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

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，“资源贫乏国家社会融合模式的综合概况”(“GlobalMix”，赠款 R01 HD097175-01)，以调查与呼吸道感染传播相关的人与人之间的相互作用。 这项建议将建立在现有的GlobalMix研究基础设施上，以估计特定于LMIC的流行病学 新冠肺炎的参数。在拟议的研究中，我们将把现场流行病学和数理统计联系起来 通过估计SARS在家庭中的传播率和异质性的建模方法- CoV-2通过纵向队列方法。我们将使用此信息与高度细粒度的 来自GlobalMix的社交互动数据，以确定新冠肺炎的关键流行病学参数，包括 社区一级的感染力量和家庭内的发病率。然后，我们将使用此信息来 建立特定于LMIC的动态模型，以评估关键干预措施对减少传播的影响： 疫苗接种和非药物干预措施，如口罩、就地避难所政策和学校 结案了。这项工作将以三个具体目标完成： 目标1：量化新冠肺炎在家庭环境中通过联系网络的传播。我们会 在参与GlobalMix研究的家庭中进行纵向呼吸道疾病监测。我们会 采集住户成员呼吸道纵向标本鉴定新冠肺炎 以及其他呼吸道病原体，如流感。此信息将被覆盖在联系人网络数据上 来自GlobalMix。 目的2：评估LMIC中SARS-CoV-2和其他呼吸道病原体的主要流行病学特征 设置。我们将收集GlobalMix研究参与者的血液样本，并检测抗体水平(IgG) 对抗SARS-CoV-2。我们将计算特定年龄段的感染死亡率(IFRS)并使用抗体滴度 推断感染时间并计算随时间推移的社区发病率。我们将根据年龄结构生成 血清阳性率曲线，这将提供一个可靠的措施，暴露在整个年龄范围。与.一起 来自GlobalMix的联系数据，我们将推断将用作输入的特定年龄的传播概率 进入AIM 3的网络模型。样本将被存储以供将来测试，包括抗体亲和力和T/B 细胞激活。 目的3.评估控制措施对LMIC新冠肺炎的影响。我们将利用流行病学 目标1中估计的参数和目标2至#年的特定环境和特定年龄的感染力估计 对疾病传播的基于网络的动态数学模型进行参数化。模特们将包括 来自GlobalMix的社交混合数据，以预测扩展的就地避难政策、政策 关于口罩的使用和SARS-CoV-2疫苗的引入。

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.