Executive Summary
Spread Risk Assessments #1 and #2 quantified relative importation risk within the immediate cross-border region around the affected provinces of Ituri, North Kivu, and Kampala. This report focuses on the international tail, the relative ranking of destinations beyond the affected region, to support surveillance prioritization. The analysis uses the same mobility-based risk model used in Reports #1 and #2, with relative importation risk (RR) renormalized to sum to one across 197 international destinations (excluding the local cross-border area).
This analysis describes conditional ranking: it does not assert that an international export will occur, but rather, in the event of at least one international export occurring, indicates where it is most likely to be detected. The absolute probability of any single international export remains small.
Methods Overview
Mobility Data
Global air travel patterns are derived from the International Air Transport Association (IATA) and the Official Airline Guide (OAG), providing passenger-level origin–destination flows between international airports. These data are complemented by short-scale commuting patterns capturing daily movements between adjacent subpopulations, representing the local spread of disease through routine human activity within and across health zones. The modeling approach follows the GLEAM (Global Epidemic and Mobility) computational framework [4,5].
Relative Importation Risk
For each potential destination Y, the model estimates the probability P(Y) that an infected individual originating in the affected area travels to Y, conditional on at least one exported case occurring. The output is a relative risk distribution across all candidate destinations, normalized so that values represent the share of importation probability assigned to each location.
Renormalization to the International Tail
The model's native output is a relative-risk distribution P(Y) over all candidate destinations, conditional on at least one exported case occurring. To support surveillance prioritization, this report restricts the destination set to non-local international locations, excluding the immediate cross-border region around the affected provinces (Ituri, North Kivu, Kampala) covered in Reports #1 and #2, and renormalizes so the RR values sum to one across the 197 international destinations considered. Source provinces remain Ituri, North Kivu, and Kampala (the same multi-province footprint as Report #2).
Regional Distribution
Aggregating relative importation risk by region shows a concentration in East Africa, followed by a Middle East hub effect, with a long tail extending into Europe, Asia, and the Americas. African destinations account for roughly 69% of the conditional international risk, driven principally by Rwanda, Tanzania, and Kenya, the immediate non-local neighbours connected to the affected provinces by short overland routes and regional aviation links.
The Middle East accounts for approximately 15%, with Dubai alone contributing 10% of the conditional international risk, reflecting the role of Dubai International Airport as the dominant intercontinental hub serving East Africa. The remaining 16% is distributed across Europe (7.3%, concentrated in the United Kingdom), Asia (5.9%, concentrated in India and China), the Americas (2.0%, primarily the United States), and other regions.
City-level Analysis
The analysis is presented at two levels of granularity. Figure 1 shows the geographic distribution of country-level aggregate risk; Figure 2 ranks the top ten destination countries by aggregated relative risk. Figure 3 then shifts to the city level, providing the per-location ranking that informs surveillance prioritization. Bars in Figures 2 and 3 are coloured by region.
Figure 1. Country-level aggregate of conditional international relative importation risk. Countries shaded by the sum of location-level RR within their borders. Hover for values.
Within the international tail, conditional risk is concentrated in a small set of cities. The top-ranked destinations are Kigali (RR 17.6%), Dubai (RR 10.0%), Bukoba (Tanzania, RR 7.7%), Nairobi (RR 7.2%), and Kisumu (RR 6.5%), together accounting for nearly half of the conditional international risk. The full top-25 ranking is shown below.
| Top 25 international destinations · conditional relative risk | ||||
|---|---|---|---|---|
| Rank | City | Country | Region | RR |
Limitations & Assumptions
Model Scope
The mobility model does not incorporate sociodemographic attributes of travelers (age, economic status, occupation, or pre-existing medical conditions), any of which may modulate both exposure risk at origin and care-seeking behavior at destination. Travel probability within a catchment area is treated as independent of these attributes and of specific location within the catchment.
Case importations are modeled as statistically independent events. Real-world events involving multiple related cases (for example, a family cluster traveling together) are counted as a single importation event. To the extent that such clusters are common, the model will under-count the number of distinct exposure events while correctly counting the number of distinct geographic seedings.
Data Limitations
Mobility data may not fully capture informal cross-border movement, which is substantial within the immediate cross-border region but is not the focus of this report. For the international tail considered here, the model relies on scheduled and ticketed flight data (IATA / OAG) which provides coverage of intercontinental routes.
Epidemiological Assumptions
The incubation period of Bundibugyo virus (2–21 days; average ~10 days) constrains the window within which an exported case could reasonably travel while infectious or pre-symptomatic. The model does not predict downstream transmission chains following an importation event: risk estimates reflect the probability of a single importation and do not account for the probability of sustained local transmission in the receiving location.
Conditional vs. Absolute Risk
The values reported here are conditional on at least one international export occurring. They describe how international exportation probability is distributed across destinations given that an international export happens, not the absolute probability that any specific destination will receive a case. The unconditional probability of international export remains small.
References
- [1] World Health Organization Regional Office for Africa. Ebola Bundibugyo Virus Disease Outbreak — Democratic Republic of the Congo | Uganda. Weekly External Situation Report 01. Data as of 18 May 2026. Available at: afro.who.int
- [2] World Health Organization. Epidemic of Ebola disease caused by Bundibugyo virus in the Democratic Republic of the Congo and Uganda determined a public health emergency of international concern. WHO Statement, 16 May 2026.
- [3] Centers for Disease Control and Prevention. Ebola Disease: Current Situation. CDC Situation Summary, 18 May 2026.
- [4] Balcan D, Gonçalves B, Hu H, Ramasco JJ, Colizza V, Vespignani A. Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model. Journal of Computational Science. 2010;1(3):132–145.
- [5] Davis JT, Chinazzi M, Perra N, Mu K, Pastore y Piontti A, Ajelli M, Dean NE, Gioannini C, Litvinova M, Merler S, Rossi L, Sun K, Xiong X, Longini IM, Halloran ME, Viboud C, Vespignani A. Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave. Nature. 2021;600:127–132.
- [6] Wamala JF, Lukwago L, Malimbo M, et al. Ebola hemorrhagic fever associated with novel virus strain, Uganda, 2007–2008. Emerging Infectious Diseases. 2010;16(7):1087–1092.
- [7] International Air Transport Association (IATA). Passenger Intelligence Services. IATA, 2026.
- [8] Official Airline Guide (OAG). Aviation Analytics. OAG, 2026.