Estimating epidemiologic dynamics from cross-sectional viral load distributions

• DOI: 10.1126/science.abh0635
• Publication Date: 2021-06-03T19:13:23Z
• Authors: James A. Hay; Lee Kennedy-Shaffer; Sanjat Kanjilal; Niall J. Lennon; Stacey B. Gabriel; Marc Lipsitch; Michael J. Mina
• Keywords: Pandemic; Robustness

Estimating an epidemic's trajectory is crucial for developing public health responses to infectious diseases, but case data used such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance-in form cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing-changes during epidemic. Thus, Ct even limited numbers samples can provide improved estimates trajectory. Combining multiple improves precision and robustness this estimation. apply our methods surveillance conducted severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety settings offer alternative approaches real-time epidemic trajectories outbreak management response.