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Forecasting the Peak of COVID-19 Daily Cases in India Using Time Series Analysis and Multivariate LSTM

EasyChair Preprint no. 4061

13 pagesDate: August 21, 2020

Abstract

Since the start of COVID-19 pandemic, the main question that every country is desperately looking for an answer is when the number of daily new infection cases would decline. At the end of July 2020 (time of writing this paper), many countries have already managed to decrease the number of daily infections as well as total infections and death cases. India imposed four consecutive lockdowns which spanned over 61 days and currently it is being withdrawn phase by phase. However, the number of new cases and number of fatality is still on the rise.  This paper investigates what could be the possible time required for India before the numbers of  daily infected people could start declining and what could the peak value hit by then. Three different models are used independently for initial predictions using statistical ARIMA model, SAR epidemical model, and ML regression model. These three results are fit into a stacked LSTM model which makes the final prediction. It is forecasted that the number of daily new cases would keep on increasing till first week of Nov 2020 and can reach up to 90 thousands before it finally starts to decline.

Keyphrases: ARIMA, COVID-19 prediction, epidemic model, LSTM, polynomial regression

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:4061,
  author = {Souvik Sengupta},
  title = {Forecasting the Peak of COVID-19 Daily Cases in India Using Time Series Analysis and Multivariate LSTM},
  howpublished = {EasyChair Preprint no. 4061},

  year = {EasyChair, 2020}}
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