Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.

Large data requirements are often the main hurdle in training neural networks. Convolutional neural network (CNN) classifiers in particular require tens of thousands of pre-labeled images per category to approach human-level accuracy, while often failing to generalized to out-of-domain test sets. The acquisition and labelling of such datasets is often an expensive, time consuming and tedious task in practice. Synthetic data provides a cheap and efficient solution to assemble such large datasets. Using domain randomization (DR), we show that a sufficiently well generated synthetic image dataset can be used to train a neural network classifier that rivals state-of-the-art models trained on real datasets, achieving accuracy levels as high as 88% on a baseline cats vs dogs classification task. We show that the most important domain randomization parameter is a large variety of subjects, while secondary parameters such as lighting and textures are found to be less significant to the model accuracy. Our results also provide evidence to suggest that models trained on domain randomized images transfer to new domains better than those trained on real photos. Model performance appears to remain stable as the number of categories increases.

Large data requirements are often the main hurdle in training neural networks. Synthetic data is a cheap and efficient solution to assemble such large datasets. Using domain randomization, we show that a sufficiently well generated synthetic image dataset can be used to train a neural network classifier, achieving accuracy levels as high as 88% on 2 category classification. We show that the most important domain randomization parameter is a large variety of subjects, while secondary parameters such as lighting and textures are not. Based on our results, there is reason to believe that models trained on domain randomized images transfer to new domains better than those trained on real photos. Model performance seems to diminish slightly as the number of categories increases.