Forecasting the Spreading Trajectory of the COVID-19 Pandemic
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Abstract
Predictively forecasting future developments for the spread of the COVID-19 pandemic is extremely challenging. A recently published logistic mathematic model has achieved good predictions for infections weeks ahead. In this short communication, we summarize the Logistic spread model, which describes the dynamics of the pandemic evolution and the impacts of people social behavior in fighting against the pandemic. The new pandemic model has two parameters (i.e., transmission rate γ and social distancing d) to be calibrated to the data from the pandemic regions in the early stage of the outbreak while the social distancing is put in place. The model is capable to make early predictions about the spreading trajectory in the communities of any size (countries, states, counties and cities) including the total infections, the date of peak daily infections and the date of the infections reaching a plateau if the testing is sufficient. The results are in good agreement with data and have important applications for ongoing outbreaks and similar infectious disease pandemics in the future.
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SciScore for 10.1101/2021.03.26.21254429: (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 10.1101/2021.03.26.21254429: (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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