# Modeling the Coupled Dynamics of Influenza Transmission and Vaccination Behavior

> **NIH NIH R01** · RAND CORPORATION · 2020 · $332,611

## Abstract

   
DESCRIPTION (provided by applicant): Decisions on whether or not to get vaccinated for seasonal influenza are largely motivated by attitudes and beliefs of the risks of infection and benefits of being vaccinated. The risk of influenza infection can change from season to season and depends on one's own vaccination status and the vaccination coverage among one's social net- work. Furthermore, attitudes and beliefs related to risk of infection can spread over a social
network. Thus, in addition to personal attitudes, beliefs and experiences with vaccination and treatments for influenza, interactions of individuals on and characteristics of the social network
can play important roles in shaping the nature and severity of influenza outbreaks and the effectiveness and cost of promoting vaccination. Our previous exploratory research has confirmed a strong dynamical interplay between behavior to get vaccinated, influenza epidemiology and social network structures. We collected nationally-representative cross- sectional survey data on behavioral factors associated with the decision to seek influenza vaccination. We then used these data to inform the development of an innovative agent-based model (ABM) that allowed experiences from past influenza seasons affect decisions to get vaccinated in the current season, and thus influence the course of an epidemic at the population level. In contrast to past and standard approaches, our models include two important properties of human decision-making: (a) memory and adaptability from past experiences and (b) peer-influences via rumor/information spreading. However, our ABM assumed a demographically homogenous population, considered just idealized social network structures and only considered a reduced set of attitudes and beliefs that affect the behavior to get vaccinated as suggested by our survey. In the proposed research we are interested in enhancing and refining our ABM by allowing our population to vary in terms of the demographic characteristics that influence vaccination, predisposition towards vaccination, and exposure to advice and opportunities for vaccination. We will conduct a four-year longitudinal panel study to construct an empirical behavioral model of decisions to get vaccinated for seasonal influenza that will include questions on additional attitudinal factors and considers a wider set of behavioral mechanisms. We will construct an improved social contact network structure that is representative of a large town/small city within the United States (US). We will consider different
overlaying social contact network structures representative of different types of mixing. This approach will allow us to model social interactions and disease spread at a finer granularity and a higher level of realism than any existing random network model. We will calibrate our model in order to reproduce general yearly US trends of vaccination rates and infections by socio-demographic strata. We will then use our model to evalu...

## Key facts

- **NIH application ID:** 9858221
- **Project number:** 5R01AI118705-05
- **Recipient organization:** RAND CORPORATION
- **Principal Investigator:** Andrew Parker
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $332,611
- **Award type:** 5
- **Project period:** 2016-02-05 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9858221

## Citation

> US National Institutes of Health, RePORTER application 9858221, Modeling the Coupled Dynamics of Influenza Transmission and Vaccination Behavior (5R01AI118705-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9858221. Licensed CC0.

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