Accurate detection and tracking of model organisms such as C. elegans worms remains a fundamental task in behavioral studies. Traditional Machine Learning (ML) and Computer Vision (CV) methods produce poor detection results and suffer from repeated ID switches during tracking under occlusions and noisy backgrounds. Using Deep Learning (DL) methods, the task of animal tracking from video recordings, like those in camera trap experiments, has become much more viable. The large amount of data generated in ethological studies, makes such models suitable for real world scenarios in the wild. We propose Deep-Worm-Tracker, an end to end DL model, which is a combination of You Only Look Once (YOLOv5) object detection model and Strong Simple Online Real Time Tracking (Strong SORT) tracking backbone that is highly accurate and provides tracking results in real time inference speeds. Present literature has few solutions to track animals under occlusions and even fewer publicly available large scale animal re-ID datasets. Thus, we also provide a worm re-ID dataset to minimize worm ID switches, which, to the best of our knowledge, is first-of-its-kind for C. elegans. We are able to track worms at a mean Average Precision (mAP@0.5) > 98% within just 9 minutes of training time with inference speeds of 9-15 ms for worm detection and on average 27 ms for worm tracking. Our tracking results show that Deep-Worm-Tracker is well suited for ethological studies involving C. elegans.

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The slowly decaying viral dynamics, even after 2–3 weeks from diagnosis, is one of the characteristics of COVID-19 infection that is still unexplored in theoretical and experimental studies. This long-lived characteristic of viral infections in the framework of inherent variations or noise present at the cellular level is often overlooked. Therefore, in this work, we aim to understand the effect of these variations by proposing a stochastic non-Markovian model that not only captures the coupled dynamics between the immune cells and the virus but also enables the study of the effect of fluctuations. Numerical simulations of our model reveal that the long-range temporal correlations in fluctuations dictate the long-lived dynamics of a viral infection and, in turn, also affect the rates of immune response. Furthermore, predictions of our model system are in agreement with the experimental viral load data of COVID-19 patients from various countries.