Ebola modeling: behavior, asymptomatic infection, and contacts

项目摘要 ! 未被识别的埃博拉病毒（EBOV）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知之甚少。个人 无症状EBOV感染者或未被识别的有症状埃博拉病毒病（EVD）代表 这两组人可能有不同程度的暴露和EBOV传播率。日益 避免接触EVD病例的保护行为可能导致暴露水平降低， 这些暴露可能与无症状EBOV感染有关。另一方面， 有症状的EVD但从未被诊断可能对传播不成比例地重要 因为其中一些人是导致埃博拉疫情的传播链的一部分， 以前未受影响的社区。 我们的研究问题的重点是了解EBOV传输的驱动因素， 疫情下降。相互竞争的假设集中在预防行为，健康- 寻求行为、接触者之间的传播饱和度和无症状EBOV感染。新 从研究中获得的可用的、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 1）未识别的EBOV感染在社会网络中的动态影响 结构，2）未识别的症状性EVD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终结了埃博拉 流行病和改进数学模型。数学建模应用将改善预测 在新的疫情期间，通过环形疫苗接种或新的社会 网络算法 随着埃博拉疫情继续发生，2018年将发生两次，本R 01提案将提供经验教训 这是立即适用于未来爆发的EBOV，其他病毒性出血热， 传染病 !

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. !