BVDOutbreakSize

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News ​

Release notes for BVDOutbreakSize. Major versions of the report are kept as GitHub Releases; each push to main also republishes the rendered analysis and the output/ artifacts.

v1.8.0 ​

Changes since v1.7.0.

Data ​

  • Added the daily contact-tracing follow-up rate ("taux de suivi des contacts", 7 June–1 July) as [contact_followup_history], recorded as a fraction from the confirmed-based situation-report banners. An observed proxy for surge-driven case-finding intensity.

Model ​

  • Scored the confirmed-case laboratory positives as an overdispersed BetaBinomial of the observed analysed denominator instead of a plain Binomial. A plain Binomial on denominators of several hundred specimens gave posterior-predictive intervals far too tight, so the confirmed stream was systematically under-covered: the smooth pooled / composition-linked per-window positivity does not capture the day-to-day laboratory batching and within-window positivity heterogeneity the confirmed counts carry. A single intra-window overdispersion ρ (confirmed_overdispersion_model, weakly-informative Beta(1, 24)) inflates each window's variance to n·p·(1 − p)·(1 + (n − 1)·ρ), identified across the laboratory windows, and recovers the Binomial as ρ → 0. The mean structure and the composition link that identifies the background λ_bg are unchanged.

  • The headline model now grounds the suspected-case surveillance in observed contact-tracing effort, on BOTH channels case-finding acts through. Case-finding intensity is a shared LATENT follow-up-rate process (contact_tracing_model): the observed daily contact follow-up rate is modelled as a logistic random walk q_t over the whole grid, fitted to the reported rate where it exists, so the anchor is defined before the reported window and projects into forecasts under its own dynamics rather than stopping with the data. The non-BVD background scales with it (exp(β_contact · (q − q̄)), contact_background_model), and the BVD suspected ascertainment carries a reporting-effort multiplier anchored to the same rate plus a random-walk deviation (exp(β_asc · (q − q̄) + w), reporting_effort_walk_model, suspected_reporting_effort = true). Contact tracing is a detection process, so it scales detection of both non-BVD and BVD suspects rather than shifting the outbreak size; the effort touches the suspected likelihood only, not the shared onsets or p_drc, so it does not reopen the ascertainment / outbreak-size degeneracy (addresses #374). This stops a change in case-finding reporting being read as a change in transmission (the reproduction-number decline in the joint fit).

Analysis ​

  • Two gated sensitivity re-fits on the sensitivity page: sens_no_contact (drop the contact grounding entirely) and sens_no_effort (drop the BVD ascertainment effort, keeping the contact-scaled background), each compared to the headline for the reproduction number and outbreak size.

Documentation and infrastructure ​

  • Excluded the published released_estimates.csv overlay from the fit content hash, so the render jobs reuse the matrix fits instead of refitting every model. The render step rewrites that overlay before rendering, which changed the data-tree digest and busted every fit cache key; the overlay feeds only the estimate-evolution figure and is not a fit input. tree_sha256 and content_hash gained an exclude for non-input data files, and genuine data changes still refit.

v1.7.0 ​

Changes since v1.6.0.

Data ​

  • Added the situation-report Tableau 6 treatment-centre patient-movement flows (CTE/CT/CI) as optional daily streams: admissions, in-care deaths, rule-outs and absconded patients (13–23 June). Each stream is resilient: an empty history is a no-op, so the model degrades to the occupancy backbone where a flow is not reported. Advanced the data through situation report 046 (29 June).

Model ​

  • Reworked the isolation submodel into a treatment-centre flow model that fits the Tableau 6 flows alongside occupancy. The bed length-of-stay is an outcome mixture, with the death and recovery branches weighted by an in-care case-fatality CFR_iso = logistic(logit(CFR) + β_iso) — a reported modifier on the infection case-fatality, identified by the in-care death flow rather than estimated independently. The daily discharge flows are scored as optional negative-binomial streams (resolves #338).

