, n_patches = N_PATCHES,
    observed = forecast_province_observed(p)
)
# The province blocks below are written out one per patch.
@assert N_PATCHES == 4 && PROVINCE_LABELS[1:4] ==
    ["Ituri", "Nord-Kivu", "Haut-Uele", "Other provinces"]
# Cases and deaths come from separate tables, so each stream's observed
# week is stated on its own.
function forecast_province_recent_week(r, stream)
    r === nothing &&
        return "The spatial tables carry no recent week of $(stream)."
    return string(
        "The observed week of $(stream) runs from ",
        grid_date(r.start_day), " to ", grid_date(r.last_day), "."
    )
end
recent_note = join(
    [
        forecast_province_recent_week(recent_cases, "confirmed cases"),
        forecast_province_recent_week(recent_deaths, "confirmed deaths"),
        "Each ends at its last spatial vintage, which can be earlier than " *
            "the cut-off.",
    ], " "
);

The observed week of confirmed cases runs from 2026-09-19 to 2026-09-26. The observed week of confirmed deaths runs from 2026-09-19 to 2026-09-26. Each ends at its last spatial vintage, which can be earlier than the cut-off.

Ituri

Nord-Kivu

Haut-Uele

Other provinces

The other provinces are pooled into one patch, so their forecast is for the pool.

Past forecasts against what was observed

Only province forecast projections are shown, so the figure fills in as releases accumulate. Each panel is one province stream at one horizon. The x-axis is the cut-off each forecast was made from. Each forecast shows its median and 90% predictive interval, beside the persistence baseline and the count the province went on to report. A window holding a harmonisation-break day is left out, because that day's backfill is published for the country and not by province.

Load the archived province forecasts and their outcomes

julia

# Written by `scripts/score_releases.jl` from each release's
# `province_forecast.csv`, in the national overlay's schema. A missing
# file reads as an empty table, which the figure reports as nothing scored.
province_overlay_df = _release_data(
    joinpath("province", "forecast_overlay.csv"),
    (;
        release = String, made_date = Date, stream = String, horizon = Int,
        target_date = Date, fit = String, observed = Float64,
        median = Float64, lo30 = Float64, hi30 = Float64, lo60 = Float64,
        hi60 = Float64, lo90 = Float64, hi90 = Float64,
    )
)
province_overlay_fig = plot_forecast_overlay(
    scored_overlay(province_overlay_df);
    empty_message = "No projection forecast has been scored yet. " *
        "This fills in as releases accumulate."
);

The full analysis code, data and model definitions are in the epiforecasts/BVDOutbreakSize repository.

Province forecasts ​

Summary ​

One-week-ahead forecast by province ​

Forecast for each province ​

Ituri ​

Nord-Kivu ​

Haut-Uele ​

Other provinces ​

Past forecasts against what was observed ​

Province forecasts

Summary

One-week-ahead forecast by province

Forecast for each province

Ituri

Nord-Kivu

Haut-Uele

Other provinces

Past forecasts against what was observed