Ebola Bundibugyo Virus — DRC
Spread Risk Assessment · International Dissemination

This update refreshes the international-dissemination assessment for the 2026 Bundibugyo outbreak using the 23 July 2026 DRC data, over a two-week projection horizon. The framework is the same air-travel plus commuting mobility model used in Reports #1 to #4; no transmission dynamics are applied. The analysis is presented in two acts. First, proximity spillover into DRC's neighbouring countries at city level (each city is a metropolitan catchment inside the country's administrative geography): 99.2 % of the non-DRC RR mass sits in five cities — Kasese (UGA, 56.3 %), Pakuba (UGA, 25.4 %), Arua (UGA, 12.8 %), Juba (SSD, 3.1 %), Entebbe (UGA, 1.5 %) — with Rwanda, Tanzania, Central African Republic, and Burundi carrying a thin residual. Second, the international tail after removing only Uganda (96.1 % of the non-DRC mass) and renormalising over the remaining 117 countries (281 cities): under this scope South Sudan takes 80.1 % of the tail (Juba alone 79.6 %), followed by the UAE (3.2 %), Kenya (2.8 %), Tanzania (1.1 %), the United Kingdom (1.0 %), and Saudi Arabia (0.9 %). Regionally, the tail sits at Africa 89.4 %, Middle East 4.9 %, Europe 3.0 %, Asia 2.0 %, and the Americas 0.7 %. Values are conditional on at least one international export occurring; the unconditional probability of international export remains small, and country-level border-control measures are not modelled.

1   Executive Summary

This update refreshes the international-dissemination assessment using the 23 July 2026 DRC data. The framework is unchanged from Reports #1 to #4: air-travel and short-scale commuting flows are combined into a mobility-only relative importation risk (RR), evaluated over a two-week horizon. No transmission model is used at this stage; the analysis describes where any exported case is most likely to be detected, conditional on at least one export occurring.

The report is organised as two acts. The first characterises the proximity spillover into DRC's neighbouring countries at the city level (each city denotes a metropolitan catchment aligned to the country's administrative subdivisions). The second isolates the international tail by removing only Uganda, which alone carries 96.1 % of the non-DRC RR, and renormalising over the remaining 117 international destinations. All other neighbours (South Sudan, Rwanda, Tanzania, Burundi, Central African Republic, Zambia, Angola, Republic of Congo) are retained in the tail; South Sudan (Juba) then dominates the tail with ~80 %.

This analysis describes conditional ranking: it does not assert that an international export will occur, but, given that one occurs, indicates where it is most likely to be detected. The absolute probability of any single international export remains small.

2   Methods

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 modelling 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, normalised so that values represent the share of importation probability assigned to each location. This report uses the 23 July 2026 DRC snapshot and projects over the next two weeks; no transmission-dynamics model is applied.

Two-stage decomposition · neighbours and tail

This update splits the assessment into two views over the same underlying RR distribution. The first (Section 3) reports the city-level RR across DRC's nine neighbouring countries (before renormalisation), showing that Uganda alone accounts for 96.1 % of the non-DRC mass. The second (Sections 4 and 5) removes only Uganda and renormalises the remaining mass over 117 international destinations reachable by air. Under this scope, South Sudan (essentially Juba) dominates the tail with ~80 %; the remaining ~20 % is spread across the wider East-African, Gulf, European, and Asian networks. All other neighbours (South Sudan, Rwanda, Tanzania, Burundi, Central African Republic, Zambia, Angola, Republic of Congo) are retained in the tail.

Spatial units · cities and administrative geography

Model spatial units are city catchments defined around each country's populated centres (airports and their metropolitan draws). Each city sits inside the country's administrative geography — Ugandan districts, South Sudanese states, Rwandan provinces, Tanzanian regions, and so on. City-level RR aggregates naturally to country-level RR by summation.

Country-level border-control measures · caveat

The mobility model does not simulate country-level border-control interventions such as entry screening, restrictions on travellers from the affected area, or air-service suspensions. Any such measure would in particular reduce the absolute probability of an international export; the conditional ranking of where an export would land, given that one occurs, is less sensitive but still affected. Readers should treat the RR values here as the mobility-only baseline.

