# LatiNET, a Multilevel Social Network Model to Examine and Address SARS-CoV-2 Misinformation in Low-Income Latinx Communities. > **NIH NIH R01** · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · 2023 · $753,712 ## Abstract PROJECT SUMMARY/ABSTRACT LatiNET will use a multilevel social network model to examine how SARS-CoV-2 misinformation and Conspiracy Theory (CT) messages are shared across five settings (friends, family, work, health service and influencers), impacting Latinx vaccine hesitancy. Social networks are self-organizing social systems that create and reinforce perceptions, both positive and negative. An important gap in current knowledge relates to the content, context and communication direction about SARS-CoV-2 misinformation and CT messages. By learning how Latinx social network structures hinder or promote SARS-CoV-2 misinformation and CT messages, we will inform the design of interventions that will reduce mistrust/fear and provide correct, timely, and comprehensive information, through multiple social network sources, enabling Latinx to make the best health decisions for themselves and their families. LatiNET will focus on low-income Latinx, which have long struggled with social, economic and health inequalities. Miami-Dade County, Florida will be the site for this study, where almost 100% of residents from in wealthiest areas have received at least one SARS-CoV-2 vaccine dose while fewer than a third of residents in poorer communities, mainly inhabited by Latinx individuals, have been vaccinated.1 We have also identified that misinformation and CT messages are prevalent in Florida.2 We will use Dr. Kanamori’s (PI) K99/R00 social network approaches3-8 and Drs. Uscinski’s and Stoler’s (Co-Is) misinformation and CT message framework2,9-11 to identify how network structures and dynamics introduce and spread misinformation and CT messages that could then influence Latinx vaccine hesitancy. We will also identify network structures and dynamics that promote discussion against SARS-CoV-2 misinformation and CT messages. LatiNET will study: 1) participants’ characteristics, 2) 624 friendship sociocentric networks, 3) 1,872 egocentric networks (family, work and health service), and 4) influencer networks, all of which will be part of our adapted NIMHD framework.12 Our AIMS are: 1) Determine how network structures and dynamics inside Latinx friendship networks shape the spread and adoption of misinformation and CT messages associated with SARS-CoV-2 vaccine hesitancy. 2) Distinguish homophily and dyadic characteristics and dynamics associated with misinformation and CT messages shared with family members, co-workers and health service providers. 3) Identify Latinx affiliations with community, celebrity, public health, political influencer and communication channels that spread CT and anti-CT messages. In all AIMS, we will also study the underlying social and structural factors associated with Latinx health decision-making (e.g., discrimination, stigma, intimate partner violence) and beliefs and behaviors tied to misinformation and CT messages (e.g., individual-level political, psychological, and social factors). LatiNET will provide new information that can i... ## Key facts - **NIH application ID:** 10707207 - **Project number:** 5R01MD018343-02 - **Recipient organization:** UNIVERSITY OF MIAMI SCHOOL OF MEDICINE - **Principal Investigator:** Mariano Juan Kanamori Nishimura - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $753,712 - **Award type:** 5 - **Project period:** 2022-09-20 → 2027-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10707207 ## Citation > US National Institutes of Health, RePORTER application 10707207, LatiNET, a Multilevel Social Network Model to Examine and Address SARS-CoV-2 Misinformation in Low-Income Latinx Communities. (5R01MD018343-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10707207. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*