  • Added a manual, opt-in occupancy reclassification-break offset. Break days are listed explicitly in [occupancy_break_dates], replacing the removed threshold detector that flagged too many days. A level step is fitted into the modelled occupancy mean at each listed day, so the fit tracks a known between-report measurement-basis discontinuity without bending Rt to chase it. Each step is centred on zero, so the fit partitions it into reporting artifact vs real demand. The 19 June DHIS2 reclassification (occupancy 416 → 361, confirmed by the au-lit start-of-day stock) is listed; the joint fit with the isolation stream had been bending Rt up and down to chase this and the later missing-SitRep steps, which no single-stream fit shows.

Report and forecasts ​

  • Added one-week-ahead forecasts for the treatment-centre admissions, in-care deaths and rule-outs.

  • Added a per-stream calibration plot (with the calibration table kept in a collapsible block), and posterior-predictive panels for the four flow streams. The treatment-centre flow methods section was rewritten, and the flow streams added to the data-overview table.

  • Added a comparison of the confirmed-case projection against Chamla et al. as a second external comparator, forward-projected from a dedicated frozen fit at their 8 June confirmed-case calibration anchor. This carries the confirmed-case testing history, replacing the poorly-identified 27 May proxy that had effectively no testing data. The 8 June fit also shows as a vintage in the estimate-evolution overlay (resolves #340, #349).

  • Refreshed the released-estimate evolution overlay to v1.6.0 and refresh it automatically in continuous integration before each documentation deploy. Dropped the per-release current-model re-fits from the estimate-evolution plot, keeping the matched-McCabe cut-offs and the one-week-back validation fit (resolves #341).

  • Tightened the reproduction-number plot y-axis to 1.2 times the 90% upper bound so the credible band is legible (resolves #342).

  • Added a posterior correlation heatmap across the key estimates (outbreak size, reproduction number, outbreak age, CFR, ascertainment, background, fraction tested and each stream's expected total) and a pairs plot of the per-stream modelled totals against each other and the observed value, so the size-versus-ascertainment trade-off and per-stream over/undershoot are visible in one place (resolves #346).

Performance ​

  • Halved the NUTS warmup: nuts_sample now defaults n_adapts to min(250, samples ÷ 2) (250 adaptation steps at the standard 1000 draws) instead of Turing's min(1000, samples ÷ 2) (500), cutting the discarded warmup iterations on every report fit. Pass n_adapts explicitly to override.

  • Centred the per-stream pooled negative-binomial dispersion (pooled_dispersion_model) instead of drawing it non-centred, and made centred the default. The surveillance streams are data-rich, so the non-centred pooling funnelled as τ → 0; the centred form is an exact reparameterisation that removes the funnel (resolves #352).

  • Put the bed-capacity baseline C0 on the log scale (LogNormal(log 450, 0.42) in bed_capacity_model and bed_capacity_walk_model) instead of a truncated normal, so the whole capacity C(t) = C0·exp(walk) is log-consistent with no hard boundary, improving the worst-mixing capacity block (resolves #358).

Fixes ​

  • Relaxed the tau_death test assertion to non-negativity: the joint exposes the realised cut-off death-testing intensity (analysed over suspected, a diagnostic computed independently of the death volume), which is not a probability and can exceed one in a backlog regime.

  • Widened the per-stream dispersion pooling-SD prior τ in pooled_dispersion_model from HalfNormal(0.3) to HalfNormal(0.6), resolving a prior-data conflict where the posterior τ sat entirely above the old prior's tail because the stream dispersions genuinely span ~9× (resolves #336).

  • Fixed the one-week-ahead forecast to project new counts over the horizon and add them to the cut-off cumulative, instead of scaling the cumulative stock by exp(r·horizon), which made a below-one reproduction number imply an impossible shrinking cumulative in the Chamla comparison (cases_cum, deaths_cum, confirmed_cum, confirmed_deaths_cum and recovered_cum; resolves #351).

Documentation and infrastructure ​

  • Split the report into two literate pages rendered from a shared setup: an analysis page (methods, results, one-week-ahead forecast) and a sensitivity page (forecast validation, per-stream outbreak size, estimate evolution, McCabe and Chamla comparisons, delay and clock sensitivity), so the deploy no longer renders one 4.3k-line file in a single job (resolves #364).

