Nowcasting and forecasting COVID-19 waves: the recursive and stochastic nature of transmission
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Abstract
We propose a parsimonious, yet effective, susceptible–exposed–infected–removed-type model that incorporates the time change in the transmission and death rates. The model is calibrated by Tikhonov-type regularization from official reports from New York City (NYC), Chicago, the State of São Paulo, in Brazil and British Columbia, in Canada. To forecast, we propose different ways to extend the transmission parameter, considering its estimated values. The forecast accuracy is then evaluated using real data from the above referred places. All the techniques accurately provided forecast scenarios for periods 15 days long. One of the models effectively predicted the magnitude of the four waves of infections in NYC, including the one caused by the Omicron variant for periods of 45 days using out-of-sample data.
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SciScore for 10.1101/2022.04.12.22273804: (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:Finally, the present study has some limitations since we considered only the class of SEIR-type models. There are a number of other ways to model the dynamics of infectious diseases [2], but accounting for them is beyond the scope of this article that is intended to enlighten the stochastic nature of transmission. Moreover, we did not perform a rigorous statistical analysis of the transmission parameter and other rates defining the …
SciScore for 10.1101/2022.04.12.22273804: (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:Finally, the present study has some limitations since we considered only the class of SEIR-type models. There are a number of other ways to model the dynamics of infectious diseases [2], but accounting for them is beyond the scope of this article that is intended to enlighten the stochastic nature of transmission. Moreover, we did not perform a rigorous statistical analysis of the transmission parameter and other rates defining the SEIR-type model in Eqs. (2)–(5) since the calibration techniques considered in this work are deterministic. The statistical analysis of SEIR-type models and their parameters will be subject of a future work.
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.
Results from scite Reference Check: We found no unreliable references.
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