COVID-19 mRNA Vaccine Degradation Prediction Using Denoising Autoencoder and Multi-headed Attention Mechanism

Introduction

mRNA Vaccines:

Unlike traditional vaccines, mRNA vaccines work by introducing a small piece of the SARS-CoV-2 virus's mRNA into the body, prompting cells to produce a protein similar to the one on the virus's surface. The immune system then recognizes this protein as foreign and creates antibodies and trains T-cells to fight the virus if the body is exposed in the future. mRNA is inherently unstable and can degrade quickly. This is why mRNA vaccines, like those from Pfizer-BioNTech and Moderna, require ultra-cold storage.

Problem Statement:

The instability and rapid degradation of mRNA vaccines, especially those developed for COVID-19, pose significant challenges in storage, transportation, and overall vaccine efficacy. Predicting the degradation rate of these vaccines under various conditions is crucial to ensure their potency and effectiveness when administered. A reliable prediction model can aid in optimizing storage conditions, reducing vaccine wastage, and ensuring the delivery of effective doses to the end recipients.

Common Methodologies:

Time-Series Analysis: Given the temporal nature of degradation, time-series models like ARIMA or Prophet can be used to predict future degradation based on past data.

Regression Models: Linear regression, decision trees, or ensemble methods like random forests can be used to predict degradation based on features like temperature, humidity, and time.

Neural Networks: Deep learning models, especially recurrent neural networks (RNNs) or Long Short-Term Memory (LSTM) networks, which are designed to handle sequential data, can be employed.

Methodology Used :

Training Scheme:

Denoising Autoencoder Training: Utilize all available data, including both training and test datasets, to train a denoising autoencoder model. The primary purpose of this step is to learn a robust representation of the data and reduce the noise inherent in real-world datasets.

Finetuning: Using the weights from the denoising autoencoder model, finetune the model to predict specific targets, such as reactivity, which can be indicative of mRNA degradation.

Network Architecture:

Inputs: The initial data fed into the model, which can include features like temperature, humidity, time, and other environmental factors.

Conv1D Layers: These are convolutional layers designed to detect local patterns or features in the input data.

Aggregation of Neighborhoods: This step aggregates information from neighboring data points, potentially capturing the local context around each data point.

Multi-Head Attention: This mechanism allows the model to focus on different parts of the input data differently, capturing complex relationships and dependencies.

Conv1D Layer: Another convolutional layer to further refine the features detected.

Prediction: The final layer that outputs the predicted degradation rate or reactivity of the mRNA vaccine.