Ebola modeling: behavior, asymptomatic infection, and contacts

埃博拉模型：行为、无症状感染者和接触者

• 批准号: 10001553
• 负责人: Travis Christian Porco
• 金额: $ 34.01万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001553
• 关键词: Africa; Algorithms; Behavior; Behavioral; Biology; Collaborations; Communities; Data; Democratic Republic of the Congo; Diagnosis; Diagnostic tests; Disease Outbreaks; Dose; Ebola; Ebola Hemorrhagic Fever; Ebola virus; Emerging Communicable Diseases; Epidemic; Ethnography; Event; Exhibits; Exposure to; First Degree Relative; Future; Health Care Seeking Behavior; Health Personnel; Human; Ice; Immunity; Individual; Infection; International; Intervention; Liberia; Liquid substance; Modeling; Paint; Pattern; Play; Preventive; Reporting; Research; Role; Seminal fluid; Seroepidemiologic Studies; Serological; Sierra Leone; Social Network; Statistical Models; Structure; Study models; Survivors; Testing; Time; Vaccination; Vaccines; Viral Hemorrhagic Fevers; Virus Diseases; barrier to care; falls; health seeking behavior; improved; infection rate; mathematical model; member; nonhuman primate; novel; pandemic disease; point-of-care diagnostics; prevent; protective behavior; response; transmission process; vaccination strategy; viral transmission

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. !

项目概要 ！ 未识别的埃博拉病毒 (EBOV) 感染（无症状和有症状）对人的影响 人们对 2013 年至 2016 年西非埃博拉疫情期间的传播动态知之甚少。个人 患有无症状埃博拉病毒感染或未被识别的有症状埃博拉病毒病 (EVD) 的人代表 两组可能有不同程度的接触和埃博拉病毒传播率。日益 避免与埃博拉病毒病病例接触的保护行为可能会导致暴露水平降低，并且 这些暴露可能与无症状埃博拉病毒感染有关。另一方面，个人 患有埃博拉病毒病症状但从未被诊断出来可能对传播尤为重要 动态，因为其中一些人是导致埃博拉疫情爆发的传播链的一部分 以前未受影响的社区。 我们的研究问题侧重于了解埃博拉病毒传播的驱动因素 疫情下降。相互竞争的假设集中在预防行为、健康问题等方面。 寻求行为、接触者传播饱和度以及无症状埃博拉病毒感染。新 来自研究的可用、详细的血清学、社交网络、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 假设目标如下：1）社交网络中未被识别的埃博拉病毒感染的动态效应 结构，2) 未被识别的有症状的埃博拉病毒病病例、护理障碍和预防行为，以及 3) 无症状埃博拉病毒感染的原因。这些发现有可能量化结束埃博拉病毒的原因 流行病并改进数学模型。数学建模应用将改善预测 在新的疫情爆发期间，并告知如何通过环形疫苗接种或新的社交方式向接触者提供疫苗 网络算法。 随着埃博拉疫情持续发生（2018 年两次），此 R01 提案将提供经验教训 立即适用于未来埃博拉病毒、其他病毒性出血热和新出现的病毒爆发 传染病。 ！