Back

Sixteen Days Undetected: Growth Dynamics and the Case for Pre-Positioned Response Capacity in the 2026 Bundibugyo Virus Disease Outbreak, Democratic Republic of the Congo A back-calculation and growth-rate analysis using corrected daily surveillance data

Verheyden, J. G. L.; Mudogo, C. N.

2026-08-12 infectious diseases
10.64898/2026.08.12.26360240 medRxiv
Show abstract

Objectives: To estimate early growth rate, back-calculate transmission onset, and characterise the case-fatality trajectory of the 2026 Bundibugyo virus disease (BDBV) outbreak in the Democratic Republic of the Congo, the largest recorded BDBV outbreak to date. Design or methods: We analysed a corrected daily surveillance series (65 observations, 14 May to 27 July 2026) using non-linear least-squares regression and a Bayesian Poisson growth model fitted by Markov chain Monte Carlo, with five sensitivity analyses. Results: Early confirmed cases grew at 0.1261 per day (95% CI 0.0885-0.1636), a doubling time of 5.50 days (4.24-7.83), three-fold faster than previous BDBV outbreaks (15-18 days). Bayesian back-calculation placed transmission onset on 19 April 2026 (95% highest-density interval 9-27 April), 16 days before the WHO alert and 25 days before laboratory confirmation. Confirmed case-fatality ratio rose from 12.1% to 44.3%; a higher ratio among suspected than confirmed cases on 21 May (23.6% vs 10.8%; p=0.0080) supported progressive reclassification rather than increasing virulence. Conclusions: Rapid BDBV growth leaves little time for outbreak-triggered mobilisation. Sentinel alerts based on unexplained healthcare-worker death clusters, together with pre-positioned surveillance, diagnostic, and response capacity, could reduce avoidable amplification before confirmation.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.