  • Fanned the documentation build into a list → fit → render → combine CI grid: one content-addressed, cached NUTS fit per matrix job, the two pages rendered in parallel from the cached chains, and a combine job that deploys and publishes, dropping the critical path from all fits serialised behind one runner to roughly the slowest single fit.

  • Pinned the shared workspace-root Manifest.toml as the artifact the docs grid uploads and restores, since docs/Project.toml is a workspace member and resolves the root manifest rather than docs/Manifest.toml, so the fit, render and combine jobs resolve the same package set (resolves #368).

  • Set include-matrix: false on the render job's env-cache step so the two render jobs restore the precompiled depot shared by list and fit instead of keying on the matrix page, which missed the shared cache and made each render re-precompile the whole stack (~530 deps) before rendering.

v1.6.0 ​

Changes since v1.5.0.

Model ​

  • The isolation BVD treatment length-of-stay uses the BDBV line-list admission-to-death delay as its prior.

  • The reproduction-number random walk starts a month before the first situation report (RT_WALK_LEAD = 28, exposed as the bvd_joint keyword rt_walk_lead), so R_t can move over the weeks of transmission leading up to that report. The walk start is floored at the renewal start, and the plot_rt reconstruction uses the same knot grid.

  • Added a supply-limited isolation/treatment-bed stream ("Patients en isolement"), the renewal analogue of the convolution secondary-observation model of EpiNow2. Bed occupancy may be supply-driven (demand can outstrip supply), so the model fits a latent bed demand, the suspect inflow carried through a length-of-stay survival (BVD cases with a sampled treatment stay, non-BVD suspects leaving after a sampled rule-out stay), right-censored at an effective bed capacity ρ·C(t) (a censored negative binomial). The capacity C(t) is a random walk (bed_capacity_walk_model) that tracks the beds being added and can be projected forward, pinned by the implied bed count (reported occupancy / "Taux d'occupation" rate) on the days a rate is published. The stream exposes the bed demand, occupancy, capacity, shortfall and utilisation, and carries its own observation dispersion. Added convolve_survival, the treatment_admission_model observation submodel, the isolation_admission_model, bed_capacity_model and bed_capacity_walk_model priors and the treatment_only_model single-stream composer (resolves #265). This is a single national model, so it cannot represent local bed saturation (Ituri at 93.9% occupancy on 13 June against Sud-Kivu 21.9%); the national shortfall understates the local unmet need.

  • Gave the non-BVD isolation rule-out stay its own sampled length-of-stay (ruleout_los) in treatment_admission_model, separate from the report-to-receipt laboratory delay, so the occupancy identifies the rule-out stay on its own clock and the lab-turnaround delay is set by the testing, composition and confirmed-death streams. Exposes isolation_ruleout_los_mean.

  • Added a recovered-among-confirmed stream ("cumul guéris"), the secondary-observation incidence analogue: survivors among the modelled daily confirmed cases, scaled by a recovery proportion and convolved with a confirmation-to-recovery delay, with its own observation dispersion. The recovery proportion is grounded on the case-fatality ratio (a recovered case is one that did not die) with a log-odds adjustment for the confirmed population, rather than estimated independently. The confirmed model exposes one daily confirmed-case series (confirmed_daily) that both the recovered stream and the cumulative- confirmed trajectory reuse. Added the recovered_model submodel and the recovery_probability_model prior.

  • Added the exports_joint_only_model composer, which fits the Uganda export cases and deaths together over the one travel-gated at-risk prevalence. The single-stream comparison in the walkthrough now shows one joint "exports" fit instead of separate export-case and export-death fits.

  • The one-week-ahead forecast also projects the isolation/treatment beds: the bed demand a week ahead (need under unconstrained supply) and the supply-limited occupancy it produces, whose gap is the projected bed shortfall, and the cumulative recovered total, each replicated with its own dispersion. Added plot_forecast_beds, which shows the projected bed need against the supply-limited occupancy and the shortfall in the walkthrough's forecast section.

