Investigation of within Household Transmission of Influenza in Low-Resource Settings

Abstract: Seasonal influenza causes a significant burden of disease in low-middle income countries, specifically in some of the most vulnerable populations such as infants, pregnant women, and the elderly. Influenza spreads primarily within households and while this has been extensively studied in the high-income countries, there remains a significant gap in the literature around household transmission of influenza in low-middle income countries. Differences in population structure, household membership, vaccine availability, and contact patterns make it difficult to apply knowledge from high to low-middle income countries. Understanding household transmission of influenza in this setting will help to generate key epidemiologic parameters of influenza such as secondary attack rate and help to model potential interventions to decrease transmission. This K23 offers an outstanding opportunity to leverage respiratory and serologic samples from a household- based cohort with self-reported and sensor-based social contact data who were followed prospectively for any respiratory illness in three low-middle income countries: Guatemala, Mozamique, India. Our approach will be to use established molecular techniques including hemagglutination inhibition assay and RT-PCR to calculate seroprevalence curves, the force of infection, and with-in household secondary attack rates. We will the develop a heterogenous chain binomial model of within household transmission by incorporating household contact data and risk of infectious between pairs of individuals in the household. Aim 1 is to quantify the spread of with-in household transmission of influenza. Aim 2 is to develop a heterogenous chain binomial model of within household transmission with the hopes of projecting how effective different interventions such as vaccination and masking are in mitigating intra-household influenza transmission. To complete this project, I will require additional training in infectious disease modeling, advanced methods in infectious disease quantification, influenza epidemiology and emerging infectious diseases. To help ensure this projects success, I am surrounded an international team of experts in influenza epidemiology and infectious disease modeling with whom I have established relationships. This K23 award would provide the crucial link in my career from having a foundation in infectious disease epidemiology to having an expertise in infectious disease modeling. At the end of this award period, I will be prepared to submit a strong NIH R01 application focused on modeling emerging infectious diseases transmission in low-resource settings.