Analysis of Covid-19 Cases in India Using Seir, Arima and LSTM Models
Bibliographic record
Abstract
After one year from the start of the COVID-19 pandemic in India, the country is now having a steady decay in the number of daily new cases and active cases. Although the vaccination process is about to start from mid of January 2021, it would not affect the number of daily cases at least for the next three to four months for obvious reasons like phase-wise implementation and six to eight weeks time span required from the first dosage to develop the immunity. Therefore, the prime question is now, where would we reach at the end of the first quarter of 2021, and what could be the number of new cases and active cases before the vaccination immunity starts working. This paper analyzes the growth and decay pattern of Indian COVID-19 cases with help of SEIR epidemical modeling, ARIMA statistical modeling, and time series analysis by LSTM. The models learn the parameter and hyper-parameter values that are best suited for describing the pattern for the COVID-19 pandemic in India. Then it tries to predict the numbers for India by the end of March, 2021. It is forecasted that the number of new cases would come down near 5000 per day, active cases near 40,000 and the total number of infected may reach 11.1 million if the current pattern is followed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".