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. 2020 Aug 1:728:138762.
doi: 10.1016/j.scitotenv.2020.138762. Epub 2020 Apr 20.

Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

Anuradha Tomar  1 , Neeraj Gupta  2
Affiliations

Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

Anuradha Tomar et al. Sci Total Environ. .

Abstract

The spread of COVID-19 in the whole world has put the humanity at risk. The resources of some of the largest economies are stressed out due to the large infectivity and transmissibility of this disease. Due to the growing magnitude of number of cases and its subsequent stress on the administration and health professionals, some prediction methods would be required to predict the number of cases in future. In this paper, we have used data-driven estimation methods like long short-term memory (LSTM) and curve fitting for prediction of the number of COVID-19 cases in India 30 days ahead and effect of preventive measures like social isolation and lockdown on the spread of COVID-19. The prediction of various parameters (number of positive cases, number of recovered cases, etc.) obtained by the proposed method is accurate within a certain range and will be a beneficial tool for administrators and health officials.

Keywords: COVID-19; Curve fitting; LSTM; Prediction; Recurrent neural network.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
Transmission of COVID-19.
Fig. 2
Fig. 2
No of cases of COVID-19 worldwide (Statista, 2020).
Fig. 3
Fig. 3
Fatality rate of major virus outbreaks (Statista, 2020).
Fig. 4
Fig. 4
Basic structure of LSTM.
Fig. 5
Fig. 5
(a) Total number of confirmed cases prediction using LSTM. (b) Daily total number of positive cases prediction using LSTM. (c) Total number of recovered cases prediction using LSTM. (d) Total number of deceased cases prediction using LSTM. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)
Fig. 6
Fig. 6
(a) Total number of confirmed cases prediction by curve fitting. (b) Daily number of positive cases prediction by curve fitting. (c) Total number of recovered cases prediction by curve fitting. (d) Total number of deceased cases prediction by curve fitting.
Fig. 7
Fig. 7
(a) Estimation of number of days required for recovery (b) Effect of the transmission rate r on number of cases (c) Effect of r on number of cases with 6th April as initial point.
See this image and copyright information in PMC

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