A modified variable‐order fractional SIR model to predict the spread of COVID‐19 in India. (8th July 2021)

• Record Type: Journal Article
• Authors: Singh, Abhishek Kumar; Mehra, Mani; Gulyani, Samarth
• Other Names: Singh Jagdev guestEditor.; Kumar Devendra guestEditor.; Baleanu Dumitru guestEditor.
• Is Part Of: Mathematical methods in the applied sciences. Volume 46:Number 7(2023)
• Page Start: 8208
• Page End: 8222
• Publication Date: 2021-07-08
• Subjects: Adam–Bashforth–Moulton method -- COVID‐19 -- differential evolution method -- SIR epidemic model -- variable‐order fractional differential equations; Mathematics -- Periodicals; Technology -- Mathematics -- Periodicals; 519
• DOI: 10.1002/mma.7655
• Languages: English
• ISSNs: 0170-4214

Abstract

Abstract : The first case of COVID‐19 in India detected on January 30, 2020, after its emergence in Wuhan, China, in December 2019. The lockdown was imposed as anemergency measure by the Indian government to prevent the spread of COVID‐19 but gradually eased out due to its vast economic consequences. Just 15 days after the relaxation of lockdown restrictions, Delhi became India's worst city in terms of COVID‐19 cases. In this paper, we propose a variable‐order fractional SIR (susceptible, infected, removed) model at state‐level scale. We introduce a algorithm that uses the differential evolution algorithm in combination with Adam–Bashforth–Moulton method to learn the parameters in a system of variable‐order fractional SIR model. The model can predict the confirm COVID‐19 cases in India considering the effects of nationwide lockdown and the possible estimate of the number of infliction inactive cases after the removal of lockdown on June 1, 2020. A new parameter p is introduced in the classical SIR model representing the fraction of infected people that get tested and are thereby quarantined. The COVID‐19 trajectory in Delhi, as per our model, predicts the slowing down of the spread between January and February 2021, touching a peak of around 5 lakh confirmed cases.