  • The forecast-versus-frozen validation now also scores the isolation beds: the frozen one-week-back fit conditions on the isolation occupancy, and the projected bed occupancy is compared against the beds actually held a week later (forecast_vs_truth gains an isolation argument, and plot_forecast_beds_vs_truth plots the projected occupancy against the observed beds). The bed check is weak at a one-week-back freeze because the reported occupancy rate starts only on 9 June, so the capacity rides its random walk back to the freeze date.

  • Replaced the per-vintage step background random effect with a smooth daily lognormal random walk (background_walk_model): a per-day background with no reporting-vintage steps, gated to begin a report-to-receipt lead before the first suspected-case report, shared across the suspected-case and suspected-death streams, with a half-normal baseline and a tight random-walk innovation SD. This also removes the per-vintage step in the modelled cumulative-death trajectory.

  • Widened the non-BVD background level prior so the laboratory positivity (210/755 ≈ 0.28 positive) identifies it. The suspect pool is inferred to be a minority BVD, which lowers the cumulative-infection estimate (C_T) with a wider credible interval.

  • Gated the laboratory analysed-specimen capacity to the testing onset, so no specimens are modelled as analysed before testing existed.

  • Redesigned the death pathway. Suspected deaths carry a death ascertainment p_death (the death analogue of the case ascertainment, with an informative prior centred high) and a non-BVD death background that applies a background CFR (cfr_bg) to the suspected-case background and lags it by the onset-to-death delay, so a background death follows its background case. The death background tracks the identified case background rather than a second free, outbreak-size- degenerate rate, and inherits the case background's smooth gated daily shape, so the modelled cumulative-death trajectory is smooth. Added the death_ascertainment_model and background_cfr_model priors.

  • Rebuilt the confirmed-death stream as a laboratory pipeline mirroring the confirmed cases. The death analysed volume scales the modelled case analysed volume at the per-day suspected death-to-case ratio, times a testing-intensity scaling (LogNormal(0, 0.25), centred on one), so death testing follows the laboratory's realised throughput; the death-to-case ratio carries the suspect-pool severity and the suspected-death level. The volume is scored through a death-pool composition positivity p = s·q_death + (1−spec)(1−q_death), with q_death the BVD share of the suspected deaths from the death series' own components. The case volume carries the laboratory capacity onset, so the death volume inherits it and no deaths are confirmed before testing began. The joint exposes the death_ascertainment, background_cfr, death_testing_scaling, tau_death and death_composition deterministics and drops m_death.

Data ​

  • Added the daily "Patients en isolement" occupancy for 1-11 June (SitReps 018-028) as a structured patients_isolated column and the [isolation_history] manifest block. The fitted series begins 1 June where the all-patients column definition is stable; the narrower suspects-only count in SitReps 016-017 is a different quantity and is excluded. Corrected the SitRep 020 note (the PDF headline occupancy is 233, not the 173 the note claimed).

  • Added the implied bed-capacity series (occupancy / reported "Taux d'occupation" rate ≈ 400-452 beds, 9-13 June) as the [bed_capacity_history] manifest block, which pins the bed capacity in the supply-limited isolation model.

  • Added the cumulative "cumul guéris" recovered-among-confirmed total for 6-11 June (SitReps 023-028) as a structured cumul_recovered column and the [recovered_history] manifest block.

  • Added the peer-reviewed McCabe et al. Lancet Infectious Diseases publication (online first 9 June 2026, DOI 10.1016/S1473-3099(26)00299-9) as a third scenario vintage in REPORT_SCENARIOS_CI, with inputs as of 27 May 2026 (1031 DRC cases, 240 deaths, three Uganda imports). Both methods now vary the epidemic doubling time (7/10/14 d); the back-calculation assumes 30% of deaths are attributable to Ebola. The published paper swaps the method numbers relative to the Imperial reports, which the noted convention reconciles. Recorded the matching frozen-data snapshot in data/report-snapshot-27may.toml.

