Ebola modeling: behavior, asymptomatic infection, and contacts

项目摘要 好了！ 未被识别的埃博拉病毒感染(无症状和有症状)对 2013-2016年西非埃博拉疫情期间的传播动态尚不清楚。个人 患有无症状埃博拉病毒感染或未被识别的症状性埃博拉病毒病(EVD)的人代表 两组可能有不同程度的接触和EBOV传播率。越来越多 避免接触埃博拉病例的防护行为可能导致接触水平较低，以及 这些暴露可能与无症状的EBOV感染有关。另一方面，那些 有症状但从未确诊的埃博拉病毒病可能对传播特别重要 动态，因为其中一些人是导致#年埃博拉疫情的传播链的一部分 以前未受影响的社区。 我们的研究问题集中在了解导致EBOV传播的驱动因素 疫情下降。相互竞争的假说围绕着预防行为、健康- 寻找行为、接触者之间的传播饱和以及无症状的EBOV感染。新开 来自研究的可用的详细的血清学、社会网络、行为、人种学和疫苗接种数据 在利比里亚、塞拉利昂和刚果民主共和国的合作将使我们能够测试竞争对手 以下假设：1)社会网络中未被识别的EBOV感染的动态效应 结构，2)未识别的症状性埃博拉病例、护理障碍和预防行为，以及3) 无症状EBOV感染的原因。这些发现有可能量化是什么导致了埃博拉疫情的结束 并改进数学模型。数学建模应用程序将改进预测 在新的疫情期间，并通过环状疫苗接种或新的社交网络向接触者提供疫苗的方法 网络算法。 随着埃博拉疫情继续发生，2018年将有两起，这项R01提案将提供经验教训 立即适用于未来爆发的EBOV、其他病毒性出血热和新出现的 传染病。 好了！

Project Summary ! The impact of unrecognized Ebola virus (EBOV) infection (asymptomatic and symptomatic) on transmission dynamics during the 2013–2016 West Africa Ebola outbreak is poorly understood. Individuals who had asymptomatic EBOV infection or unrecognized symptomatic Ebola virus disease (EVD) represent two groups who may have had different levels of exposure and rates of EBOV transmission. Increasingly protective behaviors to avoid contact with EVD cases may have resulted in lower levels of exposure, and these exposures may be associated with asymptomatic EBOV infection. On the other hand, individuals who had symptomatic EVD but were never diagnosed may be disproportionately important to transmission dynamics because some of these individuals were part of transmission chains leading to Ebola outbreaks in previously unaffected communities. Our research question focuses on understanding the drivers of EBOV transmission leading to epidemic decline. Competing hypotheses were centered around issues of preventive behaviors, health- seeking behaviors, saturation of transmission among contacts, and asymptomatic EBOV infection. Newly available, detailed serologic, social network, behavioral, ethnographic, and vaccination data from research collaborations in Liberia, Sierra Leone, and Democratic Republic of Congo will allow us to test competing hypotheses in the following aims: 1) Dynamical effects of unrecognized EBOV infection in social network structure, 2) Unrecognized symptomatic EVD cases, barriers to care, and preventive behaviors, and 3) Causes of asymptomatic EBOV infection. These findings have the potential to quantify what ended the Ebola pandemic and improve mathematical models. Mathematical modeling applications will improve forecasting during new outbreaks and inform ways to deliver vaccines to contacts, by ring vaccination or novel social network algorithms. As Ebola outbreaks continue to occur, two in 2018, this R01 proposal will provide lessons learned that are immediately applicable to future outbreaks of EBOV, other viral hemorrhagic fevers, and emerging infectious diseases. !