Evaluating growth pattern and assessing future scenario of COVID-19 epidemic of India
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Abstract
COVID-19 the modern pandemic has spread across the world at a rapid pace. SARS-CoV 2 is highly transmissible and the rate of infection is exponential for heavily infected countries. Asymptotic carriers and longer incubation period have been key towards such a large-scale distribution of disease. Data released by official authorities on COVID-19 cases is significantly affected by various factors such as size of sample, incubation period of disease and time taken to test the sample. These factors mask the useful pattern (signal) of disease spread. Thus, an ingenious method to group data into cycles of five and seven days, for studying pattern of disease spread is undertaken. Occurrence of recurrent peaks as indicated by Adjusted Rate of infection per day indicated the spread of disease has been non-uniform. Currently, India is yet to reach the critical point (peak of epidemic) with adjusted daily cases more than 1000. Increasing testing capacity along with random sampling and sample pooling can help in preventing formation of these peaks in future. The proposed method helps in assessing the current state and for predicting future scenarios epidemics.
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SciScore for 10.1101/2020.05.02.20087544: (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: We detected the following sentences addressing limitations in the study:3.6 Limitations of the method: As of other statistical methods there are few limitations for the present method. When the spread of disease is slow the ARcn and Rcmi plot can’t depict the pattern of disease spread. Thus, the method is useful where local transmission or community spread of disease is prevalent. Pattern of disease spread …
SciScore for 10.1101/2020.05.02.20087544: (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: We detected the following sentences addressing limitations in the study:3.6 Limitations of the method: As of other statistical methods there are few limitations for the present method. When the spread of disease is slow the ARcn and Rcmi plot can’t depict the pattern of disease spread. Thus, the method is useful where local transmission or community spread of disease is prevalent. Pattern of disease spread is delineated by the method is based on reported data and doesn’t consider the underlaying unreported cases.
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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