Modeled Scenario Projections for the Ebola Disease Outbreak Caused by Bundibugyo Virus, 2026

Methods

CDC used a model to simulate BVD outbreaks. The model was adapted from one applied to previous viral hemorrhagic fever outbreaks, including a Marburg virus disease outbreak in Ethiopia in 2025. In this model, each simulated outbreak was initialized with one infected person, who represented the person first infected from a zoonotic source (a spillover event). This person infected a randomly generated number of additional persons based on assumptions about the basic reproductive number ([R₀], the average number of persons in a susceptible population infected by an infected person). Any infected persons were added to the simulation at times selected according to the distribution of intervals from one infection to the next and, in turn, were able to cause further infections. This simulation, called a branching process, continued until either 1) none of the infected persons in a generation caused any secondary infections, indicating termination of the outbreak or 2) the simulation reached 5,000 deaths, indicating a very large and exponentially growing outbreak.

Time Intervals

Intervals from infection to symptom onset, symptom onset to death, and symptom onset to recovery were held constant for all infections within each simulated outbreak but varied among simulated outbreaks. Simulated persons were never infectious before symptom onset or after recovery but could be infectious after death.

Assumptions about parameters were based on published estimates from previous Ebola outbreaks (Supplementary Box). Estimates specific to BVD were used when available.

Model Calibration to Assumed Number of Deaths

Assumptions for the cumulative number of BVD deaths as of May 24, 2026, were based on publicly available situation reports from DRC.* The model was calibrated to three different numbers of cumulative deaths (50, 100, and 200) to account for uncertainty in the current number of deaths caused by BVD.

A simulated outbreak was compatible with the real-world outbreak if it reached the assumed number of cumulative deaths by May 24, 2026, and if the first death occurred on or before April 24, 2026. Outbreaks were simulated until 500 simulations met these criteria. The accepted 500 simulated outbreaks were used to infer when the outbreak began and served as the basis for scenario projections of interventions for each model calibration.

Scenario Projections for Isolation

Four intervention scenarios were assessed for each calibration, each implementing a different level of isolation (i.e., percentage of symptomatic infected persons detected, isolated, and treated: 20% [poor], 50% [moderate], 70% [high], and 95% [extremely high]). The extremely high scenario was chosen to estimate a lower bound for transmission.

The intervention was assumed to start on May 24, 2026. On that day in each simulation, the designated percentage of symptomatic persons was selected to begin isolating, with an average delay of 2 days until isolation and treatment. The same percentage of persons who later developed signs or symptoms was selected to begin isolating, with an average delay of 2 days from symptom onset. Simulated persons in isolation were prevented from causing any onward transmission; the model implicitly assumed that isolated persons who died were safely buried (i.e., without washing or embalming and buried by trained teams using personal protective equipment).

Each simulation reported the cumulative number of cases and cumulative number of deaths from the date of spillover until August 22, which would be 90 days after interventions began. The percentages of simulations with <10,000, 10,000–19,999, and ≥20,000 cases and with <2,000, 2,000–3,999, and ≥4,000 deaths were calculated for all simulations in each scenario and separately for those with an R₀ less than or equal to and greater than the median R₀ value. The effective reproductive number (R_e, the average number of onward infections per infectious person, accounting for immunity and public health interventions) was calculated for the preintervention and postintervention periods.

The branching process model was written in Rust (version 1.95.0; The Rust Development Team), and the model calibration and scenario projection pipeline was written in Python (version 3.14.4; Python Software Foundation). This activity was reviewed by CDC, deemed not research, and conducted consistent with applicable federal law and CDC policy.^§