Contributors

This work is led by the Institut National de Santé Publique (INSP) (Pierre Akilimali, Adelard Lofungola) and the Institut National de Recherche Biomédicale (INRB) Kinshasa/One Health Institute for Africa (INOHA) Kinshasa (Dav Ebengo, Placide Mbala-Kingebeni and Tania Bishola) in collaboration with the AfricaCDC and partners across the University of Oxford and Northeastern University; please contact dav.ebengo@umie-inrb.org or pierre.akilimali@insp.cd for further information.

Collaborating institutions and agencies

Institut National de Santé Publique (INSP)

National Institute of Biomedical Research (INRB)

Africa Centres for Disease Control and Prevention, Addis Ababa, Ethiopia

World Health Organization, Geneva, Switzerland

World Health Organization Country Office, Kinshasa, Democratic Republic of the Congo

Northeastern University, United States

University of Oxford, United Kingdom

Statement on continuing work and analysis before publication

Please note that the epidemiological data presented here is based on work in progress and should be considered preliminary. Our analyses are ongoing, and a publication communicating our findings is in preparation. Contextual data are publicly accessible; please refer to the original license when re-using these data. If you intend to use the epidemiological data prior to our publication, please contact Prof. Placide Mbala-Kingebeni (INRB, DRC), Prof. Dav Ebengo (INRB, DRC), and Pierre Akilimali (INSP).

Data

The dashboard was built predominantly using open-access, publicly available data, and some private linelist data hosted by the INRB.

Data sources

Data: https://github.com/INRB-UMIE/BDBV2026-Data/

Administrative boundaries are the DRC Ministry of Health zones de santé (519 zones) based on publicly available shape files from the Humanitarian Data Exchange, joined to a separate set of 300-dimensional Population Dynamics Foundation Model (PDFM) embeddings (520 zones) via DHIS2 codes and OpenStreatMaps reference IDs.

Per-region populations are sums of the GRID3 v4.4 population raster (spatial resolution of 3 arc-seconds, ~100-metre grid cells) within the boundary of each region per the HDX shapefile. Health-facility counts come from GRID3 COD - Health Facilities v8.0, joined by DHIS2 codes. Refugee/IDP site locations were pulled from OpenStreetMap via Overpass.

Pairwise driving travel times are obtained from the public OSRM service. Pairwise great-circle (haversine) distances are computed directly from the health zone centroids. PCR testing capacity data was made available by AfricaCDC. Outbreak counts (suspected/confirmed cases and deaths) are extracted from the latest INSP sit rep.

Methods

Risk assessment via stochastic metapopulation SEIR modeling

We implemented a discrete-time tau-leap Susceptible Exposed Infectious Removed (SEIR) metapopulation model that allows for overdispersed transmission. We partitioned DRC into 519 health zones. Each zone i is treated as its own well-mixed sub-population with a fixed total population N_i (from GRID3 v4.4) and its own per-zone S_i / E_i / I_i / R_i compartments. Zones interact through a mobility kernel M_ij. The state evolves in discrete daily time steps. At each step, every susceptible in zone i has a probability 1 − exp(−λ_i Δt) of becoming exposed, with the per-zone force of infection

λ_i = (1 − ε) · B_i / N_i + ε · Σ_j M_ij · B_j / N_j,

where B_i is the summed per-individual transmission rate over all infectious cohorts currently in I_i, and a mixing parameter ε ∈ [0, 1] sets the fraction of contacts drawn from outside the zone. Individuals exit the latent period (E→I) following a gamma-distribution with mean 10 days and shape parameter n_E = 2 (coefficient of variation ≈ 0.71). We implement the gamma distribution by splitting E into n_E sequential exponential sub-stages each with rate n_E·σ, where σ = 1/(10 days), such that the total time governing the E→I transition is Erlang(n_E, n_E·σ) ~ Gamma(shape=n_E, mean=10 d). The infectious period (I→R) is assumed to be exponential with a mean of 7 days.

Overdispersion is implemented at the individual level such that rate β_j is drawn from Gamma(k, R0/(k·D_inf)) when a transition happens from E→I. The result is that the per-individual distribution of secondary infections is roughly NegBinom(R0, k). Individuals entering I in the same time step are tracked as a cohort, and recoveries split the cohort's transmission rate exactly via the Beta thinning identity Gamma(a+b) → Beta(a, b)-fraction.

Between-zone coupling M_ij is a row-normalized convex combination of three independently constructed components. Each component is a normalized origin→destination matrix. The three components are a long-range gravity model, short-range gravity model, and empirical flow model. We use driving times to calibrate the gravity models and Flowminder data for empirical flows and combine them such that

M_gravity_ij = (1 − w_short) · M_long_ij + w_short · M_short_ij

if row i has empirical outflow:

M_ij = (1 − w_emp) · M_gravity_ij + w_emp · M_emp_ij

else:

M_ij = M_gravity_ij;

where M_long_ij = N_j^α · exp(−D_ij / scale_long), M_short_ij = N_j · exp(−D_ij / scale_short), α is the gravity model exponent, scale_long and scale_short are normalization factors, M_emp_ij are the empirical movements, w_emp a weighting factor, and D_ij is the pairwise distance, and N_j the destination population.

We consider a range of different initial conditions. From each set of initial conditions, an outbreak is simulated forward to the date of the latest situation report. We compute the per-zone reporting rate such that the observed case fatality rate is 30%. We run a grid search over R0 × k × ε × and emp_w. Each combination is run 1,000 times via stochastic simulation. Scoring is log-RMSE between the observed and reporting-rate-scaled simulated case counts. We pool across initial conditions and parameters using Approximate Bayesian Computation to identify an overall most-favored scenario and calculate credible intervals.

DRC Ebola Bundibugyo 2026