Vaccine hesitancy in the post‑vaccination COVID‑19 era: a machine learning and statistical analysis driven study

Study by Himanshu Gupta · Om Prakash Verma.
The COVID-19 pandemic has badly affected people of all ages globally. Therefore, its vaccine has been developed and made available for public use in unprecedented times. However, because of various levels of hesitancy, it did not have general acceptance. The main objective of this work is to identify the risk associated with the COVID-19 vaccines by developing a prognosis tool that will help in enhancing its acceptability and therefore, reducing the lethality of SARS-CoV-2.

1. Adverse effects of COVID-19 vaccination: Machine learning and statistical approach Rama Irsheidat

2. 01 02 03 04 05 TABLE OF CONTENTS SECTION Introduction SECTION Goal SECTION Methodology SECTION Conclusion SECTION Critique

3. INTRODUCTION 01.

4. Vaccination Is a well-accepted reliable approach to preventing diseases. It has proven to be one of the most effective strategies to control pandemics, such as the SARS-CoV-2 outbreak. All vaccines result in at least a small number of patients that demonstrate some kind of post-vaccination side effects. Although vaccines are life-saving medications, they can sometimes result in an after-effect, sometimes even resulting in severe symptoms, although with a low probability.

5. GOAL 02.

6. I dentify and analyze the most probable causes in the individuals' medical history that resulted in adverse reactions to vaccination. Identify the key symptoms that indicate the cause or causes of the adverse reactions . Developing the ML models for the prediction and classification of individuals most susceptible to the adverse effects of vaccination and therefore, may require high medical attention.

7. METHODOLOGY 03.

8. LET’S START Dataset & Feature extraction STEP 1 Statistical methods & ML models STEP 2 Technology & Evaluation metrics STEP 3 Discussion STEP 4 Result STEP 5

9. Vaccine Adverse Event Reporting System (VAERS) dataset General data (Such as:   Age , Sex , Current illness, Medical history, Allergic history, etc. ) Vaccination status Post-vaccination symptoms More than 354 thousand samples Individuals who have been vaccinated between 1st January 2021 to 11th June 2021 and also reported adverse reactions. Dataset

10. Convert features in textual format into attributes by employing the String matching technique. Feature extraction

11. Diseases with greater than 500 counts in medical history have been considered as attributes and the rest neglected . Based on the frequency of symptoms, a total of 49 symptoms have been used here for the analysis . Feature extraction

12. 85 different features for over 354 thousand samples.

13. C hi-square ( χ2 ) T est The test of independence analyses the association between various attributes of the dataset and the outcome (target).  T arget variables ( Outcome) Died H ospitalized   COVID-19 positive Statistical methods

14. Logistic Regression (LR) Random Forest (RF)  Naive Bayes (NB ) Light Grading Boosting Machine (LGBM) Multilayer feed-forward Perceptron (MLP) ML models

15. Logistic Regression (LR) Logistic regression is a predictive analysis . U sed to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. The Line is fit to the data using maximum likelihood.

16. Light Gradient Boosting Machine (LGBM) LightGBM   is a gradient boosting algorithm that employs a tree-based learning framework. As compared to other tree-based frameworks, it grew trees vertically (leaf-wise) whereas, others horizontally (level-wise ).

17. Multilayer feed-forward Perceptron (MLP) The MLP is a type of neural network which may have one input layer, multiple hidden layers, and one output layer. All these layers contain several artificial neurons that have been connected with each other in a unidirectional manner by mesh arrangements. It is a mathematical model which aims to mimic the functioning of human brains.

18. Python Keras ( Keras is an open-source software library that provides a Python interface for artificial neural networks. ) Technology

19. Evaluation metrics

20. The investigation has been carried out in three scenarios: Based on medical history only Based on the reaction of vaccination only Based on both medical history and adverse reaction T he important contributing features have been identified by both statistical analysis and developed ML models (LR, RF, and LGBM ) in all scenarios. All the developed ML models have been used to predict the important key outcomes of interest ( Death, Hospitalization , and COVID-19 positive ). Discussion

21. Result

22. Died Hospitalized COVID-19 positive

23. Result

24. Died Hospitalized COVID-19 positive

25. Result

26. Died Hospitalized COVID-19 positive

27. CONCLUSION 04.

28. The people in the age group of 50–70 have been found as most susceptible to the SARS-CoV-2 . The male population has been identified as more vulnerable than to female population. The population with a history of life-threatening diseases should be vaccinated in close observation . The most common post-vaccination symptoms have been identified. Most of these major symptoms have been found normal and do not indicate towards any sign of serious and immediate concern . Conclusion

29. CRITIQUE 05.

30. Strengths : T his has been the very first study that analyses the impact of COVID-19 vaccines employing more than 354 thousand samples . Weaknesses : In dataset there was twice the number of female participants compared to male participants and almost half of them were recorded as regularly taking other medications. Limitations: The study is conducted o n USA population . Critique

31. THANKS!