Forecast of the COVID-19 trend in India: A simple modelling approach
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 transm...
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oai:doaj.org-article:383c6985ad654bfa8e927d5fa35ec7ca2021-11-29T06:41:25ZForecast of the COVID-19 trend in India: A simple modelling approach10.3934/mbe.20214791551-0018https://doaj.org/article/383c6985ad654bfa8e927d5fa35ec7ca2021-11-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021479?viewType=HTMLhttps://doaj.org/toc/1551-0018By 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).Haitao SongGuihong FanShi ZhaoHuaichen Li Qihua HuangDaihai He AIMS Pressarticlecovid-19indiamathematical modellingiterated filteringforecastBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9775-9786 (2021) |
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covid-19 india mathematical modelling iterated filtering forecast Biotechnology TP248.13-248.65 Mathematics QA1-939 |
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covid-19 india mathematical modelling iterated filtering forecast Biotechnology TP248.13-248.65 Mathematics QA1-939 Haitao Song Guihong Fan Shi Zhao Huaichen Li Qihua Huang Daihai He Forecast of the COVID-19 trend in India: A simple modelling approach |
| description |
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). |
| format |
article |
| author |
Haitao Song Guihong Fan Shi Zhao Huaichen Li Qihua Huang Daihai He |
| author_facet |
Haitao Song Guihong Fan Shi Zhao Huaichen Li Qihua Huang Daihai He |
| author_sort |
Haitao Song |
| title |
Forecast of the COVID-19 trend in India: A simple modelling approach |
| title_short |
Forecast of the COVID-19 trend in India: A simple modelling approach |
| title_full |
Forecast of the COVID-19 trend in India: A simple modelling approach |
| title_fullStr |
Forecast of the COVID-19 trend in India: A simple modelling approach |
| title_full_unstemmed |
Forecast of the COVID-19 trend in India: A simple modelling approach |
| title_sort |
forecast of the covid-19 trend in india: a simple modelling approach |
| publisher |
AIMS Press |
| publishDate |
2021 |
| url |
https://doaj.org/article/383c6985ad654bfa8e927d5fa35ec7ca |
| work_keys_str_mv |
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