Analysis ​

  • The walkthrough adds posterior-predictive panels for the isolation occupancy and recovered streams, a single-stream "in isolation" fit for the isolation occupancy, and surfaces the isolation length-of-stay and confirmation-to-recovery delays in the observation-delay table and pair plot, and the admission proportion, recovery probability and the two new per-stream dispersions in the surveillance-parameter table.

  • Cite EpiNow2 (Abbott et al., 2020) for the convolution-and-scaling secondary-observation analogy, and fix the epinow2 bibliography entry so the documentation build no longer warns about a missing field.

  • The McCabe et al. scenario comparison now carries a third vintage, the 27 May 2026 Lancet publication, plotted beside our renewal estimate on 27 May, with a frozen re-fit at the 27 May cut-off added to the frozen-fit outbreak-size table.

  • Quantified the per-vintage posterior-predictive checks with a per-stream calibration table (stream_calibration): the mean forecast bias and the empirical 50%/90% interval coverage of each stream's one-step-ahead conditional predictive, so the streams the joint fit reproduces less well can be read off rather than eyeballed. Added the bias_sample scoring helper (resolves #269).

Documentation ​

  • Added a one-page Summary dashboard for readers with limited time: the headline estimates as prose and tables alongside the reproduction number, infections-over-time and modelled-versus-observed reported-case figures. It reuses the artifacts written by the analysis build rather than re-fitting, so it refreshes whenever the data updates.

Outputs ​

  • Added the latent symptom onsets (the "symptomatic cases" outcome) to the shared posterior outputs. posterior_draws.csv gains a cumulative_onsets_T column, the cumulative symptom onsets by the cut-off per draw (the onset analogue of C_T), and a new onsets_over_time.csv records the daily new and cumulative onset trajectory over time with 30/60/90% credible intervals. Exposed through onsets_over_time.

v1.5.0 ​

Changes since v1.4.0.

Model ​

  • Confirmed deaths now carry the report-to-receipt laboratory delay, so the laboratory-confirmed-death series lags the death event rather than tracking it instantaneously and the confirmed case and death streams pay a consistent laboratory delay.

  • Added an optional daily new-suspect inflow stream ("nouveaux cas suspects du jour") to the suspected-case likelihood. The post-26 May per-day counts are scored against the modelled daily suspected series at each report day, continuing the suspected signal where the frozen cumulative series stops, on days disjoint from it (#222).

  • Added the deaths analogue, an optional daily new suspected-death inflow stream ("cas suspects du jour N (M deces)") to the suspected-death likelihood. The post-26 May per-day counts are scored against the modelled daily suspected-death series at each report day, continuing the suspected-death signal where the frozen cumulative series stops, on days disjoint from it, and a matching "New suspected deaths/day" posterior-predictive panel is added alongside the new-suspects-per-day panel.

  • Collapsed the laboratory pipeline onto a single suspected-to-analysed volume, fit to the specimens-analysed series through one report-to-analysed delay. The received stream is still recorded but no longer fitted, and the post-cut-off 24-hour analysed volume is now fit directly.

  • Late reporting windows, where the cumulative national analysed denominator stops, are scored in one submodel. A day with a published 24-hour analysed count anchors its positivity as a binomial on that count, and the remaining days are scored against the modelled laboratory volume. These are a reporting-format change rather than data blackouts, so the earlier "dark window" framing is dropped.

  • Added the delay-corrected confirmed case-fatality ratio, the Nishiura et al. (2009) real-time correction computed per posterior draw on the modelled confirmed trajectory and sampled confirmation-to-death delay. The denominator shrinks from all confirmed cases to those expected to have had a fatal outcome resolve by the cut-off, debiasing the naive confirmed ratio.

Forecast ​

  • The one-week-ahead forecast and its validation now target the laboratory-confirmed case and confirmed death streams. The suspected reported cases and deaths are no longer published, so they no longer serve as forecast targets or as the last-week-versus-now comparison.

Report ​

  • Added a confirmed case-fatality ratio section, setting the delay-corrected confirmed CFR against the structural infection-based CFR and the naive confirmed ratio, with a comparison table and posterior-density plot.

