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

> **NIH NIH R01** · YALE UNIVERSITY · 2023 · $270,127

## Abstract

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.

## Key facts

- **NIH application ID:** 10577833
- **Project number:** 5R01AI161399-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Benjamin A Lopman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $270,127
- **Award type:** 5
- **Project period:** 2022-03-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10577833

## Citation

> US National Institutes of Health, RePORTER application 10577833, COVID-19 - Global Mix / Investigation of COVID-19 Disease Parameters for Transmission Models in Low-Resource Settings (5R01AI161399-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10577833. Licensed CC0.

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