Asset Details
Do you wish to reserve the book?
Counting the uncounted: estimating the unaccounted COVID-19 infections in India
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Counting the uncounted: estimating the unaccounted COVID-19 infections in India
Do you wish to request the book?
Counting the uncounted: estimating the unaccounted COVID-19 infections in India
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Counting the uncounted: estimating the unaccounted COVID-19 infections in India
2024
Overview
Undetected infectious populations have played a major role in the COVID-19 outbreak across the globe and estimation of this undetected class is a major concern in understanding the actual size of the COVID-19 infections. Due to the asymptomatic nature of some infections, many cases have gone undetected. Also, despite carrying COVID-19 symptoms, most of the infected population kept the infections hidden and stayed unreported, especially in a country like India. Based on these factors, we have added an undetected compartment to the already developed SEIR model (Saikia et al. in Nonlinear Dyn 104:4727–4751, 2021) to estimate these uncounted infections. In this article, we have applied Physics Informed Neural Network (PINN) to estimate the undetected infectious populations in the 20 worst-affected Indian states as well as India as a whole. The analysis has been carried out for the first as well as second surge of COVID-19 infections in India. A ratio of the active undetected infectious to the active detected infectious population is calculated through the PINN analysis which gives a picture of the real size of the pandemic in India. The rate at which symptomatic infectious population goes undetected and are never reported is also estimated using the PINN method. Toward the end, an artificial neural network based forecasting scenario of the pandemic in India is presented. The prediction is found to be reliable as the training of the neural network has been carried out using the unique features, obtained from the state-wide analysis of the newly proposed model as well as from the PINN analysis.
Publisher
Springer Netherlands,Springer Nature B.V
