Mathematical Model Based COVID-19 Prediction in India and its Different States
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Abstract
By employing the successive approximation method to the real-time data of India and its different states, we have predicted the bounds of the spread of COVID-19 in India and its various states. The calculated lower and upper bound of patients (deaths) till 10th June 2020 comes out to be 79496 (3835) and 241759 (7045), respectively. States like Delhi, Gujarat, Maharashtra, Punjab, Rajasthan and Tamil Nadu are the spike states as suggested by the range of expected COVID-19 patients and deaths. Impact of return of stranded pilgrims from Nanded (Maharashtra) has also been looked into in the case of Punjab. It has been found that Punjab may see ~ 5 times increase in the lower bound of expected patients till 10th June 2020 due to the return of pilgrims from Maharashtra. Our study provides an insight into the possible number of expected patients and deaths in near future that may be of importance for the respective governments to be ready with the appropriate preventive measures and logistics to put appropriate infrastructure and medical facilities in place to manage the spread of deadly virus and go down the flattening curve.
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SciScore for 10.1101/2020.05.16.20104232: (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: 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…
SciScore for 10.1101/2020.05.16.20104232: (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: 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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