Back

Statistical analysis of national & municipal corporation level database of COVID-19 cases In India

Bajaj, N. S.; Pardeshi, S. S.; Patange, A. D.; Kotecha, D.; Mate, K. K.

2020-09-01 health informatics
10.1101/2020.07.18.20156794 medRxiv
Show abstract

Since its origin in December 2019, Novel Coronavirus or COVID-19 has caused massive panic in the word by infecting millions of people with a varying fatality rate. The main objective of Governments worldwide is to control the extent of the outbreak until a vaccine or cure has been devised. Machine learning has been an efficient mechanism to train, map, analyze, and predict datasets. This paper aims to utilize regression, a supervised machine learning algorithm to assess time-series datasets of COVID-19 pandemic by performing comparative analysis on datasets of India and two Municipal Corporations of Maharashtra, namely, Mira-Bhayander and Akola. Current study is an attempt towards drawing attention to the dynamics and nature of the pandemic in a controlled locality such as Municipal Corporation; which differs from the exponential nature observed nationally. However, for limited area like the one considered the nature of curve is observed to be cubic for total cases and multi-peak Gaussian for active cases. In conclusion, Government should empower district/ corporations/local authorities to adopt their own methodology and decision-making policy to contain the pandemic at regional-level like the case study discussed herein.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.