Severe side effects from COVID-19 vaccines can be predicted by probability models
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Peer-reviewed literature demonstrates that machine learning frameworks and predictive probability models can successfully evaluate demographic and clinical data to predict various COVID-19 vaccine adverse effects and side effects with reasonable accuracy.
Due to the widespread COVID-19 vaccinations, we are focusing more on side effects to immunizations that might affect people's perceptions, and ultimately vaccine hesitancy. Machine learning (ML)-based predictive models using individual-level data serve as robust tools for predicting such events. The objective of this study was to develop and evaluate machine learning models that could predict side effects using clinical and demographic characteristics from a public dataset after administering AstraZeneca and Sinopharm COVID-19 vaccines. The performance of ML models in predicting vaccine side effects varied across doses and types of side effects. For local side effects, SVM and GB excelled after the first dose (AUC = 0.77), while XGB and RF led after the second dose (AUC = 0.87), with SHAP analysis highlighting factors like age, symptom onset day, and vaccine type. Systemic side effects showed strong performance from SVM, GB, and LR for the first dose (AUC ~ 0.75-0.77), and LR and RF for the second dose (AUC = 0.80), influenced by factors such as first-dose effects and symptom duration. For total side effects, SVM, GB, and ANN performed best for the first dose (AUC = 0.82), while RF dominated for the second dose (AUC = 0.85), with SHAP analysis emphasizing symptom onset and prior dose effects. Machine learning models, specifically SVM and RF, have been demonstrated to provide promising and with reasonable accuracy in predicting COVID-19 vaccine adverse effects, including side effects. These predictive tools can support personalized vaccination strategies, enhance monitoring systems, and reduce public hesitancy by providing data-driven insights into post-vaccination responses.
others, and/or willingness to be vaccinated. Identifying modifiable factors that influence these side effects may increase the number of people vaccinated. In this observational study, data were from individuals who received an mRNA COVID-19 vaccine between December 2020 and April 2021 and responded to at least one post-vaccination symptoms survey that was sent daily for three days after each vaccination. We excluded those with a COVID-19 diagnosis or positive SARS-CoV2 test within one week after their vaccination because of the overlap of symptoms. We used machine learning techniques to analyze the data after the first vaccination. Data from 50,484 individuals (73% female, 18 to 95 years old) were included in the primary analysis. Demographics, history of an epinephrine autoinjector prescription, allergy history category (e.g., food, vaccine, medication, insect sting, seasonal), prior COVID-19 diagnosis or positive test, and vaccine manufacturer were identified as factors associated with allergic and non-allergic side effects; vaccination time 6:00–10:59 was associated with more non-allergic side effects. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of vaccination.
Keywords: vaccination, COVID-19, side effects, allergy, time-of-day-effects, machine learning, model explanation 1. Introduction
COVID-19 vaccines have been distributed to billions of individuals worldwide and have reduced serious illness, hospitalizations, and death [ 1 ]. As of July 2022, only 61% of the world’s population has been fully vaccinated against COVID-19 [ 2 ]. An important factor for vaccine hesitancy is concern about vaccine safety, efficacy, and side effects [ 3 , 4 ]. Understanding risk factors for vaccine-related side effects—especially ones that may be modifiable—is important for clinicians, for patient safety, for patient expectations and planning, and possibly for reducing hesitancy to be vaccinated.
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