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

COVID Global Mix - 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: 10863617
• 负责人: Benjamin A Lopman
• 金额: $ 42.9万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 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/10863617
• 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; 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; Parameter Estimation; Participant; Pattern; Policies; Prevention; Probability; Research; Resource-limited setting; Resources; Respiratory Disease; Respiratory Tract Infections; Rural; SARS-CoV-2 transmission; Sampling; Science; Seroprevalences; Social Interaction; Specimen; Structure; Testing; Time; Update; Vaccination; Work; antibody test; coronavirus disease; disease transmission; epidemic preparedness; epidemiological model; face mask; low and middle-income countries; mathematical model; member; network models; pandemic preparedness; pandemic response; respiratory; respiratory pathogen; response; school closure; 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 - 我们于 2019 年开始量化家庭和社区层面的互动 项目，“资源匮乏国家社会混合模式的综合分析”（“GlobalMix”，赠款 R01 HD097175-01）研究与呼吸道感染传播相关的人与人之间的相互作用。 该提案将建立在现有 GlobalMix 研究基础设施的基础上，以估计 LMIC 特定的流行病学 在拟议的研究中，我们将结合现场流行病学和数学。 通过估计 SARS 家庭传播率和异质性的建模方法 我们将通过纵向队列方法结合使用这些信息。 GlobalMix 提供的社交互动数据可用于识别 COVID-19 的关键流行病学参数，包括 然后，我们将利用这些信息来确定社区层面的感染力和家庭内的感染率。 建立中低收入国家特定的动态模型，以评估减少传播的关键干预措施的影响： 疫苗接种和非药物干预措施，例如口罩、就地避难政策和学校 这项工作将完成三个具体目标： 目标 1：量化家庭环境内接触网络中的 COVID-19 传播情况。 我们将对参与 GlobalMix 研究的家庭进行纵向呼吸道疾病监测。 收集家庭成员呼吸道标本的纵向样本以识别 COVID-19 和其他呼吸道病原体，例如流感，该信息将覆盖在接触网络数据上。 来自 GlobalMix。 目标 2：估计中低收入国家 SARS-CoV-2 和其他呼吸道病原体的主要流行病学特征 我们将从 GlobalMix 研究参与者那里采集血液样本并测试抗体水平 (IgG)。 我们将计算特定年龄的感染死亡率 (IFR) 并使用抗体滴度来计算 推断感染时间并计算一段时间内的社区发病率。我们将生成年龄结构的数据。 血清流行率曲线，这将提供整个年龄范围内暴露的可靠测量。 来自 GlobalMix 的联系数据，我们将推断特定年龄的传播概率，并将其用作输入 进入目标 3 中的网络模型。样本将被存储以供将来测试，包括抗体亲合力和 T/B 细胞激活。 目标 3. 估计中低收入国家控制措施对 COVID-19 的影响 我们将使用流行病学数据。 目标 1 中估计的参数以及目标 2 中估计的特定环境和年龄的感染力 参数化基于动态网络的疾病传播数学模型。 来自 GlobalMix 的社会混合数据，用于预测延长就地避难政策、政策的影响 关于口罩的使用以及 SARS-CoV-2 疫苗的引入。