Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015-16: a modelling study

Abstract

Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock.

Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region.

Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected.

Interpretation: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy.

Funding: Wellcome Trust.

Figure 1

Number of cases in Angola and geographic spread of the epidemic (A) Epidemic curve for suspected and confirmed cases in Angola and Luanda from Dec 1, 2015, to Aug 25, 2016. (B) Fit of exponential growth during the early phase of the epidemic. (C) Number of districts affected during each week over the course of the outbreak.

Figure 2

Timing of the introduction of yellow fever virus and duration of infection (A) Timing of the introduction of yellow fever virus to each district starting from the origin of the outbreak in Luanda, Angola. Colouring shows the weeks until the first case was reported. (B) Duration of transmission.

Figure 3

Model accuracy and real-time prediction of the yellow fever virus invasion model (A) Model prediction accuracy as assessed by comparing the predicted invasion probability from the geographic spread model with the observed proportion of districts that became invaded; numbers represent district–weeks. (B) Comparisons between district targeting based on real-time modelling analysis vs random targeting during the expansion phase of the outbreak between mid-March and mid-April, during which 32 districts were newly invaded.

Figure 4

Model-based predictions of yellow fever virus spread Maps show model-based predictions for the invasion of yellow fever virus in central Africa originating from Kinshasa, the location with the latest reported cases, at 4 weeks (A) and 8 weeks (B) ahead of the last case onset date, July 12, 2016. Colours represent weekly probability of invasion.