Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming

Salgotra, Rohit; Gandomi, Mostafa; Gandomi, Amir H.

doi:10.1016/j.chaos.2020.109945

Cited by 172 publications

(138 citation statements)

References 26 publications

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“…On 13 May 2020, the cumulative cases and death cases were 80,000 and 2500 respectively. The prediction for next ten days was done with 142,000 and 4,200 cumulative cases and deaths respectively on 23 May 2020 [30]. Sujath used linear regression, multilayer perceptron and vector autoregressive method and 80 time points till 10 Apr 2020, to predict con rmed cases, deaths and recovered cases from 11 Apr 2020 to 18 Jun 2020.…”

Section: Discussionmentioning

confidence: 99%

Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.

Bhandari

Tak

Gupta

et al. 2020

Preprint

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Abstract The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centerpiece in evidence based disease management. Numerous approaches that use mathematical modeling have been used to predict the outcome of the pandemic, including data driven models, empirical and hybrid models. This study was aimed at prediction COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Retrieving real time data from the Johns Hopkins dashboard from 11 Mar 2020 to 25 Jun 2020 (N = 107 time points) to fit the model. The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths respectively with minimum Akaike Informaton Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 Jun 2020 to 05 Jul 2020 showed a trend toward continuous increment. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) of ARIMA(1,3,2) model was 21137 and 166330 respectively. Similarly, PredRMSE and BaseRMSE of ARIMA(3,3,1) model was 668.7 and 5431 respectively. We propose that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimization and evidence based decision making for a subsequent state of affairs.

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Section: Discussionmentioning

confidence: 99%

Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.

Bhandari

Tak

Gupta

et al. 2020

Preprint

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“…It was expected that the number of daily cases will be declined due to the following of Canadians to the rules and regulations issued by the government. Genetic evolutionary programming has been applied to forecast the spread of COVID-19 outbreak in India ( Salgotra et al, 2020 ). The proposed model is highly reliable in forecasting both total cases and death cases.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Elsheikh

Saba

Elaziz

et al. 2021

Process Safety and Environmental Protection

107

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“…For public health officials, such an estimate plays an important role in allocating limited health resources rationally and in directing when and which health interventions should be adopted to alleviate the disease outbreak [4,6,11,12]. Recently, a great number of mathematical and statistical techniques have been deemed as policy-supportive tools to model the prevalence, morbidity and mortality of COVID-19 in different countries [10,13,14]. For example, Saba et al used an autoregressive integrated moving average (ARIMA) model and a nonlinear autoregressive artificial neural networks (NARNN) to forecast the prevalence of the COVID-19 outbreak in Egypt [11].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

Wang

Yao

et al. 2020

Epidemiol. Infect.

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Forecasting the epidemics of the diseases is very valuable in planning and supplying resources effectively. This study aims to estimate the epidemiological trends of the coronavirus disease 2019 (COVID-19) prevalence and mortality using the advanced α-Sutte Indicator, and its prediction accuracy level was compared with the most frequently adopted autoregressive integrated moving average (ARIMA) method. Time-series analysis was performed based on the total confirmed cases and deaths of COVID-19 in the world, Brazil, Peru, Canada and Chile between 27 February 2020 and 30 June 2020. By comparing the prediction reliability indices, including the root mean square error, mean absolute error, mean error rate, mean absolute percentage error and root mean square percentage error, the α-Sutte Indicator was found to produce lower forecasting error rates than the ARIMA model in all data apart from the prevalence testing set globally. The α-Sutte Indicator can be recommended as a useful tool to nowcast and forecast the COVID-19 prevalence and mortality of these regions except for the prevalence around the globe in the near future, which will help policymakers to plan and prepare health resources effectively. Also, the findings of our study may have managerial implications for the outbreak in other countries.

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Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming

Cited by 172 publications

References 26 publications

Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.

Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

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