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Personalized predictions of adverse side effects of the COVID-19 vaccines

Background Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620–0.686, 0.685–0.716, 0.632–0.727, 0.527–0.598, 0.548–0.655, 0.545–0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777–0.867, 0.795–0.848, 0.857–0.906, 0.788–0.875, 0.683–0.850, and 0.486–0.680, respectively. Conclusions Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Although vaccination is essential to limit the spread of COVID-19, its success is dependent on the 73 fact that enough individuals would be willing to get vaccinated, but some proportions of the general 74 public show hesitancy. This vaccine hesitancy originates from various concerns, from distrusting 75 governments and pharmaceutical companies to fearing the adverse side effects of vaccines. 76 Vaccines are one of the most potent weapons against many infectious diseases, but at the same 77 time, their side effects still generate intricacies among diverse populations. 3-7 78 COVID-19 vaccines have shown numerous adverse side effects ranging from local side effects to 79 systemic side effects. These adverse side effects mostly include minor and mild side effects such 80 as headache, fever and pain in the injection area. 8,9 On the other hand, some rare but concerning 81 severe side effects such as thrombotic events and myocarditis cases have been reported. [10][11][12][13] 82 correlations between medical and personal characteristics and adverse side effects following the 89 injection of COVID-19 vaccines, which have revealed a clear correlation between some aspects of 90 vaccine recipients such as age and sex with the experienced adverse reactions. 9,18 91 Recognizing, anticipating, and predicting the adverse side effects of vaccines, including COVID-92 19 vaccines, can decrease anxiety and pave the way for the next steps toward a personalized 93 vaccinology approach. 19 To the best of our knowledge, no study to date has addressed this critical 94 matter for any drug or vaccine. 95 Based on the correlation between health-related traits and adverse side effects of vaccines, 96 applying the medical and personal records may support a personalized estimate of each individual's 97 adverse effects. Finding a correlation between the medical and personal characteristics and the 98 occurrence of an adverse reaction can only be achievable through a large dataset and an enormous 99 amount of data. Due to this reason, these predictions can only be calculated for milder adverse side 100 effects that happen with high frequency in the population and finding a correlation between health-101 related traits of an individual and the rare severe adverse side effects can not be achieved. Of 102 course, even predicting the more common adverse side effects depends on many factors. 103 The presence of an extensive number of parameters potentially affecting the adverse effects that 104 one would experience makes the predictions of these side effects a complex issue. Finding 105 correlation and building prediction models between these high numbers of parameters can be best 106 handled with more complex methodologies such as Machine Learning (ML) and Artificial 107 intelligence (AI). 20 108 AI in healthcare has undergone meaningful progress in recent years; AI has been used as a tool for 6 Since the onset of this pandemic, AI has played a pivotal role in mitigating the impacts of COVID-112   19. Starting from predicting the COVID-19 dynamics, scanning for drug candidates from   113   previously approved drugs, vaccine development, predicting the severity of COVID-19 induced   114 infection, and even analyzing the behavioral changes towards COVID-19 vaccination. 24-29 115 In this study using health-related characteristics and personal traits, a machine learning approach 116 was designed to predict the potential adverse side effects after COVID-19 vaccination.

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Source of data and participants 122 The prospective data of the most common adverse side effects were utilized following COVID-19 123 vaccination in 19943 participants. Data collection was performed using a completely anonymous 124 online survey from health care personnel at 90 hospitals in Iran and at the University Hospital of 125 Lausanne in Switzerland. No personal information was gathered during the process to follow our 126 anonymity strategy. The healthcare authorities informed the vaccination recipients at the 127 vaccination centers to fill out the online survey at least 72 hours following vaccination. The 72 128 hours interval was done as some adverse reactions may appear more than 24-48 hours following 129 the vaccination. 130 There were no additional selection criteria outside the criteria used for the vaccine eligibility. The 7 participants had not received the second dose of their vaccine upon completing the survey; the data 134 from these participants were only used to train the first dose models. 135 Overall , 6022, 7290, 5279, 802, 277, and 273

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To ensure that models will not be overfitted on training data and are generalizable to unseen real-164 world data, 20% of the data was kept as a test dataset. A 5-fold cross-validation algorithm was 165 performed on the remaining 80% of the data. 33 For this purpose, all records were randomly split 166 into five subsets. Four subsets were used as training data, and one subset was held for model testing 167 as a validation set. The cross-validation process was repeated four more times, with each of the 168 five subsets being used once as the validation data. Model performance metrics were subsequently 169 calculated separately for each training and validation model. 170 To compare training and validation metrics, they need to have a similar positive and negative data 171 points distribution; this can be achieved by splitting vaccine receivers who showed specific side 172 effects from those who did not, and putting them into five stratified subsets, then combining them It is important to note that the first and second dose models have been trained and validated 186 independently. The training procedure for the second dose models was similar to the first dose, 187 except that this time models also had access to the first dose side effect data as the input.   In this study, a novel machine-learning based approach was designed to predict the occurrence 318 possibility of each common side effect for six widely approved COVID-19 vaccines solely based 319 on recipients' personal and health-related traits. To the best of our knowledge, this is the first study 320 to use a machine learning method to predict the occurrence of adverse side effects of any vaccine 321 or drug based on an individual's personal and health-related characteristics. characteristics and their adverse reactions. Age had the most substantial impact on the prediction 324 of the side effects of the first dose, which was inversely proportional to the side effects occurrence. 325 This effect is likely due to a more robust immune response in younger individuals leading to more 326 side effects. Interestingly, one of the other influential factors was a history of COVID-19 infection. had not been rolled out, and it seems logical that the models should be extended to cover the 365 booster vaccines for an increase in the practicality of the approach. The incapability to predict 366 severe adverse side effects is another limitation of our model, which due to the rare occurrence of 367 these reactions, seems unlikely to be achievable. Furthermore, the statistical significance of the input-output correlation is not as strong for the COVAXIN, BNT162b2, and the mRNA-1273 369 vaccines, which is mainly due to a lower number of participants for these vaccines. 370 In future studies, this approach can be enhanced by including more input and output data for both 371 more practicality and more accuracy. Furthermore, this approach can easily be generalized to other 372 vaccines and drugs and should not remain exclusive to COVID-19 vaccines. Moreover, a clinical 373 validation study to support the real-world application of these predictions among the general public 374 is another subject that must be studied in further investigations of this approach. 375 Ultimately, we anticipate that providing the public with a personalized prediction of their adverse

Figure 2. Contribution of Each Feature to The Side Effect Prediction for the First Dose of Vaccines
Negative coefficients (blue circles) reflect a decreasing effect on the side effect probability. The positive coefficients (red circles) show features with an increasing effect on the probability. For the continuous variables (age and BMI) a higher value indicates a positive coefficient. Also, we encoded Male as 0 and Female as 1 in the SEX parameter.

Prediction for Second Dose of Vaccines
Negative coefficients (blue circles) reflect a decreasing effect on the side effect probability. The positive coefficients (red circles) show features with an increasing effect on the probability. For the continuous variables (age and BMI) a higher value indicates a positive coefficient. Also, we encoded Male as 0 and Female as 1 in the SEX parameter.

Table 1. Models' Performance for The First Dose of Vaccines' Side Effect Prediction
Model average performance, calculated on training, validation, and test set using AUC-ROC parameter.