Early detection of seasonality and second waves prediction in the COVID-19 pandemic
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Abstract
Seasonality plays an essential role in the dynamics of many infectious diseases. In this study, we use statistical methods to show how to detect the presence of seasonality in a pandemic at the beginning of the seasonal period and that seasonality strongly affects SARS-coV-2 transmission. We measure the expected seasonality effect in the mean transmission rate of SARS-coV-2 and use available data to predict when a second wave of the COVID-19 will happen. In addition, we measure the average global effect of social distancing measures. The seasonal force of transmission of COVID-19 increases in October in the Northern hemisphere and in April in the Southern hemisphere. These predictions provide critical information for public health officials to plan their actions to combat the new coronavirus disease and to identify and measure seasonal effects in a future pandemic.
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SciScore for 10.1101/2020.09.02.20187203: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar …
SciScore for 10.1101/2020.09.02.20187203: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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