  • The estimate-evolution figure now draws each release as a discrete per-fit estimate with nested 30/60/90% intervals, read from data/released_estimates.csv rather than a hand-maintained literal. Frozen renewal re-fits are restricted to the integral-era release cut-offs, since renewal-era releases already are renewal fits. A new scripts/refresh_releases.jl pulls the per-release estimates from the tagged results releases.

Data ​

  • Captured the daily new-suspect counts (SitReps 021-024, 4-7 June) as a structured new_daily_suspects column in the scanned situation-report CSV and a suspected_daily_history block in the observation manifest.

  • Captured the daily new suspected-death counts ("cas suspects du jour N (M deces)", SitReps 024-032, 7-15 June) as a structured new_daily_suspected_deaths column in the scanned situation-report CSV and a suspected_daily_deaths_history block in the observation manifest, the deaths analogue of the daily new-suspect inflow.

  • Extended the confirmed case and death series to SitRep 025 (8 June).

  • Added the trusted-day 24-hour analysed laboratory counts (1, 4-7 June) as a tests_analysed_daily_history block to anchor late-window positivity.

v1.4.0 ​

The methods switch flagged in v1.3.0: the continuous-time, fixed-growth-rate model is replaced by a discrete-time renewal model that is simpler and avoids the single-stream-versus-joint size tension of issue #212. This is a substantial revision; the changes below are relative to v1.3.0.

Model ​

  • Replaced the integral exponential-growth model with a discrete-time renewal process on a daily grid. Infections follow the renewal equation under a time-varying reproduction number (a weekly log-scale random walk with an intervention ramp), and every observed stream sits downstream of latent onsets through its own sampled, discretised delay.

  • The prior is placed on the growth rate (the molecular-clock doubling time) and the first reproduction number is derived forward through Euler–Lotka. The generation interval is a Gamma with shape and scale taken from the cited source and its reported uncertainty.

  • The onset-to-event delays are taken from a Bayesian reanalysis of the 2012 Isiro line list on their natural Gamma parameters, with one onset-to- admission delay serving both suspected-case reporting and export detection and onset-to-death the convolution of two atomic components.

  • Two-phase seeding: a single import grows through an unobserved cryptic exponential phase to the renewal start, with the outbreak age bounded by the genetic time to the most recent common ancestor.

  • Confirmed positivity is tied to the suspect-pool composition through an assay sensitivity and specificity, and exports are travel-gated from infection and scored on their dated detection days.

  • The DRC streams are fitted on the incidence scale, as the between-vintage increments across successive situation reports (the first vintage being the cumulative count to that date).

Report ​

The report was rebuilt around the renewal model; the analyses carried over from v1.3.0 (the one-week-ahead forecast and its validation, the no-onward-transmission counterfactual, the delay and clock-rate sensitivity analyses, and the McCabe et al. comparison) were re-implemented for the new model rather than added here.

  • Restructured the methods in generative order (infections, epidemiological processes, observation models, the joint model) with the model maths given explicitly.

  • Reworked the figures (reproduction number with credible ribbons and sampled trajectories, cumulative infections, onsets and deaths, outbreak size by data stream, and estimate evolution across releases).

  • The McCabe et al. scenario comparison now carries their reported 95% confidence intervals.

  • The one-week-ahead forecast and its last-week-versus-now validation now target the laboratory-confirmed cases and confirmed deaths. The suspected reported cases and deaths are no longer reported, so they are dropped as forecast targets.

Data ​

  • Advanced the cut-off to 7 June 2026 (SitRep 024). The laboratory-confirmed streams run to the cut-off while the suspected streams stay frozen at their 26 May values.

v1.3.0 ​

Final release of this model formulation

This is the last planned release of the continuous-time, fixed-growth-rate model. The laboratory and testing observation model has outgrown the available data, and the joint fit now implies a larger outbreak than any single data stream does on its own (issue #212). We are replacing this model with a discrete-time renewal model, which is simpler and avoids these problems, in a follow-up release that will note the methods switch. Treat the estimates here as provisional.

Changes since v1.2.0.

