Executive Summary
This report updates the international-dissemination assessment using the 7 October 2026 DRC data. The framework is unchanged from Reports #1 to #4 and #6: 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 an urban catchment aligned to the country's administrative subdivisions, such as Ugandan districts, South Sudanese states, Rwandan provinces, and Tanzanian regions). The second isolates the international tail by removing Uganda, which alone carries 98.0 % of the non-DRC RR, and renormalising over the remaining 120 international destinations, so that longer-range air-mediated risk becomes visible. All other neighbours (South Sudan, Rwanda, Tanzania, Burundi, Central African Republic, Zambia, Angola, Republic of Congo) are retained in the tail; South Sudan (Juba and Rumbek) then leads the tail with ~53 %, followed by Kenya (~12 %) and the Central African Republic (~7 %).
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.
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.
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 neighbouring countries (before renormalisation), showing that Uganda alone accounts for 98.0 % of the non-DRC mass. The second (Sections 4 and 5) removes only Uganda and renormalises the remaining mass over 120 international destinations reachable by air. Under this scope, South Sudan (Juba and Rumbek) leads the tail with ~53 %, followed by Kenya (~12 %) and the Central African Republic (~7 %); the remaining ~28 % 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 — for example, Kasese in Uganda's Western Region (Kasese District), Juba in South Sudan's Central Equatoria State, Kigali in Rwanda's City of Kigali Province, and Dar Es Salaam in Tanzania's Dar es Salaam Region. 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.
Neighbouring-Country Cities
Across DRC's nine neighbouring countries, the mobility model concentrates 99.0 % of the non-DRC relative risk mass in five cities to the east and north-east of the country: Kasese in Uganda's Western Region, Pakuba in Northern Region, and Arua in West Nile Region, which together account for 97.9 %, plus Juba in South Sudan's Central Equatoria State and Rumbek in Lakes State. The Central African Republic, Tanzania, Rwanda, and the Republic of Congo, together with Entebbe near Kampala, contribute a thin residual (Bangui, Entebbe, Dar Es Salaam, Kigali, Brazzaville), reflecting their smaller connectivity to the affected area over the two-week horizon.
Values in the chart below are the direct model output with only DRC itself removed from the destination set; 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 98.0 % of the non-DRC mass, the international tail that follows in Section 4 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 South Sudan leads the tail with ~53 %.
Regional Distribution · International Tail
With DRC and Uganda removed and the remainder renormalised to sum to one, the tail is led by South Sudan: Juba carries 45.9 %, and Rumbek adds a further 7.6 %. This reflects the strong overland connectivity from north-eastern DRC into South Sudan captured by the mobility model. Kenya (12.0 %, mostly Nairobi) and the Central African Republic (6.9 %, Bangui) follow, and the rest of the tail (~28 %) is spread across the wider international network.
Africa carries 86.3 % of the tail (South Sudan 53.5 %; then Kenya 12.0 %, Central African Republic 6.9 %, Tanzania 2.9 %, Ethiopia 1.7 %, Rwanda 1.4 %). Europe accounts for 5.2 % (Belgium 1.2 %, United Kingdom 1.1 %, France 0.5 %), the Middle East for 4.8 % (UAE 3.2 %, Saudi Arabia 0.7 %, Qatar 0.4 %), Asia for 2.6 % (India 1.1 %, China 0.9 %), the Americas for 0.9 % (United States 0.6 %, Canada 0.3 %), 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.
Destination Analysis · International Tail
The tail is presented at two granularities. Figure 2 gives the geographic distribution of country-level aggregate risk; Figure 3 ranks the top ten destination countries by aggregated RR. Figure 4 shifts to the city level (each city denotes a metropolitan catchment inside the country's administrative geography), providing the per-location ranking across the top 30 international destinations. Bars in Figures 3 and 4 are coloured by region.
Figure 2. Country-level aggregate of the international-tail relative importation risk (DRC and Uganda removed, remainder renormalised). Countries shaded by the sum of city-level RR within their borders. Hover for values.
At the city level, Juba leads the tail with 45.9 % of the mass. Behind Juba the leading cities are Nairobi (KEN, RR 10.1 %), Rumbek (SSD, RR 7.6 %), Bangui (CAF, RR 6.9 %), Dubai (ARE, RR 3.2 %), Dar Es Salaam (TZA, RR 1.8 %), and Addis Ababa (ETH, RR 1.3 %). The full top-30 ranking is shown below.
Limitations & Assumptions
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. 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 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 ~11 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, and is further modulated by any border-control interventions not captured in the model.
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.
- [9] Reuters. Congo re-opens airport at the centre of Ebola outbreak. 2 June 2026. Available at: reuters.com