Estimating epidemiologic dynamics from cross-sectional viral load distributions
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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.
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SciScore for 10.1101/2020.10.08.20204222: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Despite the challenges of sampling variability, individual-level differences in viral kinetics, and limitations in comparing results from different laboratories or instruments, our results demonstrate that RT-qPCR Ct values, with all of their quantitative variability for an individual, can be highly informative of population-level dynamics. This information is lost when measurements are reduced to binary classifications. Our results …
SciScore for 10.1101/2020.10.08.20204222: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Despite the challenges of sampling variability, individual-level differences in viral kinetics, and limitations in comparing results from different laboratories or instruments, our results demonstrate that RT-qPCR Ct values, with all of their quantitative variability for an individual, can be highly informative of population-level dynamics. This information is lost when measurements are reduced to binary classifications. Our results demonstrate that this method can be used to estimate epidemic growth rates based on data collected at a single time point, and independent of assumptions about the intensity of testing. Comparisons of simulated Ct values and observed Ct values with growth rates and Rt estimates validate this general approach. Results should be interpreted with caution in cases where the observed Ct values are not from a population census or a largely random sample, or when there are very few samples with detectable viral load. When testing is based primarily on the presence of symptoms or follow-up of contacts of infected individuals, people may be more likely to be sampled at specific times since infection and thus the distribution of observed Cts would not be representative of the population as a whole. This method may be most useful in settings where representative surveillance samples can be obtained independent of COVID-19 symptoms, such as the REACT study (34). These methods allow municipalities to evaluate and monitor, in real-time, the role of various epid...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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