Social media for early characterization of pandemic symptoms: A qualitative analysis of patient-reported COVID-19 experiences.

BACKGROUND: Patients use social media forums to discuss their medical history and healthcare experiences, providing early insight into real-world patient experiences. We analyzed COVID-19 patient experiences from Reddit social media posts. METHODS: We extracted Reddit Application Programming Interface data for the subreddit/COVID-19 positive from March to August 2020 and selected users tagged as "Tested Positive" or "Tested Positive- Me" flair and who posted at least thirty times in any calendar month, excluding users who explicitly stated location outside of the U.S. For tested-positive patients (users), we created and reviewed individual case profiles summarizing their COVID-19 symptoms, testing, and medications or treatments. Data were imported to Nvivo qualitative analysis software and qualitative coding was conducted. FINDING: There were 31 759 posts and comments from 720 users in March to May 2020 (Q1) and 40 446 posts and comments from 1649 users from June to August 2020 (Q2). Final count of "Tested Positive" was 1296 users (280 in Q1 and 1016 in Q2). Across both quarters, frequently reported symptoms included sore throat, headaches, fevers, or chills. Loss of sense of smell or taste were reported by users in early March, prior to the inclusion of this symptom to the CDC list in April and GI-related symptoms and fatigue were reported in the March to May data, before they were added as a COVID-19 associated symptom in July 2020. Users also reported in-depth descriptions of their symptoms, motivations for testing, and long-term impacts such as post-viral fatigue. INTERPRETATION: Social media data can potentially serve as an early surveillance data source in a pandemic and offer preliminary insights into patient disease experiences.

Equivalent Pore Channel Model for Fluid Flow in Rock Based on Microscale X-ray CT Imaging.

Pore-scale modeling with a reconstructed rock microstructure has become a dominant technique for fluid flow characterization in rock thanks to technological improvements in X-ray computed tomography (CT) imaging. A new method for the construction of a pore channel model from micro-CT image analysis is suggested to improve computational efficiency by simplifying a highly complex pore structure. Ternary segmentation was applied through matching a pore volume experimentally measured by mercury intrusion porosimetry with a CT image voxel volume to distinguish regions denoted as "apparent" and "indistinct" pores. The developed pore channel model, with distinct domains of different pore phases, captures the pore shape dependence of flow in two dimensions and a tortuous flow path in three dimensions. All factors determining these geometric characteristics were identified by CT image analysis. Computation of an interaction flow regime with apparent and indistinct pore domains was conducted using both the Stokes and Brinkman equations. The coupling was successfully simulated and evaluated against the experimental results of permeability derived from Darcy's law. Reasonable agreement was found between the permeability derived from the pore channel model and that estimated experimentally. However, the model is still incapable of accurate flow modeling in very low-permeability rock. Direct numerical simulation in a computational domain with a complex pore space was also performed to compare its accuracy and efficiency with the pore channel model. Both schemes achieved reasonable results, but the pore channel model was more computationally efficient.