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-我们在2019年开始量化家庭和社区层面的互动， “资源贫乏国家社会混合模式的综合概况”项目（“GlobalMix”，赠款 R 01 HD 097175 -01），以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的GlobalMix研究基础设施上，以估计LMIC特定的流行病学 COVID-19的参数。在拟议的研究中，我们将把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， CoV-2通过纵向队列方法。我们将使用这些信息与高粒度的 来自GlobalMix的社交互动数据，以确定COVID-19的关键流行病学参数，包括 社区一级的感染力和家庭内的发病率。我们将使用这些信息， 建立针对中低收入国家的动态模型，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，如口罩、就地安置政策和学校 结束这项工作将在三个具体目标下完成： 目标1：量化COVID-19在家庭环境中通过接触网络的传播。我们将 对参与GlobalMix研究的家庭进行纵向呼吸道疾病监测。我们将 从家庭成员中纵向采集呼吸道标本，以识别COVID-19 和其他呼吸道病原体如流感。这些信息将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关键流行病学特征 设置.我们将采集GlobalMix研究参与者的血液样本，并检测抗体水平（IgG） 针对SARS-CoV-2。我们将计算年龄特异性感染死亡率（IFR），并使用抗体滴度， 推断感染时间并计算一段时间内社区发病率。我们将产生年龄结构 血清阳性率曲线，这将提供一个跨年龄范围的接触的可靠措施。连同 从GlobalMix的接触数据，我们将推断年龄特定的传输概率，将被用作输入 目标3中的网络模型。将储存样本用于未来检测，包括抗体亲合力和T/B 细胞激活 目标3.评估控制措施对LMIC COVID-19的影响。我们将使用流行病学 目标1中估计的参数以及目标2至 基于参数化动态网络的疾病传播数学模型。模型将包含 GlobalMix的社会混合数据，以预测扩大就地庇护政策的影响， 关于使用口罩和引进SARS-CoV-2疫苗。