Analysis of the Second COVID-19 Wave in India and the United Kingdom Using a Birth-Death Model
Viswanath, N. C.
Show abstract
Several countries have witnessed multiple waves of the COVID-19 pandemic between 2020 and 21. The method in [8] is applied here to analyze the COVID-19 waves in India and the UK. For this, a birth-death model is fitted to the active and total cases data for 30 days periods called windows starting from 16th March 2020 up to 10th May 2021. Peculiarities of the parameters suggested a classification of the above windows into three categories: (i) whose fitted parameters predicted a rise in the number of active cases before a fall to zero, (ii) which predicted a decrease, without rising, in the active cases to zero and (iii) which predicted an increase in the active cases until the entire susceptible population gets infected. It follows that some of the type (iii) windows are of the same or lesser concern when compared to some type (i) windows. Further analysis of the type (iii) windows leads to the identification of those which could be indicators of the start of a new wave of the pandemic. The study thus proposes a method for using the present data for identifying pandemic waves in the near future.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Prediction of confirmed and death cases of Covid-19 in Chile through time series techniques: A comparative study 97%
- Analytical Solution of a New SEIR Model Based on Latent Period-Infectious Period Chronological Order 96%
- Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling 96%
Similar papers in this journal
Similar papers in this journal
- A new, simple method of describing COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting 97%
- Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study 94%
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 94%
Similar papers in this journal
"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.