Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji.
Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji.
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DOI：
10.7554/elife.34848
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发表时间：
2018-08-14
期刊：
影响因子：
7.7
通讯作者：
Hibberd ML
中科院分区：
文献类型：
--
作者：
Kucharski AJ;Kama M;Watson CH;Aubry M;Funk S;Henderson AD;Brady OJ;Vanhomwegen J;Manuguerra JC;Lau CL;Edmunds WJ;Aaskov J;Nilles EJ;Cao-Lormeau VM;Hué S;Hibberd ML
登革热是一个重大的健康负担，但检查传播和评估控制措施可能具有挑战性，因为疫情取决于多种因素，包括人口结构，先前的免疫力和气候。我们将斐济2013/14年登革热-3爆发前后收集的具有人口代表性的配对血清与监测数据相结合，以确定这些因素如何影响岛屿环境中的传播和控制。我们的研究结果表明，10-19岁年龄组的感染风险最高，但我们没有发现强有力的证据表明其他人口统计学或环境风险因素与血清转化有关。与监测和血清学数据联合拟合的数学模型表明，群体免疫和季节性变化的传播无法解释观察到的动态。然而，该模型显示，在开展病媒清理运动的同时，传播进一步减少，这可能有助于疫情后期病例的减少。登革热--一种由蚊子传播的疾病--在亚洲和南太平洋岛屿引起大规模爆发。卫生机构经常试图通过清除蚊子滋生地来减少疾病的传播，如旧轮胎和可能容纳积水的容器。但很难判断这些预防措施是否有效，因为登革热的传播取决于许多因素，包括天气以及有多少人因以前的感染而产生了免疫力。研究感染和免疫模式的一种常见方法是在疫情爆发前后从一部分人群中收集血液样本。不幸的是，大规模登革热疫情在岛屿上零星发生，因此很难在疫情爆发前进行这样的研究。2013年和2014年期间，斐济爆发了一次重大登革热疫情，报告了25 000多例疑似病例。作为回应，政府发起了一场全国性的灭蚊运动。幸运的是，一组研究人员在爆发前立即收集了血液样本，用于伤寒和钩端螺旋体病的无关研究。现在，Kucharski等人--包括收集这些爆发前血液样本的研究人员--表明，清理运动与疾病传播的减少相吻合。在登革热爆发前采集血液的参与者被邀请在登革热爆发后提供另一份血液样本。这使得Kucharski等人能够识别在爆发前已经对登革热产生免疫力的个体以及在爆发期间可能感染的个体。对疫情爆发前后采集的血液样本进行比较后发现，10至19岁的儿童和青少年在疫情爆发期间感染风险最大。没有其他人口或环境因素与感染的可能性密切相关。使用这些数据的计算机模型也表明，清理工作可以解释登革热疫情期间传播减少的原因。这些发现表明，研究登革热的免疫力可以更好地了解疾病的传播。这可能有助于卫生机构评估控制这种疾病的努力的效果，并可能预测未来的爆发。
Dengue is a major health burden, but it can be challenging to examine transmission and evaluate control measures because outbreaks depend on multiple factors, including human population structure, prior immunity and climate. We combined population-representative paired sera collected before and after the 2013/14 dengue-3 outbreak in Fiji with surveillance data to determine how such factors influence transmission and control in island settings. Our results suggested the 10–19 year-old age group had the highest risk of infection, but we did not find strong evidence that other demographic or environmental risk factors were linked to seroconversion. A mathematical model jointly fitted to surveillance and serological data suggested that herd immunity and seasonally varying transmission could not explain observed dynamics. However, the model showed evidence of an additional reduction in transmission coinciding with a vector clean-up campaign, which may have contributed to the decline in cases in the later stages of the outbreak. Dengue fever – a disease spread by mosquitos – causes large outbreaks in Asia and the South Pacific islands. Health agencies often try to reduce the spread of the disease by removing mosquito breeding grounds, like old tires and containers that may hold standing water. But it can be difficult to tell whether these preventive measures work because dengue transmission depends on many factors, including the weather and how many people had developed immunity because of previous infections. A common way to study patterns of infection and immunity is to collect blood samples from a subset of the population before and after an outbreak. Unfortunately, large dengue outbreaks occur sporadically on islands, making it hard to set up a study like this ahead of an outbreak. During 2013 and 2014, there was a major dengue outbreak in Fiji, with over 25,000 suspected cases reported. In response, the government introduced a nationwide mosquito clean-up campaign. As luck would have it, a group of researchers had collected blood samples immediately before the outbreak for an unrelated study of typhoid fever and leptospirosis. Now, Kucharski et al. – who include the researchers who collected those pre-outbreak blood samples – show that the clean-up campaign coincided with a reduction in transmission of the disease. Participants whose blood was collected before the dengue outbreak were invited to provide another blood sample after the dengue outbreak. This allowed Kucharski et al. to identify individuals who had already developed immunity to dengue before the outbreak and those who were likely infected during the outbreak. Comparing blood samples taken before and after the outbreak revealed that children and teenagers between the ages of 10 and 19 had the greatest risk of infection during the outbreak. No other demographic or environmental factors were strongly linked to the likelihood of infection. Computer models using the data also showed that the clean-up efforts could explain the reduced dengue transmission during the outbreak. These findings suggest that studying immunity against dengue can lead to a better understanding of disease transmission. This may help health agencies to gauge the effects of efforts to control this disease, and possibly forecast future outbreaks.