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Dear Editor, we want to thank you for the opportunity offered by this prestigious journal.
We appreciate the comments on our paper [
As explained before, we focused on the most significant cases and removed the items cited by the reviewer as excessive.
We cited in the review an article that shows how often the Gaussian models failed in the predictions [
As answered in the previous item, other authors already show that classic models have failed in their predictions. What we are looking for is a simple model that can be used by health authorities, and at the same time be computationally efficient. In the text, using the appropriate reference [
Citations have been properly formatted and organized.
The authors are grateful for the comments of Reviewer X [
The aim of the study is now clearly stated in the first two paragraphs of the Introduction section. A hypothesis was successfully tested using the Kolmogorov-Smirnov method in the Results section.
The subtopic “Nondimensional Characteristics of Epidemic Cycles” introduces the triangular distribution, its assumptions, and its limitations in the context of the COVID-19 pandemic. It is complemented by the subtopic “Predictability by Similarity.”
In the Methods section, the source and date of data collection are explicitly stated (Worldometer’s COVID-19 portal, as of July 9, 2020). A copy of this data has now been provided in a separate Excel spreadsheet.
The terms were checked and fixed; to the best of our knowledge, they now match those used by experts in the field.
Regarding the journal instructions: “Using the structure used in this paper, please consolidate manuscript 23997 and 23998 into one cohesive narrative, taking into account peer-review feedback provided by the reviewers on those submissions. This way, we can present one paper with your aggregated findings in JMIRx.” We followed these recommendations and integrated the three articles originally called “Identification of Patterns in Epidemic Cycles and Methods for Estimating Their Duration: COVID-19 Case Study,” “COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling,” and “COVID-19: Estimation of the Actual Onset of Local Epidemic Cycles, Determination of Total Number of Infective, and Duration of the Incubation Period” into a single narrative. The resulting paper represents not only the combination of the content of the three others but an integrated narrative that describes the statistical framework developed by us to analyze the epidemic cycles. Thus, given that the resulting content reflects this integrated work, we find it more coherent to change the title of the article to “A Framework for a Statistical Characterization of Epidemic Cycles: COVID-19 Case Study.” In order to make the resulting text more fluid, we concentrated on the data analyzed in three countries (Germany, Italy, and Sweden), leaving the case studies related to Brazil to Multimedia Appendix 3. Other cases originally studied during the development of the statistical framework can still be found in the preprints duly referenced in the text. In addition, all the data obtained from the referred public databases, as well as all the calculations carried out both in the main paper and in Multimedia Appendix 3, are organized in three different worksheets (Multimedia Appendices 2-4), in order to facilitate the verification and reproduction of the results by readers.