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A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India

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  • Sahoo, Bijay Kumar
  • Sapra, Balvinder Kaur

Abstract

We propose a data driven epidemic model using the real data on the infection, recovery and death cases for the analysis of COVID-19 progression in India. The model assumes continuation of existing control measures such as lockdown and quarantines, the suspected and confirmed cases and does not consider the scenario of 2nd surge of the epidemic due to any reason. The model is arrived after least square fitting of epidemic behaviour model based on theoretical formulation to the real data of cumulative infection cases reported between 24 March 2020 and 30May 2020. The predictive capability of the model has been validated with real data of infection cases reported during June 1–10, 2020. A detailed analysis of model predictions in terms of future trend of COVID-19 progress individually in 18 states of India and India as a whole has been attempted. Infection rate in India, as a whole, is continuously decreasing with time and has reached 3 times lower than the initial infection rate after 6 weeks of lock down suggesting the effectiveness of the lockdown in containing the epidemic. Results suggest that India, as a whole, could see the peak and end of the epidemic in the month of July 2020 and March 2021 respectively as per the current trend in the data. Active infected cases in India may touch 2 lakhs or little above at the peak time and total infected cases may reach over 19 lakhs as per current trend. State-wise results have been discussed in the manuscript. However, the prediction may deviate particularly for longer dates, as assumptions of model cannot be met always in a real scenario. In view of this, a real time application (COV-IND Predictor) has been developed which automatically syncs the latest data from the national COVID19 dash board on daily basis and updates the model input parameters and predictions instantaneously. This real time application can be accessed from the link: https://docs.google.com/spreadsheets/d/1fCwgnQ-dz4J0YWVDHUcbEW1423wOJjdEXm8TqJDWNAk/edit?usp=sharing and can serve as a practical tool for policy makers to track peak time and maximum active infected cases based on latest trend in data for medical readiness and taking epidemic management decisions.

Suggested Citation

  • Sahoo, Bijay Kumar & Sapra, Balvinder Kaur, 2020. "A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s096007792030432x
    DOI: 10.1016/j.chaos.2020.110034
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    References listed on IDEAS

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    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Seth Flaxman & Swapnil Mishra & Axel Gandy & H. Juliette T. Unwin & Thomas A. Mellan & Helen Coupland & Charles Whittaker & Harrison Zhu & Tresnia Berah & Jeffrey W. Eaton & Mélodie Monod & Azra C. Gh, 2020. "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe," Nature, Nature, vol. 584(7820), pages 257-261, August.
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    1. Zhang, Zhenzhen & Zhang, Juan & Li, Li & Guo, Zunguang & Zhang, Zi-Ke & Sun, Gui-Quan, 2024. "Quantifying the effectiveness of brucellosis control strategies in northern China using a mechanistic and data-driven model," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
    2. Md. Mokhlesur Rahman & Jean-Claude Thill, 2022. "Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries," IJERPH, MDPI, vol. 19(12), pages 1-31, June.
    3. Zubcoff, Jose-Jacobo & Olcina, Jorge & Morales, Javier & Mazón, Jose-Norberto & Mayoral, Asunción M., 2023. "Usefulness of open data to determine the incidence of COVID-19 and its relationship with atmospheric variables in Spain during the 2020 lockdown," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).

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