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ORIGINAL ARTICLE
Year : 2020  |  Volume : 6  |  Issue : 2  |  Page : 83-90

Modeling and forecasting of confirmed and recovered cases of COVID-19 in India


1 Department of Community and Family Medicine, AIIMS Bhopal, Madhya Pradesh, India
2 Theoretical Neuroscience Group, Institute of Neuroscience Systems, Aix-Marseille University, Marseille, France
3 Member of Indian Forest Service, Ministry of Environment, Forest and Climate Change, New Delhi, India

Correspondence Address:
Dr. Anuradha Gautam
Department of Community and Family Medicine, AIIMS, Bhopal, Madhya Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJAM.IJAM_45_20

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Context: The novel coronavirus was reported in the past few weeks of 2019 in the Wuhan city, China, and the spread and outbreaks of disease require an epidemiological analysis of the disease in the shortest time and increased awareness of effective interventions Aims: This article proposes an objective approach to predicting the continuation of the COVID-19 cases in India using a simple, but powerful time-series method. Settings and Design: Cumulative confirmed and cumulative recovered cases of COVID-19 in India are taken to forecast the prevalence of incoming 3 weeks. Subjects and Methods: The model is built to predict the number of confirmed cases and recovered cases based on the data available from March 14, 2020, to April 26, 2020. Statistical Analysis Used: The autoregressive-integrated moving average model was applied to predict the number of confirmed cases and recovered cases of COVID-19 during the next 3 weeks. Results: Our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty assuming that the data used are reliable and that the future will continue to follow the past pattern of the disease. Conclusions: The timeline of a live-forecasting exercise with potential implications for planning and decision making is described. The following core competencies are addressed in this article: Medical knowledge, Practice-based learning and improvement, Systems-based practice.


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