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Covid-19: Predicting affected patients using AI and vulnerability to environmental and demographic factors for India

EasyChair Preprint 7264

24 pagesDate: December 27, 2021

Abstract

This study uses climate and demographic variables such as location, humidity, minimum temperature, maximum temperature, population density were considered to predict Covid-19 cases across different states in India. In the present study a feed forward back-propagated neural model was used with Levemberg-Marquardt algorithm. The model showed good results with correlation coefficient (R) as 0.87 during training and 0.78 during testing between COVID-19 cases detected in actual and predicted by model (ANN). The sensitivity analysis of model revealed that humidity of the region was affecting the Covid-19 positive cases by 19.41% while day of year i.e. the day on which case was detected was contributing 18.39% while the demographic factor i.e. population density contributed 14.0%.

Keyphrases: ANN, COVID-19, Environment and demography, India, sensitivity analysis

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:7264,
  author    = {Sunayana Chandra and Suresh Gurjar and Akash Priyadarshee and Vikas Kumar},
  title     = {Covid-19: Predicting affected patients using AI and vulnerability to environmental and demographic factors for India},
  howpublished = {EasyChair Preprint 7264},
  year      = {EasyChair, 2021}}
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