A Statistical Definition of Epidemic Waves
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Abstract
The timely identification of expected surges of cases during infectious disease epidemics is essential for allocating resources and preparing interventions. Failing to detect critical phases in time may lead to delayed implementation of interventions and have serious consequences. This study describes a simple way to evaluate whether an epidemic wave is likely to be present based solely on daily new case count data. The proposed measure compares two models that assume exponential or linear dynamics, respectively. The most important assumption of this approach is that epidemic waves are characterized rather by exponential than linear growth in the daily number of new cases. Technically, the coefficient of determination of two regression analyses is used to approximate a Bayes factor, which quantifies the support for the exponential over the linear model and can be used for epidemic wave detection. The trajectory of the coronavirus epidemic in three countries is analyzed and discussed for illustration. The proposed measure detects epidemic waves at an early stage, which are otherwise visible only by inspecting the development of case count data retrospectively. Major limitations include missing evidence on generalizability and performance compared to other methods. Nevertheless, the outlined approach may inform public health decision-making and serve as a starting point for scientific discussions on epidemic waves.
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SciScore for 10.1101/2022.05.04.22274677: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A central limitation of the presented approach that it relies on the number of reported cases, which can be subject to inconsistencies due to variation in reporting and testing strategies. Thus, the identified waves do not necessarily reflect changes in the true number of infections. However, it is unlikely that testing and reporting …
SciScore for 10.1101/2022.05.04.22274677: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A central limitation of the presented approach that it relies on the number of reported cases, which can be subject to inconsistencies due to variation in reporting and testing strategies. Thus, the identified waves do not necessarily reflect changes in the true number of infections. However, it is unlikely that testing and reporting strategies alone are able to produce epidemic waves with a very strong support from the proposed epidemic wave indicator. The calculation of bootstrap intervals (which should be interpreted as reference intervals rather than traditional confidence limits) can be helpful for assessing data-related uncertainty of the calculations. Still, this issue deserves further exploration. Another challenge is posed by the question, which time horizon should be used to calculate the wave indicator. In the examples, indicators with a longer time horizon (two and three months) seem to have worked better and more clearly at detecting epidemic waves. However, the choice is likely to depend on the characteristics of the waves, of which description the measure is intended to use. For epidemics with an annual periodicity of major waves, a time horizon of several months might be appropriate. However, until clearer guidance is available, I suggest using multiple timeframes, like it was done in the present study. An interesting characteristic of the proposed measure that it can also be used to detect phases of exponential decline in new case counts, which was not follow...
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.
- No funding statement was detected.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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