Exploratory network-based modelling of Bundibugyo Ebola virus disease importation and spread in Uganda using Displacement Tracking Matrix flow data

@misc{walekhwa2026exploratory,
  title = {{Exploratory network-based modelling of Bundibugyo Ebola virus disease importation and spread in Uganda using Displacement Tracking Matrix flow data}},
  author = {A. W. Walekhwa and P. Mbaka and B. K. Nannyonga and S. P. Silal and M. Bbuye and B. Magoba and L. Nakiire and M. Lamorde and T. A. Sulaimon and B. Kwesiga and W. Sabiiti and W. Mutamba and G. Turinawe and B. Nakazibwe and J. Kayiwa and M. Nantongo and P. A. Ipola and P. Kungu and A. R. Ario and A. Kagirita and B. Lubwama and B. J. Kirenga and M. Wayengera and A. N. Muruta and C. Olaro and D. Atwine},
  howpublished = {medRxiv},
  year = {2026},
  month = jun,
  doi = {10.64898/2026.06.22.26356210},
  url = {https://www.medrxiv.org/content/10.64898/2026.06.22.26356210v3},
  note = {CC BY-NC},
}

TY  - UNPB
TI  - Exploratory network-based modelling of Bundibugyo Ebola virus disease importation and spread in Uganda using Displacement Tracking Matrix flow data
AU  - A. W. Walekhwa
AU  - P. Mbaka
AU  - B. K. Nannyonga
AU  - S. P. Silal
AU  - M. Bbuye
AU  - B. Magoba
AU  - L. Nakiire
AU  - M. Lamorde
AU  - T. A. Sulaimon
AU  - B. Kwesiga
AU  - W. Sabiiti
AU  - W. Mutamba
AU  - G. Turinawe
AU  - B. Nakazibwe
AU  - J. Kayiwa
AU  - M. Nantongo
AU  - P. A. Ipola
AU  - P. Kungu
AU  - A. R. Ario
AU  - A. Kagirita
AU  - B. Lubwama
AU  - B. J. Kirenga
AU  - M. Wayengera
AU  - A. N. Muruta
AU  - C. Olaro
AU  - D. Atwine
PY  - 2026
DA  - 2026/06/22
PB  - medRxiv
DO  - 10.64898/2026.06.22.26356210
UR  - https://www.medrxiv.org/content/10.64898/2026.06.22.26356210v3
AB  - BackgroundThe 2026 Bundibugyo Ebola outbreak in Uganda, linked to the ongoing epidemic in the Democratic Republic of the Congo (DRC), spread through human mobility across borders and within the country. Existing modelling has provided regional-level importation estimates but not district-level operational guidance. We developed an exploratory, scenariobased decision-support model to characterise subnational importation and spread patterns.  MethodsWe constructed a directed, weighted mobility network using International Organization for Migration (IOM) Displacement Tracking Matrix (DTM) flows collected from 15-24 May 2026 (11,245 observed movements) and the 2024 Uganda census. A stochastic metapopulation SEIR model was simulated over 90 days across 135 Ugandan districts and two DRC provinces. We evaluated three scenario assumptions--containment (R0 = 0.80), moderate (R0 = 1.35), and weak (R0 = 2.76)--plus a one-factor-at-a-time analysis of nonpharmaceutical intervention (NPI) compliance. Global sensitivity analysis was performed using Morris elementary-effects screening and total-order Sobol indices.  ResultsThe mobility network was sparse (density 0.11), highly unequal (Gini 0.67), and modular (0.50). Kisoro had the highest import risk (in-strength 3,823) and export risk (outstrength 1,350). Under all three scenarios, the model projected similar cumulative case counts (median 20-21; pairwise Mann-Whitney p < 0.05). Under containment, Kampala was the only district with a non-zero median (20 cases, 95% simulation interval 20-22); all other districts had median 0. Prediction-interval coverage against the early observed curve was 0.80.  ConclusionsUnder the models containment assumptions, rapid 7-1-7 implementation was associated with substantial containment, but this is scenario-specific and does not imply that NPIs are unnecessary. Border surveillance, nationwide preparedness, and community engagement remain essential. These projections should be treated as exploratory and require prospective field-based validation.
ER  -