Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Jun 29, 2020)
Date Submitted: May 5, 2020
Open Peer Review Period: May 5, 2020 - May 19, 2020
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Severity Model Based Prediction of Early Trend and Pattern Recognition of the COVID-19 Infection in India: Exploratory Data Analysis and Machine Learning Study
ABSTRACT
Objective:
Recent Coronavirus Disease 2019 (COVID-19) pandemic has inflicted the whole world critically. Despite the fact that India has not been listed amongst the top ten highly affected countries, one cannot rule out COVID-19 associated complications in the near future. The accumulative testing facilities has resulted in exponential increase in COVID-19 infection cases. In figures, the number of positive cases have risen up to 33,614 as of 30 April, 2020. Keeping into consideration the serious consequences of pandemic, we aim to establish correlations between the numerous features which was acquired from the various Indian-based COVID datasets, and the impact of the containment of the pandemic on the current state of Indian population using machine learning approach. We aim to build the COVID-19 severity model employing logistic function which determines the inflection point and help in prediction of the future number of confirmed cases.
Methods:
An empirical study was performed on the COVID-19 patient status in India. We performed the study commencing from 30 January, 2020 to 30 April, 2020 for the analysis. We applied the machine learning (ML) approach to gain the insights about COVID-19 incidences in India. Several diverse exploratory data analysis ML tools and techniques were applied to establish a correlation amongst the various features. Also, the acute stage of the disease was mapped in order to build a robust model.
Results:
We collected five different datasets to execute the study. The data sets were integrated extract the essential details. We found that men were more prone to get infected of the coronavirus disease as compared to women. Also, the age group was the middle-young age of patients. On 92-days based analysis, we found a trending pattern of number of confirmed, recovered, deceased and active cases of COVID-19 in India. The as-developed growth model provided an inflection point of 85.0 days. It also predicted the number of confirmed cases as 48,958.0 in the future i.e. after 30th April. Growth rate of 13.06 percent was obtained. We achieved statistically significant correlations amongst growth rate and predicted COVID-19 confirmed cases. Conclusion: This study demonstrated the effective application of exploratory data analysis and machine learning in building a mathematical severity model for COVID-19 in India.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.