Forecasting of COVID 19 Cases in Karnataka State using Artificial Neural Network (ANN)

Abstract

COVID-19 is a pandemic that has caused lot of deaths and infections in the last 2 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 Artificial Neural network (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 to reduce the training time required. The parameters required for the forecasting model have been selected using partial autocorrelation function, which is a conventional method, and its performance has been compared with parameters selected using cuckoo search (CS) algorithm, which is a very popular metaheuristic optimization algorithm. The testing of the forecasting model has been done, and comparison between the two parameter selection methods as well as with MLP with conventional backpropagation 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 4 March to 19 April 2020 have 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.

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters

“…In general, the multivariate LSTM model significantly outperformed the other models, displaying an average improvement of 61 – 71 % in the MAPE compared to the univariate models ( Table 4 ). The improvement is similar to a study done by Shetty and Pai, (2021) who observed a 66% improvement in the MAPE (from 20.73% to 7.03%) after implementing a cookoo search algorithm for better forecasting of COVID-19 cases in the state of Karnataka, India.…”

Deep learning via LSTM models for COVID-19 infection forecasting in India

“…They demonstrated that LSTM model performed better in the medium and long range forecasting scale when integrated with the weather data. Shetty [63] presented real-time forecasting using a simple neural network for the COVID-19 cases in the state of Karnataka in India where parameter selection for the model was based on cuckoo search algorithm. The study reported that the mean-absolute percentage error (MAPE) was reduced from 20.73% to 7.03% and the proposed model was further tested on the Hungary COVID-19 dataset and reported promising results.…”

Weather Conditions and COVID-19 Cases: Insights from the GCC Countries

“…Among the machine-learning techniques, ANN-based methods have exhibited superiority over other methods in forecasting COVID-19 cases ( Shetty and Pai, 2021 ). ANNs were developed based on the mechanisms of biological nerve systems ( Maind et al., 2014 ).…”

“…Feed-forward neural networks and the MLP are forms of ANNs that have been used to forecast the spread rate of COVID-19 in India ( Chakraborty, Choudhary, Sarma, Hazarika, 2020 , Shetty, Pai, 2021 ) and across the continental USA ( Mollalo et al., 2020 ). Rizk-Allah and Hassanien (2020) presented a hybrid forecasting model that combines a multi-layer feed-forward neural network with an interior search algorithm to forecast the spread of COVID-19 in the USA, Italy, and Spain.…”