Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models

Kumar, Pavan; Singh, Ram Kumar; Nanda, Chintan; Kalita, Himangshu; Patairiya, Shashikanta; Sharma, Y. D.; Rani, Meenu; Bhagavathula, Akshaya Srikanth

doi:10.1101/2020.03.30.20047803

Cited by 28 publications

(31 citation statements)

References 12 publications

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“…However, there are several difficulties associated with creating ABMs such as integration with too many features, choice of model parameters, model results being either trivial or too complex [19]. The spread of COVID-19 in India has been investigated in many researches including [20][21] [22][23], but they laid little emphasis on post-model validation for peak COVID-19 timeline forecast. With this in mind, SIR model is explored in current research to forecast peak COVID-19 outbreak over a large population in India.…”

Section: Related Workmentioning

confidence: 99%

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Temporal Analysis of COVID-19 Peak Outbreak

Tewari

2020

Preprint

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Intent of this research is to explore how a specific class of mathematical models namely Susceptible-Infected-Removed model can be utilized to forecast peak outbreak timelines of COVID-19 epidemic amongst a population of interest starting from the date of first reported case. Till the time of this research, there was no effective and universally accepted vaccine to control transmission and spread of this infection. COVID-19 primarily spreads in population through respiratory droplets from an infected person cough and sneeze which infects people who are in proximity. COVID-19 is spreading contagiously across the world. If health policy makers and medical experts could get early and timely insights into when peak infection rate would occur after first reported case, they could plan and optimize medical personnel, ventilators supply, and other medical resources without over-taxing the infrastructure. The predictions may also help policymakers devise strategies to control the epidemic, potentially saving many lives. Thus, it can aid in critical decision-making process by providing actionable insights into COVID-19 outbreak by leveraging available data.

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Section: Related Workmentioning

confidence: 99%

“…The spread of COVID-19 in India has been investigated in many researches including [20][21][22][23], but they laid little emphasis on post-model validation for peak COVID-19 timeline forecast.…”

Section: Related Workmentioning

confidence: 99%

Temporal Analysis of COVID-19 Peak Outbreak

Tewari

2020

Preprint

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“…Kumar et. al [5], in order to analyse the Indian scenario, has used the ARIMA time series analysis technique. Their predictions were very similar to the later reported actual values.…”

Section: Related Workmentioning

confidence: 99%

Fine-tuned Forecasting Techniques for COVID-19 Prediction in India

Gola

Arya

Animesh

et al. 2020

Preprint

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Estimation of statistical quantities plays a cardinal role in handling of convoluted situations such as COVID-19 pandemic and forecasting the number of affected people and fatalities is a major component for such estimations. Past researches have shown that simplistic numerical models fare much better than the complex stochastic and regression-based models when predicting for countries such as India, United States and Brazil where there is no indication of a peak anytime soon. In this research work, we present two models which give most accurate results when compared with other forecasting techniques. We performed both short-term and long-term forecasting based on these models and present the results for two discrete durations.

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“…Even during the latency period of the infection, it can also be contagious. The contagion can be controlled by taking mitigation measures at the right time [3] . Coronavirus has been declared a global pandemic and emergency by the World Health Organization (WHO) on 11 th March 2020 [4,5] .…”

Section: Introductionmentioning

confidence: 99%

An Eye on the Future of COVID-19: Prediction of Likely Positive Cases and Fatality in India over a 30-Day Horizon Using the Prophet Model

Tulshyan

Sharma

Mittal³

2020

Disaster med. public health prep.

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Background: The coronavirus disease pandemic was initiated in Wuhan province of mainland China in December 2019 and has spread over the world. Objective: This study analyses the effects of COVID 19 based on Likely Positive Cases and fatality in India during and after the lockdown period from 24 March 2020 to 24 May 2020. Methods: Python has been used as the main programming language for data analysis and forecasting using the Prophet Model, a time series analysis model. The dataset has been preprocessed by grouping together the days for total numbers of cases and deaths on few selected dates and removed missing values present in some states. Results: The Prophet model performs better in terms of precision on the real data. Prediction depicts that during the lockdown, the total cases were rising but in a controlled manner with an accuracy of 87%. After the relaxation of lockdown rules, the predictions have shown an obstreperous situation with an accuracy of 60%. Conclusion: The resilience could have been better if the lockdown with strict norms was continued without much relaxation. The situation after lockdown has been found to be uncertain as observed by the experimental study conducted in this work.

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Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models

Cited by 28 publications

References 12 publications

Temporal Analysis of COVID-19 Peak Outbreak

Temporal Analysis of COVID-19 Peak Outbreak

Fine-tuned Forecasting Techniques for COVID-19 Prediction in India

An Eye on the Future of COVID-19: Prediction of Likely Positive Cases and Fatality in India over a 30-Day Horizon Using the Prophet Model

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