Estimating epidemiologic dynamics from cross-sectional viral load distributions

Abstract

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

Cited by papers we hold 7

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• An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence · 2022
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• Evaluation of efficiency and sensitivity of 1D and 2D sample pooling strategies for SARS-CoV-2 RT-qPCR screening purposes · 2022
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• Leveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends · 2021
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• Genomic characterization and epidemiology of an emerging SARS-CoV-2 variant in Delhi, India · 2021
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• Effect of Delta variant on viral burden and vaccine effectiveness against new SARS-CoV-2 infections in the UK · 2021
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