Dynamic Data-Driven Algorithm to Predict the Cumulative COVID-19 Infected Cases Using Susceptible-Infected-Susceptible Model
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Abstract
In recent times, researchers have used Susceptible-Infected-Susceptible (SIS) model to understand the spread of pandemic COVID-19. The SIS model has two compartments, susceptible and infected. In this model, the interest is to determine the number of infected people at a given time point. However, it is also essential to know the cumulative number of infected people at a given time point, which is not directly available from the SIS model’s present structure. In this work, we propose a modified structure of the SIS model to determine the cumulative number of infected people at a given time point. We develop a dynamic data-driven algorithm to estimate the model parameters based on an optimally chosen training phase to predict the same. We demonstrate the proposed algorithm’s prediction performance using COVID-19 data from Delhi, India’s capital city.
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SciScore for 10.1101/2021.03.24.21253599: (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: Thank you for sharing your code.
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:- Th…
SciScore for 10.1101/2021.03.24.21253599: (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: Thank you for sharing your code.
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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