Data ​

  • Advanced the model cut-off to 28 May 2026 and switched the DRC streams to the INSP national cumulative totals read from the situation-report PDFs, rather than the per-zone CSVs whose zone sums drop cases not yet attributed to a zone. The suspected streams are frozen at their 26 May values, after which INSP stopped publishing a national suspected total; the confirmed and laboratory streams run to 28 May.

  • Added per-sitrep-vintage confirmed cases, confirmed deaths and laboratory throughput (samples received and analysed) to data/observations.toml, alongside the suspected cases and deaths.

  • Extended the observed series through 5 June 2026 to validate the forecast out of sample.

Modelling ​

  • The joint model now fits four DRC streams per sitrep vintage (suspected cases, suspected deaths, laboratory-confirmed cases and laboratory-confirmed deaths) by conditioning on the between-vintage increments, alongside the Uganda exports and export deaths. A single-vintage stream reduces to the cumulative likelihood.

  • Confirmed cases are fitted through a laboratory-throughput queue: suspects enter a received backlog after a report-to-receipt delay, a capacity-limited drain sets the samples analysed, and the new positives in each window are a Binomial on the samples newly analysed. Windows with no published analysed count fall back to the queue's expected throughput, so no free per-window denominator is introduced.

  • Test positivity is severity-first: early specimens skew toward severe presentations and relax toward the latent case composition as analysed volume accrues.

  • Suspected cases and deaths are BVD onset-to-report convolutions plus additive non-BVD background rates; confirmed deaths share the case-lab PCR sensitivity and specificity.

  • Split ascertainment into independent DRC and Uganda reporting fractions (DRC centre 0.75), and recentred the growth prior on the molecular-clock 20-day doubling time ((Cuomo-Dannenburg and Ghafari, 2026)).

  • Exports and export deaths are timed from infection via an infection→detection delay convolution rather than a rectangular detection window, reducing to the McCabe et al. window as the delay collapses to a point mass.

  • The headline estimand is cumulative infections (2^m), with the under-ascertainment multiplier anchored on the laboratory-confirmed cases.

Outputs ​

  • Posterior summary table, a laboratory-pipeline pair plot, and posterior-predictive panels for the confirmed-case and confirmed-death streams in the per-stream-versus-joint grid.

  • Recast the forecast around the four trusted quantities (infections, true BVD deaths, confirmed cases, confirmed deaths) over a one-week-ahead and a counterfactual-year horizon, dropping the untrusted suspected and tests-analysed streams.

  • Restored the forecast validation as a last-week-vs-now out-of-sample check: fit the joint through 28 May, forecast forward, and score the predicted confirmed cases and deaths against the observed counts.

  • Added a conditional one-step-ahead predictive across the sitrep series, each vintage predicting only its new increment.

Documentation ​

  • Surfaced the delay priors as equations, clarified that the latent pool is the true-case count rather than the tested or confirmed count, and added limitations on the constant-growth assumption and on per-sitrep increments mixing incidence with backfill.

Infrastructure ​

  • Added streaming progress to nuts_sample via an optional callback (a dependency-free file stream or TensorBoard), and optional Enzyme reverse-mode AD alongside the default Mooncake backend.

v1.2.0 ​

Modelling ​

  • Improved the comparison to the McCabe et al. report by making sure that 95% credible intervals are being compared and reordering it.

  • Added a custom chain rule for SpecialFunctions.gamma_inc. This allows us to differentiate through the analytical solution to the gamma convolution integral.

Data ​

  • Moved the cut-off to 23 May 2026 and switched the DRC source from the WHO AFRO joint sitrep to the situation reports of the Institut National de Santé Publique (INSP), transcribed by INRB-UMIE/Ebola_DRC_2026. The INSP series gives a per-zone, per-sitrep daily vintage trajectory (suspected and confirmed; this analysis uses suspected). Cumulative counts at 23 May: 905 suspected DRC cases, 220 suspected DRC deaths, across the 12 reporting health zones. The 18 May INSP vintage (516 cases, 131 deaths) matches the WHO joint sitrep 01 total exactly.

