Accepted for/Published in: JMIRx Med
Date Submitted: Jul 19, 2020
Open Peer Review Period: Jul 19, 2020 - Nov 4, 2020
Date Accepted: Dec 26, 2020
Date Submitted to PubMed: Aug 4, 2023
(closed for review but you can still tweet)
A Framework for Statistical Characterization of Epidemic Cycles: COVID-19 Case Study
ABSTRACT
Background:
Since the beginning of the COVID-19 pandemic, researchers and health services have been looking for patterns in the series of deaths caused by the virus, in order to try to predict the future course of the epidemic in different locations or to find relationships between the deaths and the different measures taken in infection control and treatment of infected people.
Objective:
This article presents several cycles practically closed and compares them with others that are in progress in order to show how it is possible to use similarity patterns to make predictions.
Methods:
Virtually closed cycles are compared with cycles in progress from other locations with similar patterns. In order to be able to compare populations of different sizes at different times, the cycles are normalized by known and simple methods. Three normalization methods are presented and discussed.
Results:
Several practically closed cycles are used to show their similarity to cycles in progress. The case of the city of Rio de Janeiro, Brazil, is analyzed in detail and its prediction is shown with high precision.
Conclusions:
It is evident that there is a practically universal pattern among the studied cycles, which takes a triangular shape. This repeated shape can be used to make predictions on cycles duration.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.