Identifying psychological predictors of SARS-CoV-2 vaccination: A machine learning study

Major barriers to addressing SARS-CoV-2 vaccine hesitancy include limited knowledge of what causes delay/refusal of SARS-CoV-2 vaccination and limited ability to predict who will remain unvaccinated over significant time periods despite vaccine availability. The present study begins to address these barriers by developing a machine learning model that prospectively predicts who will persist in not vaccinating against SARS-CoV-2.

Unvaccinated individuals (n = 325) who completed a baseline survey were followed over the six-month period when vaccines against SARS-CoV-2 were first widely available (April-October 2021). A random forest model was used to predict who would remain unvaccinated against SARS-CoV-2 from their baseline measures, including demographic information (e.g., age), medical history (e.g., of influenza vaccination), Health-Belief Model constructs (e.g., perceived vaccine dangerousness), conspiracist ideation, and task-based metrics of vulnerability to conspiracist ideation (e.g., tendency toward illusory pattern perception).

The resulting model significantly predicted vaccination status (AUC-PR = 0.77, 95%CI [0.56 0.90]). At the optimal probability threshold determined by the Generalized Threshold Shifting Protocol, the model was moderately precise (0.83) when identifying individuals who remained unvaccinated (n = 80), and had a very low rate (0.04) of false-positives (incorrectly suggesting that individuals remained unvaccinated). Permutational importance tests suggested that baseline SARS-CoV-2 vaccine intentions conveyed the most information about future SARS-CoV-2 vaccination status. Conspiracist ideation was the second most informative predictor, suggesting that misinformation influences vaccination behavior. Other important predictors included perceived vaccine dangerousness, as expected under the Health Belief Model, and influenza vaccination history.

The model we developed can accurately and prospectively identify individuals who remain unvaccinated against SARS-CoV-2. It could therefore facilitate empirically-informed allocation of interventions that encourage vaccine uptake. The predictive value of conspiracist ideation, perceived vaccine dangerousness, and vaccine intentions in our model is consistent with potential causal relations between these variables and SARS-CoV-2 vaccine uptake.