A machine learning forecasting model for COVID-19 pandemic in India

Sujath, R.; Chatterjee, Jyotir Moy; Hassanien, Aboul Ella

doi:10.1007/s00477-020-01827-8

Cited by 298 publications

(219 citation statements)

References 18 publications

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“…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. Although prediction varies across the methods and did not seems very accurate [31]. Yadav used six regression analysis based machine learning models for prediction and found six degree polynomial model predict very close to observed data [32].…”

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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“…Sujath et al [9] proposed machine learning based techniques to predict the spread of COVID •Hybrid algorithms have been implemented using Matlab and performance in terms of RMSE, MAPE, MAE and R 2 has been evaluated.…”

Section: Related Workmentioning

confidence: 99%

A hybrid model based on mBA-ANFIS for COVID 19 confirmed cases prediction and forecast

Saif

Das

Biswas

2020

Preprint

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In India, the first confirmed case of novel corona virus (COVID-19) was discovered on 30 January, 2020. The number of confirmed cases is increasing day by day and it crossed 21,53,010 on 09 August, 2020. In this paper a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed modelis based on adaptive neuro-fuzzy inference system (ANFIS) and mutation based Bees Algorithm (mBA). ThemetaheuristicBees Algorithm (BA) has been modified applying 4 types of mutation and Mutation based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USAand the number of confirmed cases in next 10 days in Indiahas been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results showthat the proposed model has achieved better performance in terms of Root Mean squared error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE).It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data.Similarly, for USA the values are 4468.72, 3082.07, 6.1, 0.000952 for RMSE, MAE, MAPE, NRMSE respectively.

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“…The data is readily available and they are valid and from reliable sources. There is a need for a mechanism to manage the data efficiently and AI and in particular machine learning models can help in doing the same [1].…”

Section: Introductionmentioning

confidence: 99%

Realtime forecasting of COVID 19 cases in Karnataka state using Artificial neural network (ANN)

Shetty

2020

Preprint

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COVID-19 is a pandemic that has caused lot of deaths and infections in the last 6 months and is showing an increasing trend not only in the number of infections and deaths, but also in the recovery rate. Accurate prediction models are very much essential to make proper forecasts and take necessary actions. This study demonstrates the capability of Multilayer Perceptron (MLP, an ANN model for forecasting the number of infected cases in the state of Karnataka in India. It is trained using a fast training algorithm namely, Extreme Learning machine (ELM) to reduce the training time required. The parameters required for the forecasting model have been selected using partial autocorrelation function (PACF), which is a conventional method and its performance has been compared with parameters selected using cuckoo search (CS) algorithm, which is a very popular nature-inspired optimization algorithm. The testing of the forecasting model has been done and comparison between the two parameter selection methods has been carried out. Use of CS algorithm has resulted in a better forecasting performance based on mean absolute percentage error (MAPE), with a value of 6.62 % on training data and 7.03% on the test data. Further to check the efficacy of the model, the data of COVID-19 cases of Hungary from 4th March to 19th April 2020 has been used, which resulted in a MAPE of 1.55%, thereby establishing the robustness of the proposed ANN model for forecasting COVID-19 cases for the state of Karnataka.

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A machine learning forecasting model for COVID-19 pandemic in India

Cited by 298 publications

References 18 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.

A hybrid model based on mBA-ANFIS for COVID 19 confirmed cases prediction and forecast

Realtime forecasting of COVID 19 cases in Karnataka state using Artificial neural network (ANN)

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