Time series forecasting, as one of the fundamental machine learning areas, has attracted tremendous attentions over recent years. The solutions have evolved from statistical machine learning (ML) methods to deep learning techniques. One emerging sub-field of time series forecasting is individual disease progression forecasting, e.g., predicting individuals' disease development over a few days (e.g., deteriorating trends, recovery speed) based on few past observations. Despite the promises in the existing ML techniques, a variety of unique challenges emerge for disease progression forecasting, such as irregularly-sampled time series, data sparsity, and individual heterogeneity in disease progression. To tackle these challenges, we propose novel Conditional Neural Ordinary Differential Equations Processes (CNDPs), and validate it in a COVID-19 disease progression forecasting task using audio data. CNDPs allow for irregularly-sampled time series modelling, enable accurate forecasting with sparse past observations, and achieve individual-level progression forecasting. CNDPs show strong performance with an Unweighted Average Recall (UAR) of 78.1%, outperforming a variety of commonly used Recurrent Neural Networks based models. With the proposed label-enhancing mechanism (i.e., including the initial health status as input) and the customised individual-level loss, CNDPs further boost the performance reaching a UAR of 93.6%. Additional analysis also reveals the model's capability in tracking individual-specific recovery trend, implying the potential usage of the model for remote disease progression monitoring. In general, CNDPs pave new pathways for time series forecasting, and provide considerable advantages for disease progression monitoring.

The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we propose a voice-based framework to automatically detect individuals who have tested positive for COVID-19. We evaluate the performance of the proposed framework on a subset of data crowdsourced from our app, containing 828 samples from 343 participants. By combining voice signals and reported symptoms, an AUC of 0.79 has been attained, with a sensitivity of 0.68 and a specificity of 0.82. We hope that this study opens the door to rapid, low-cost, and convenient pre-screening tools to automatically detect the disease.

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AuDeep toolkit, and deep feature extraction from pre-trained CNNs using the Deep Spectrum toolkit; in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis.

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.