Forecasting Epidemic Spread of Covid-19 in India Using Arima Model and Effectiveness of Lockdown

Maheshwari, Himani; Yadav, Dharminder; Chandra, Umesh; Rai, D. S.

doi:10.37418/amsj.9.6.22

Cited by 10 publications

(4 citation statements)

References 19 publications

(11 reference statements)

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“…The isolation strategy, a probable option for lockdown, has been developed to assist health planning based on the predictive analysis of the number of tests [25]. Also, data driven models like Auto Regressive Integrated Moving Average (ARIMA) model for predicting the number of patients [26] and maxent model for estimating the potential risk zonal areas [27] has helped the government for better strategic planning.…”

Section: Statistical Analysis Of Covid-19 Trendsmentioning

confidence: 99%

Role of data science in managing COVID-19 pandemic

Nikita

Gupta

Raman

et al. 2020

Indian Chemical Engineer

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In December of 2019, the first outbreak of COVID-19 was detected in mainland China, eventually spreading to every continent in the world except Antarctica. Named Coronavirus Disease 19 by the World Health Organization (WHO), this highly contagious disease was caused by the virus SARS-CoV-2. With a transmission rate greater than SARS or common flu, continued tremendous efforts will be needed to successfully combat this disease. In this article, we review the role that data science is playing in this war. Data science, combined with statistical analysis, computer science and computational biology, is helping in myriad ways with applications including epidemiology, drug discovery, and molecular design for diagnostic and therapeutic purposes. A number of data driven models, mathematical models, correlations and predictive models have been developed for COVID-19. Challenges faced by the data scientists today have been highlighted. Finally, open source datasets sources are mentioned that can be potentially used in diagnostics and evaluation of health policies.

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Section: Statistical Analysis Of Covid-19 Trendsmentioning

confidence: 99%

Role of data science in managing COVID-19 pandemic

Nikita

Gupta

Raman

et al. 2020

Indian Chemical Engineer

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“… [27] Case SA, Nigeria, Ghana, Egypt Perone [28] Case Italy, Russia, USA Maheshwari et al. [29] Case and death India Katoch et al. [30] Case India Kırbaş et al.…”

Section: Introductionmentioning

confidence: 99%

Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World

Cihan

2021

Applied Soft Computing

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Coronavirus disease 2019 (COVID-2019) has spread rapidly all over the world and it is known that the most effective way to eliminate the disease is vaccination. Although the traditional vaccine development process is quite long, more than ten COVID-19 vaccines have been approved for use in about a year. The COVID-19 vaccines that have been administered are highly effective enough, but achieving herd immunity is required to end the pandemic. The motivation of this study is to contribute to review the countries’ vaccine policies and adjusting the manufacturing plans of the vaccine companies. In this study, the total number of people fully vaccinated against COVID-19 was forecasted in the US, Asia, Europe, Africa, South America, and the World with the Autoregressive Integrated Moving Average (ARIMA) model, which is a new approach in vaccination studies. Additionally, for herd immunity, the percentage of fully vaccinated people in these regions at the beginning of 2021 summer was determined. ARIMA results show that in the US, Asia, Europe, Africa, South America, and the World will reach 139 million, 109 million, 127 million, 8 million, 38 million, and 441 million people will be fully vaccinated on 1 June 2021, respectively. According to these results, 41.8% of the US, 2.3% of Asia, 17% of Europe, 0.6% of Africa, 8.8% of South America, and 5.6% of the World population will be fully vaccinated people against the COVID-19. Results show that countries are far from the herd immunity threshold level desired to reach for safely slow or stop the COVID-19 epidemic.

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“…Lee et al ( 2020 ) proposed a COVID‐19 epidemic model for estimating the unidentified infected population in China. Maheshwari et al ( 2020 ) forecasted the epidemic spread of COVID‐19 in India using the ARIMA model. Zakharov et al ( 2020 ) predicted the dynamics of the COVID‐19 epidemic in real‐time using the case‐based rate reasoning model.…”

Section: Introductionmentioning

confidence: 99%

Dynamical analysis of novel COVID‐19 epidemic model with non‐monotonic incidence function

Kumar

Basu

Ghosh

et al. 2021

Journal of Public Affairs

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In this study, we developed and analyzed a mathematical model for explaining the transmission dynamics of COVID‐19 in India. The proposed model is a modified version of the existing model. Our model divides the infected class of model into two classes: (unknown infected class) and (known infected class). In addition, we consider a recovered and reserved class, where susceptible people can hide them due to fear of the COVID‐19 infection. Furthermore, a non‐monotonic incidence function is deemed to incorporate the psychological effect of the novel coronavirus diseases on India's community. The epidemiological threshold parameter, namely the basic reproduction number, has been formulated and presented graphically. With this threshold parameter, the local and global stability analysis of the disease‐free equilibrium and the endemic proportion equilibrium based on disease persistence have been analyzed. Lastly, numerical results of long‐run prediction using MATLAB show that the fate of this situation is very harmful if people are not following the guidelines issued by the authority.

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Forecasting Epidemic Spread of Covid-19 in India Using Arima Model and Effectiveness of Lockdown

Cited by 10 publications

References 19 publications

Role of data science in managing COVID-19 pandemic

Role of data science in managing COVID-19 pandemic

Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World

Dynamical analysis of novel COVID‐19 epidemic model with non‐monotonic incidence function

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