Counting the uncounted : estimating the unaccounted COVID-19 infections in India
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910414323843072 |
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| author | Saikia, Debashis Bora, Kalpana Bora, Madhurjya P. |
| author_facet | Saikia, Debashis Bora, Kalpana Bora, Madhurjya P. |
| contents | 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 [48] 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 (ANN) 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_00900 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Counting the uncounted : estimating the unaccounted COVID-19 infections in India Saikia, Debashis Bora, Kalpana Bora, Madhurjya P. Populations and Evolution Physics and Society 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 [48] 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 (ANN) 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. |
| title | Counting the uncounted : estimating the unaccounted COVID-19 infections in India |
| topic | Populations and Evolution Physics and Society |
| url | https://arxiv.org/abs/2403.00900 |