Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study
The Lancet Infectious Diseases·
- DOI
- 10.1016/s1473-3099(16)30513-8
- PMID
- 28017559
- PMCID
- PMC5332542
- OpenAlex
- W2566415269
- Study type
- Mathematical modelling
- Publisher
- Elsevier BV
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The findings provide estimates for prioritising areas for vaccination during yellow fever outbreaks and demonstrate the potential for predictive modelling to guide targeted vaccine allocation, though practical implementation would require consideration of vaccine supply and delivery constraints.
Structured evidence summary
Research question
The study aimed to understand the spatial spread of the 2015–16 yellow fever outbreak in Angola and the Democratic Republic of the Congo to inform optimal use of limited vaccine supplies.
Study design
This was a mathematical modelling study that jointly analysed datasets describing the yellow fever epidemic, vector suitability, human demography, and mobility patterns in central Africa. A standard logistic model and Cox proportional hazards model were used to infer district-level infection risk and sustained transmission risk, respectively.
Population and setting
The study examined the yellow fever outbreak spanning Angola and the Democratic Republic of the Congo, with initial spread traced from Luanda, Angola. Analysis covered 313 districts in the region, with cases reported across 49 districts within the first 3 months of the epidemic.
Main findings
Early outbreak spread exhibited rapid exponential growth with a 5–7 day doubling time and quick spatial expansion from Luanda. Early invasion was positively correlated with population density (Pearson's r 0.52) and inversely correlated with distance from Luanda (Pearson's r 0.60). Higher district population density was associated with increased risk of sustained transmission (hazard ratio 0.74 per log-unit increase). A mobility and vector-suitability model discriminated high-risk districts with high accuracy (AUC 0.94). Retrospective analysis indicated that targeting 50 of 313 districts would have identified 84% of eventually affected districts.
Public-health relevance
The findings provide estimates for prioritising areas for vaccination during yellow fever outbreaks and demonstrate the potential for predictive modelling to guide targeted vaccine allocation, though practical implementation would require consideration of vaccine supply and delivery constraints.
Important limitations
The authors note that insights require integration with constraints such as vaccine supply and delivery logistics before translation into policy. This summary is limited to the supplied single-article abstract and metadata; full interpretation requires review of the original paper.
GIDS interpretation
This modelling study characterises historical outbreak dynamics from 2015–16 and provides retrospective estimates of potential vaccination targeting. It does not represent current surveillance data or an active signal, but may inform understanding of factors contributing to yellow fever spread in this geographic region for context only.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 13 auditable classifier relationships to diseases, places, topics, and study design.