Abstract: This research intends to answer: how do (i) generation frequency and (ii) retweeting count of health agencies' messages impact the exposure of the general users to vaccine-related misinformation on Twitter? We creatively employed a Susceptible-Infected-Recovered (SIR) System Dynamics paradigm to model interactions between message dissemination of 168 U.S. health agencies and proportions of users who are at different exposure statuses to misinformation, namely “Susceptible”, “Infected”, or “Recovered” status. The SIR model was built based on the vaccine-relevant tweets posted over November and December in 2020. Our preliminary outcomes suggest that augmenting the generation frequency of agencies' messages and increasing retweeting count can effectively moderate the exposure risk to vaccine-related misinformation. This model illustrates how health agencies may combat vaccine hesitancy through credible information dissemination on social media. It offers a novel approach for crisis informatics studies to model different information categories and the impacted population in the complex digital world.