Forecast of the COVID-19 trend in India: A simple modelling approach

Haitao Song; Guihong Fan; Shi Zhao; Huaichen Li; Qihua Huang; Daihai He; Haitao Song; Guihong Fan; Shi Zhao; Huaichen Li; Qihua Huang; Daihai He

doi:10.3934/mbe.2021479

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 9775-9786. doi: 10.3934/mbe.2021479

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Forecast of the COVID-19 trend in India: A simple modelling approach

1.
Complex Systems Research Center, Shanxi University, Taiyuan 030006, China
2.
Department of Mathematics, Columbus State University, Columbus 31907, USA
3.
JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
4.
Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
5.
School of Mathematical and Statistical Sciences, Southwest University, Chongqing 400715, China
6.
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China

Received: 18 August 2021 Accepted: 25 October 2021 Published: 05 November 2021

By February 2021, the overall impact of the COVID-19 pandemic in India had been relatively mild in terms of total reported cases and deaths. Surprisingly, the second wave in early April becomes devastating and attracts worldwide attention. Multiple factors (e.g., Delta variants with increased transmissibility) could have driven the rapid growth of the epidemic in India and led to a large number of deaths within a short period. We aim to reconstruct the transmission rate, estimate the infection fatality rate and forecast the epidemic size. We download the reported COVID-19 mortality data in India and formulate a simple mathematical model with a flexible transmission rate. We use iterated filtering to fit our model to deaths data. We forecast the infection attack rate in a month ahead. Our model simulation matched the reported deaths well and is reasonably close to the results of the serological study. We forecast that the infection attack rate (IAR) could have reached 43% by July 24, 2021, under the current trend. Our estimated infection fatality rate is about 0.07%. Under the current trend, the IAR will likely reach a level of 43% by July 24, 2021. Our estimated infection fatality rate appears unusually low, which could be due to a low case to infection ratio reported in previous study. Our approach is readily applicable in other countries and with other types of data (e.g., excess deaths).
- COVID-19,
- India,
- mathematical modelling,
- iterated filtering,
- forecast
Citation: Haitao Song, Guihong Fan, Shi Zhao, Huaichen Li, Qihua Huang, Daihai He. Forecast of the COVID-19 trend in India: A simple modelling approach[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9775-9786. doi: 10.3934/mbe.2021479

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Abstract

By February 2021, the overall impact of the COVID-19 pandemic in India had been relatively mild in terms of total reported cases and deaths. Surprisingly, the second wave in early April becomes devastating and attracts worldwide attention. Multiple factors (e.g., Delta variants with increased transmissibility) could have driven the rapid growth of the epidemic in India and led to a large number of deaths within a short period. We aim to reconstruct the transmission rate, estimate the infection fatality rate and forecast the epidemic size. We download the reported COVID-19 mortality data in India and formulate a simple mathematical model with a flexible transmission rate. We use iterated filtering to fit our model to deaths data. We forecast the infection attack rate in a month ahead. Our model simulation matched the reported deaths well and is reasonably close to the results of the serological study. We forecast that the infection attack rate (IAR) could have reached 43% by July 24, 2021, under the current trend. Our estimated infection fatality rate is about 0.07%. Under the current trend, the IAR will likely reach a level of 43% by July 24, 2021. Our estimated infection fatality rate appears unusually low, which could be due to a low case to infection ratio reported in previous study. Our approach is readily applicable in other countries and with other types of data (e.g., excess deaths).

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