Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

COVID-19 India Prediction Model Based on the Trend from Other Countries

Version 1 : Received: 16 June 2020 / Approved: 17 June 2020 / Online: 17 June 2020 (09:40:23 CEST)

How to cite: Prakash, S.; Deepak, P.S. COVID-19 India Prediction Model Based on the Trend from Other Countries. Preprints 2020, 2020060214 (doi: 10.20944/preprints202006.0214.v1). Prakash, S.; Deepak, P.S. COVID-19 India Prediction Model Based on the Trend from Other Countries. Preprints 2020, 2020060214 (doi: 10.20944/preprints202006.0214.v1).

Abstract

This paper is an attempt to present a COVID-19 prediction model for India. Lockdown plays an important role in the arrest of community spread of the disease. This was evident from the study of other countries such as Russia, Belgium and Germany, where peak cases were recorded within a month of the imposition of lockdown, that it showed an immediate positive effect. However, in India, even after 65 days of lockdown, there is no decrease in the number of daily new cases reported. There were many models prepared for India and almost all of them were proven wrong by the increase in the number of cases. The model in this paper is prepared using the COVID-19 trend in other countries, population density and the pandemic bell curve. Based on the available data until 24th May 2020, two scenarios have been presented. In one, the peak shall be obtained when the number of daily new cases per million reaches 190 and in the second when the daily new cases per million reach 724. One model predicts the number of cases to reach 1 million by mid-July 2020. The other model predicts the number of cases to peak by mid-July with the total cases reaching 20 million. The predicted cases were compared with the actual cases recorded for the period 25th May to 11th June 2020. It was observed that the actual values matched quite reasonably with the predicted values.

Subject Areas

COVID-19; Prediction model; Pandemic bell curve; India; Different scenarios

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