  • Updated Uganda to three travel-related imports with one death, reflecting the third import announced on 23 May 2026 (woman from DRC who travelled Arua to Entebbe to Kampala; tested positive on follow-up). Two further Uganda-confirmed cases announced the same day (a driver and a healthcare worker) are domestic contacts of the first import and are excluded from exported_cases because the model treats Uganda as imports only.

  • Added a reported_case_history block in data/observations.toml with eight INSP sitrep vintages (14 May to 23 May 2026), ready for the cumulative-trajectory likelihood once it merges.

Infrastructure ​

  • Moved the submodels out of the analysis file and into the supporting package. Instead we now print these in the analysis.

  • Added additional package infrastructure including Aqua.jl and Jet.jl.

  • Streamlined the package unit tests.

v1.1.0 ​

Modelling ​

  • Bound the seeding time T from below with a soft prior on the genetic time to the most recent common ancestor (TMRCA), following a suggestion from Neil Ferguson to combine the genetic signal with the other data streams as a seeding bound.

  • Switched the export deaths to a daily (time-resolved binned) Poisson process: a continuous survival weight for the no-death stretch before the first dated death, then a per-day Poisson from that day to the cut-off.

  • Bound T with export-death timing through that survival weight, and with case-export detection timing through a first-export-detection survival term on the Uganda admission date. Dates supplied in data/observations.toml.

  • Death-convolution quadrature adapted to the sampled delay scale.

  • Added a clock-rate sensitivity: refit the joint model under the faster 1.9e-3 early-epidemic TMRCA estimate and compare the impact on outbreak size, seeding time and growth rate against the 1.2e-3 baseline.

  • Sped up the deaths-among-exports likelihood: precompute the onset-to-death CDF once and reuse it across bin edges (ExportDeathDelay), replacing the per-node nested quadrature.

  • Removed hardcoded death and case constants that diverged from the observations in data/observations.toml.

  • Added a forecast validation: fit the joint model to the original report's data, project it forward to the current cut-off, and compare the predicted cumulative and new counts per stream against the counts observed since, as a table and a 2×3 coverage plot.

Data ​

  • Updated to the McCabe et al. 20 May 2026 report, comparing both report versions.

  • Sourced the genetic TMRCA seeding bound from the BEAST temporal-tree estimate in the 2026-05-21 virological.org update (mean 2026-03-25, 95% HPD 2026-02-20 to 2026-04-20, at the 1.2e-3 EBOV clock rate this analysis assumes).

Infrastructure ​

  • Dropped MCMCChains for FlexiChains and prepared for registry release.

  • CI docs preview PR comments and version-bump automation.

Docs ​

  • Added a scope note to the README and analysis report framing the work as an external view built on our understanding of real-time infectious disease dynamics, and inviting feedback, reuse and adaptation.

  • Surfaced results from the README and analysis landing page, added stable and dev docs badges.

  • Plotting and labelling fixes: surveillance dispersion on the 1/√k scale, predictive histograms labelled as frequency, and coarser (four-weekly) start-date axis ticks so the labels stay readable.

  • Reworked the headline summary to report the credible intervals as sentences rather than leading with a median, defined the prior-IQR shift, and explained the reported-case scaling in terms of the DRC reporting fraction with a link to the pair plot.

  • Replaced the model-structure diagram with a parameter-to-observation table.

  • Culled promotional register in the analysis report.

v1.0.0 ​

First release. A joint Bayesian re-analysis of the McCabe et al. report that fits all data streams together in a single Turing model over the latent cumulative case count.

  • Conditions on the exported cases and DRC deaths the report uses, plus reported DRC cases (with an ascertainment component) and deaths among exported cases.

  • Adds a no-onward-transmission projected-deaths counterfactual, a one-week-ahead forecast of newly reported cases, deaths and exports, and an onset-to-death delay sensitivity analysis.

  • Replaces the deaths-convolution and small-growth-rate exports closed-form approximations with their exact forms.

  • Maths-first analysis page with code folded behind dropdowns and a diagram of the model build-up.

  • Compares against a joint reimplementation of the report's approach and its original published estimates.