A new method for augmenting short time series, with application to pain events in sickle cell disease

Researchers across different fields, including but not limited to ecology, biology, and healthcare, often face the challenge of sparse data. Such sparsity can lead to uncertainties, estimation difficulties, and potential biases in modeling. Here we introduce a novel data augmentation method that combines multiple sparse time series datasets when they share similar statistical properties, thereby improving parameter estimation and model selection reliability. We demonstrate the effectiveness of this approach through validation studies comparing Hawkes and Poisson processes, followed by application to subjective pain dynamics in patients with sickle cell disease (SCD), a condition affecting millions worldwide, particularly those of African, Mediterranean, Middle Eastern, and Indian descent.Author summary: When studying health conditions like sickle cell disease, we often face a frustrating challenge: individual patient datasets are too small or sparse to draw reliable conclusions. We developed a method to overcome this by combining data from multiple patients who show similar patterns, effectively treating them as different snapshots of the same underlying process. We tested this approach on pain event data from sickle cell disease patients, where understanding pain patterns is crucial for improving care but individual records are often incomplete. Our method revealed that pain events in most patients follow a “self-exciting” pattern, something that couldn’t be confidently determined from individual patient data alone. This technique could help researchers in many fields where data is scarce but understanding temporal patterns is essential, from ecology to healthcare, enabling more reliable insights from limited observations.