COVID-19 - Global Mix / Investigation of COVID-19 Disease Parameters for Transmission Models in Low-Resource Settings

COVID-19 - 全球混合/资源匮乏环境中传播模型的 COVID-19 疾病参数调查

• 批准号: 10367612
• 负责人: Benjamin A Lopman
• 金额: $ 78.58万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367612
• 关键词: 2019-nCoV; Age; Antibody Avidity; Antibody titer measurement; Area; B-Cell Activation; Blood specimen; COVID-19; COVID-19 pandemic; COVID-19 prevention; COVID-19 vaccine; Communities; Country; Data; Decision Making; Disease; Disease Surveillance; Environment; Epidemic; Epidemiology; Family member; Fatality rate; Future; Grant; Guatemala; Healthcare; Heterogeneity; Household; Human; Immunoglobulin G; Incidence; India; Infection; Influenza; Infrastructure; Intervention; Investigation; Longitudinal cohort; Low income; Measures; Modeling; Mozambique; Network-based; Participant; Pattern; Policies; Prevention; Probability; Readiness; Research; Resource-limited setting; Resources; Respiratory Disease; Respiratory Tract Infections; Rural; SARS-CoV-2 transmission; Sampling; Schools; Science; Seroprevalences; Shelter facility; Social Interaction; Specimen; Structure; Testing; Time; Update; Vaccination; Work; antibody test; base; disease transmission; face mask; low and middle-income countries; mathematical model; member; network models; pandemic disease; respiratory; respiratory pathogen; response; social; transmission process; vaccine distribution

PROJECT SUMMARY - We began to quantify household- and community-level interactions in 2019 with our project, “Comprehensive Profiling of Social Mixing Patterns in Resource Poor Countries” (“GlobalMix”, grant R01 HD097175-01) to investigate human-to-human interactions relevant for respiratory infection transmission. This proposal will build on existing GlobalMix study infrastructure to estimate LMIC-specific epidemiologic parameters for COVID-19. In the proposed study, we will connect field epidemiology and mathematical modeling approaches by estimating the rate of, and heterogeneity in, household-based transmission of SARS- CoV-2 through longitudinal cohort approaches. We will use this information in conjunction with highly-granular data on social interactions from GlobalMix to identify key epidemiological parameters for COVID-19, including the community-level force of infection and attack rates within households. We will then use this information to build LMIC-specific dynamic models, to evaluate the impact of key interventions to reduce transmission: vaccination and non-pharmaceutical interventions such as face masks, shelter-in-place policies and school closure. This work will be completed in three specific aims: Aim 1: Quantify COVID-19 transmission across contact networks within the household environment. We will conduct longitudinal respiratory disease surveillance in households participating in the GlobalMix study. We will collect longitudinal samples of respiratory specimens from household members for identification of COVID-19 and other respiratory pathogens such as influenza. This information will be overlaid on contact network data from GlobalMix. Aim 2: Estimate key epidemiological features of SARS-CoV-2 and other respiratory pathogens in LMIC settings. We will collect blood specimens from GlobalMix study participants and test for antibody levels (IgG) against SARS-CoV-2. We will calculate age-specific infection fatality rates (IFRs) and use antibody titers to infer time of infection and calculate community-level incidence over time. We will generate age-structured seroprevalence curves, which will provide a robust measure of exposure across the age range. Together with the contact data from GlobalMix, we will infer age-specific transmission probabilities that will be used as inputs into the network models in Aim 3. Samples will be stored for future testing, including antibody avidity and T/B cell activation. Aim 3. Estimate the impact of control measures on COVID-19 in LMIC. We will use the epidemiological parameters estimated in Aim 1 and the setting- and age-specific force of infection estimates from Aim 2 to parameterize dynamic network-based mathematical models of disease transmission. Models will incorporate social mixing data from GlobalMix to project the impact of extended shelter-in-place policies, policies concerning the use of face masks, and the introduction of a SARS-CoV-2 vaccine.

项目摘要 - 我们开始在2019年与我们的家庭和社区级互动量化 项目，“资源贫乏国家中社会混合模式的全面概述”（“ GlobalMix”，Grant R01 HD097175-01）研究与呼吸道感染传播相关的人与人类相互作用。 该提案将基于现有的全球构造研究基础设施，以估算LMIC特定的流行病学 COVID-19的参数。在拟议的研究中，我们将连接现场流行病学和数学 通过估计SARS的基于家庭基于家庭的传播的速度和异质性传播来建模方法 COV-2通过纵向队列方法。我们将使用此信息与高粒度结合 从GlobalMix中识别Covid-19的关键流行病学参数的有关社交互动的数据，包括 家庭内感染和攻击率的社区级力量。然后，我们将使用此信息来 构建LMIC特定的动态模型，以评估关键干预措施减少传输的影响： 疫苗接种和非药物干预措施，例如面具，现场政策和学校 关闭。这项工作将以三个具体的目的完成： AIM 1：量化COVID-19跨家庭环境中的接触网络的传输。我们将 在参加全球性研究的家庭中进行纵向呼吸道疾病监测。我们将 从家庭成员那里收集呼吸标本的纵向样品，以识别Covid-19 以及其他呼吸道病原体，例如影响力。此信息将被覆盖在联系网络数据上 来自GlobalMix。 目标2：估计SARS-COV-2和其他呼吸道病原体的关键流行病学特征 设置。我们将从GlobalMix研究参与者中收集血液标本，并测试抗体水平（IgG） 反对SARS-COV-2。我们将计算特定年龄的感染死亡率（IFRS），并使用抗体滴度 推断感染时间并计算社区级别的事件。我们将生成年龄结构的 血清阳性曲线将对整个年龄范围内的暴露量进行强大的测量。一起 来自GlobalMix的联系数据，我们将推断出特定年龄的传输可能性，将用作输入 在AIM 3中进入网络模型。样品将存储用于将来的测试，包括抗体亲和力和T/B 细胞激活。 目标3。估计控制措施对LMIC中COVID-19的影响。我们将使用流行病学 在AIM 1中估计的参数以及从AIM 2到2 参数化基于动态网络的疾病传播的数学模型。模型将合并 从GlobalMix的社会混合数据投影了扩展的就地政策，政策的影响 涉及使用口罩的使用，以及引入SARS-COV-2疫苗。