Kraemer Moritz U G, Faria Nuno R, Reiner Robert C, Golding Nick, Nikolay Birgit, Stasse Stephanie, Johansson Michael A, Salje Henrik, Faye Ousmane, Wint G R William, Niedrig Matthias, Shearer Freya M, Hill Sarah C, Thompson Robin N, Bisanzio Donal, Taveira Nuno, Nax Heinrich H, Pradelski Bary S R, Nsoesie Elaine O, Murphy Nicholas R, Bogoch Isaac I, Khan Kamran, Brownstein John S, Tatem Andrew J, de Oliveira Tulio, Smith David L, Sall Amadou A, Pybus Oliver G, Hay Simon I, Cauchemez Simon
Department of Zoology, University of Oxford, Oxford, UK.
Department of Zoology, University of Oxford, Oxford, UK.
Lancet Infect Dis. 2017 Mar;17(3):330-338. doi: 10.1016/S1473-3099(16)30513-8. Epub 2016 Dec 23.
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.
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.
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.
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.
Wellcome Trust.
自2015年末以来，黄热病疫情已在安哥拉和刚果民主共和国造成7334多例疑似病例，其中393人死亡。我们试图了解此次疫情的空间传播情况，以优化有限可用疫苗库存的使用。
我们联合分析了描述黄热病疫情、媒介适宜性、人口统计学和中非地区人口流动情况的数据集，以了解和预测黄热病病毒的传播。我们使用标准逻辑模型推断该地区疫情期间各地区特定的黄热病病毒感染风险。
黄热病病毒的早期传播特点是从安哥拉首都罗安达快速指数增长（倍增时间为5 - 7天）和快速空间扩散（仅3个月后就有49个地区报告病例）。早期入侵与高人口密度呈正相关（皮尔逊相关系数r为0.52，95%置信区间为0.34 - 0.66）。地点离罗安达越远，入侵日期越晚（皮尔逊相关系数r为0.60，95%置信区间为0.52 - 0.66）。在一个考克斯模型中，我们注意到人口密度较高的地区持续传播的风险也较高（地区人口规模每增加一个对数单位，病例停止的风险比为0.74，95%置信区间为0.13 - 0.92）。一个考虑了人口流动和媒介适宜性的模型成功地区分了高入侵风险地区和低风险地区（曲线下面积为0.94，95%置信区间为0.92 - 0.97）。如果在疫情开始时，有足够的疫苗可用于该地区313个地区中的50个，我们的模型将正确识别出最终受影响的32个地区中的27个（84%）。
我们的研究结果显示了生态和人口因素对黄热病疫情持续传播的作用，并提供了可优先进行疫苗接种地区的估计，不过在将这些见解转化为政策之前，还需要考虑疫苗供应和配送等其他限制因素。
惠康信托基金会。