3   Neighbouring-Country Cities

Across DRC's nine neighbouring countries, the mobility model concentrates 99.2 % of the non-DRC relative risk mass in a small number of cities on the eastern side of the country. Five cities (Kasese in Uganda's Western Region, Pakuba in Northern Region, Arua in West Nile Region, Entebbe near Kampala; Juba in South Sudan's Central Equatoria State) together account for 99.2 % of the neighbour total. Rwanda, Tanzania, Central African Republic, and Burundi each contribute a thin residual (Kigali, Dar Es Salaam, Rumbek, Bangui, Bujumbura), reflecting their smaller connectivity to the affected area over the two-week horizon.

Values are the direct model output with only DRC itself removed; they are not renormalised. The neighbours together do not sum to one because the residual (0.7 %) spans destinations beyond the region. This section describes proximity spillover; because Uganda alone carries 96.1 % of the non-DRC mass, the international tail that follows removes only Uganda and renormalises the remainder. All other neighbours (South Sudan, Rwanda, Tanzania, Burundi, Central African Republic, Zambia, Angola, Republic of Congo) are retained in the tail. Note that under this scope Juba (SSD) dominates the tail with ~80 %.

4   Regional Distribution · International Tail

With DRC and Uganda removed and the remainder renormalised to sum to one, the tail is dominated by South Sudan: Juba alone carries 79.6 %, and Rumbek adds a further 0.5 %. This reflects the strong overland connectivity from north-eastern DRC into South Sudan captured by the mobility model. The rest of the tail (~20 %) is spread across the wider international network.

Africa carries 89.4 % of the tail (South Sudan alone 80.1 %; then Kenya 2.8 %, Tanzania 1.1 %, Rwanda 0.8 %, South Africa 0.8 %, Ethiopia 0.7 %). The Middle East accounts for 4.9 % (UAE 3.2 %, Saudi Arabia 0.9 %, Qatar 0.3 %), Europe for 3.0 % (United Kingdom 1.0 %, Germany 0.3 %, Belgium 0.3 %), Asia for 2.0 % (India 0.9 %, China 0.7 %), the Americas for 0.7 % (United States 0.5 %, Canada 0.2 %), and the "Other" residual sits at 0.1 %. The intercontinental picture is a mobility fingerprint of DRC's air connectivity through Nairobi and the Gulf, not a downstream-transmission forecast.

5   Destination Analysis · International Tail

At the city level, the tail is essentially defined by Juba: it takes 79.6 % of the mass. Behind Juba the leading cities are Dubai (ARE, RR 3.2 %), Nairobi (KEN, RR 2.3 %), London (GBR, RR 0.9 %), Kigali (RWA, RR 0.8 %), Dar Es Salaam (TZA, RR 0.6 %), and Johannesburg (ZAF, RR 0.6 %). The full top-30 ranking is shown below.

The geographic distribution shown in the world map (Figure 2) reflects country-level aggregate risk. Figure 3 ranks the top ten destination countries, and Figure 4 the top-30 destination cities. Bars are coloured by region.

6   Caveats & Limitations

No transmission model at this stage

This report uses the mobility framework only. No transmission-dynamics model is applied, and the RR values should not be read as forecasts of future case counts at any destination. They describe the mobility geometry of where an exported case would likely land, given that one occurs within the next two weeks.

Country-level border-control measures

The model does not include country-level border-control interventions such as entry screening, restrictions on travellers from the affected area, or air-service suspensions. Any such measure would in particular reduce the absolute probability of an international export; conditional destination ranking is less sensitive but still affected. The RR values here should be interpreted as the mobility-only baseline.

Model scope

The mobility model does not incorporate sociodemographic attributes of travellers (age, economic status, occupation, or pre-existing medical conditions), any of which may modulate both exposure risk at origin and care-seeking behaviour at destination. Travel probability within a catchment area is treated as independent of these attributes and of specific location within the catchment.

These estimates apply to traffic from the general population. The importation risk associated with repatriation flights, responder evacuations, or other ad-hoc operational movements is not part of this analysis.

Case importations are modelled as statistically independent events. Real-world events involving multiple related cases (for example, a family cluster travelling together) are counted as a single importation event.

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 the international-tail analysis in Sections 4 and 5. The model relies on origin–destination flight data (IATA / OAG) for the international tail and short-scale commuting for proximity spillover.

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.

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, and is further modulated by any border-control interventions not captured in the model.

7   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.

[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. J. Comput. Sci. 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. Emerg. Infect. Dis. 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.

[9] Reuters. Congo re-opens airport at the centre of Ebola outbreak. 2 June 2026.